7 Facts You Probably Didn’t Know About Virtual Machines

MMS Founder
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Posted on mongodb google news. Visit mongodb google news

Virtual machines are software-based applications that allow you to run a virtual ecosystem or computer within a physical host computer. They basically allow you to emulate an entire computer system for testing, experimentation, resource scaling, and hosting multiple systems.

Laptop closing

Laptop closing

Virtualization as a whole has seen staggering growth. Different implementations have seen adoption from various industries, with IT infrastructure seeing massive server virtualization and the rise of the virtual private network. Statistics show that 23% of companies worldwide are already running serverless thanks to virtualization. With such a large scope, you might be surprised to learn seven interesting facts you might have missed on virtual machines.

1. Different components can be individually virtualized

While it’s general knowledge that virtual machines create virtualized environments that emulate entire operating systems and infrastructure, did you know that components can individually be virtualized as well?

Based on a guide to virtual machines on MongoDB, two types of hypervisors enable each key component to be virtualized on a system. Type 1 hypervisors, also known as bare-metal hypervisors, run directly on a physical machine and can be used for app virtual environments, desktops, and server virtualization. Type 2 are hosted hypervisors, which are installed on a host machine that already has its own OS running. This allows for the VM to focus on specific tasks instead of resource allocation.

2. VM can run faster than native hardware

VirtualBox 6.1.14 on Kubuntu running FedoraVirtualBox 6.1.14 on Kubuntu running Fedora
Photo Credit: Diego Carvalho, under CC BY-SA 4.0 License.

Credit – Diego Carvalho, under CC BY-SA 4.0 License. 

Although a virtual machine generally takes its computing resources from the host machine, studies have actually seen VM implementation see faster performance than native computing. While it’s usually expected for virtualized environments to be slower, we see the likes of vSphere run and perform at a faster rate than native systems after getting properly tuned for its designated workload. Of course, bare-metal servers still usually have better average results despite VMs hitting higher peaks.

3. Virtual machines go beyond computers

Android recovery screenAndroid recovery screen
Photo Credit : Power, under CC BY-SA 4.0 License.

Virtual machines are not limited to computers! You can see that VM software or applications work for mobile phones, cloud products, and embedded systems. Android makes use of ART implementation, making it compatible and capable of running a virtual machine without crashing. In 2023, you can run a robust virtual machine on your smartphone just by downloading Andronix and VNC Viewer from Google Play. The latter is an optional installation that provides a graphical user interface (GUI) for users who don’t want to be limited to a command line.

4. NASA uses VMWare

NASA commercialization campNASA commercialization camp
NASA commercialization camp; Photo Credit: Bill Stafford, under CC BY 2.0 License.

NASA has seen many great strides through the years, with its most recent grand foray resulting in amazing accomplishments from the James Webb Space Telescope’s first year. With its many huge undertakings and complex responsibilities, it is notable that the NASA Enterprise Application Competency Center (NEACC) makes use of massive virtualized server environments. NEACC operates more than 337 virtual machines using VMWare.

5. Virtualization and virtual machine types aren’t the same

Virtualization design

Virtualization design

There are different types of virtualization, but this isn’t the same thing as the different types of virtual machines. You can categorize virtual machines into two basic types: system VMs and process VMs. Where things become more complex is in the type of virtualization you run on any given VM. This can be desktop, network, hardware, storage, data, application, GPU, cloud, or Linux virtualization.

6. VMs were first developed in the 1960s

IBM WatsonIBM Watson
IBM Watson

Although the very concept of a virtual machine feels modern, it actually predates the modern hardware we work on today. Although the patent for virtualization systems that make use of segmented architecture using a virtual machine monitor belongs to and was filed by VMWare in 1998, the very first iteration goes all the way back to the 1960s.

In this era, IBM would create the M44/44X, CP-40, and SIMMON to perform tasks using virtualization. They would serve as the very first hypervisors, eventually making way for the CP-67/CMS, a virtual machine OS developed for time sharing. This would eventually be made widely available in 1972 as VM/370.

7. You can use a VM for obsolete programs

Inner workings of a virtual machine

Inner workings of a virtual machine

Today, virtual machines are usually used for software development, testing, server optimization, resource allocation, and disaster recovery. That said, virtual machines have long been used to host legacy applications and obsolete programs that would no longer run properly on modern hardware. Think of it like how Back to the Future 2 used technology from the 1700s. Virtual machines allow users to fully launch and operate systems that would otherwise be considered unusable in today’s computing environments.

Article originally posted on mongodb google news. Visit mongodb google news

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MongoDB, Inc. (NASDAQ:MDB) Director Dwight A. Merriman Sells 6000 Shares of Stock

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Posted on mongodb google news. Visit mongodb google news

MongoDB, Inc. (NASDAQ:MDBGet Free Report) Director Dwight A. Merriman sold 6,000 shares of the stock in a transaction that occurred on Friday, August 4th. The shares were sold at an average price of $415.06, for a total transaction of $2,490,360.00. Following the transaction, the director now owns 1,207,159 shares in the company, valued at $501,043,414.54. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which is accessible through this link.

MongoDB Stock Performance

Shares of MDB stock opened at $370.19 on Wednesday. MongoDB, Inc. has a 12-month low of $135.15 and a 12-month high of $439.00. The business’s 50 day moving average is $392.37 and its two-hundred day moving average is $286.63. The company has a current ratio of 4.19, a quick ratio of 4.19 and a debt-to-equity ratio of 1.44.

MongoDB (NASDAQ:MDBGet Free Report) last released its earnings results on Thursday, June 1st. The company reported $0.56 EPS for the quarter, topping analysts’ consensus estimates of $0.18 by $0.38. MongoDB had a negative net margin of 23.58% and a negative return on equity of 43.25%. The business had revenue of $368.28 million during the quarter, compared to analysts’ expectations of $347.77 million. During the same quarter in the previous year, the company earned ($1.15) earnings per share. The company’s quarterly revenue was up 29.0% on a year-over-year basis. On average, equities analysts expect that MongoDB, Inc. will post -2.8 earnings per share for the current fiscal year.

Institutional Investors Weigh In On MongoDB

Large investors have recently modified their holdings of the business. Jennison Associates LLC boosted its position in MongoDB by 101,056.3% during the second quarter. Jennison Associates LLC now owns 1,988,733 shares of the company’s stock worth $817,350,000 after acquiring an additional 1,986,767 shares during the last quarter. 1832 Asset Management L.P. raised its holdings in shares of MongoDB by 3,283,771.0% during the 4th quarter. 1832 Asset Management L.P. now owns 1,018,000 shares of the company’s stock worth $200,383,000 after acquiring an additional 1,017,969 shares during the period. Price T Rowe Associates Inc. MD boosted its holdings in MongoDB by 13.4% in the 1st quarter. Price T Rowe Associates Inc. MD now owns 7,593,996 shares of the company’s stock valued at $1,770,313,000 after purchasing an additional 897,911 shares during the last quarter. Renaissance Technologies LLC increased its stake in MongoDB by 493.2% during the fourth quarter. Renaissance Technologies LLC now owns 918,200 shares of the company’s stock worth $180,738,000 after acquiring an additional 763,400 shares during the last quarter. Finally, Norges Bank purchased a new stake in MongoDB in the fourth quarter valued at $147,735,000. Hedge funds and other institutional investors own 89.22% of the company’s stock.

Analysts Set New Price Targets

Several research firms have recently commented on MDB. Stifel Nicolaus boosted their price target on MongoDB from $375.00 to $420.00 in a research report on Friday, June 23rd. Oppenheimer raised their price target on shares of MongoDB from $270.00 to $430.00 in a report on Friday, June 2nd. Citigroup upped their price target on MongoDB from $363.00 to $430.00 in a research report on Friday, June 2nd. VNET Group reaffirmed a “maintains” rating on shares of MongoDB in a research report on Monday, June 26th. Finally, Truist Financial boosted their price objective on shares of MongoDB from $365.00 to $420.00 in a report on Friday, June 23rd. One investment analyst has rated the stock with a sell rating, three have given a hold rating and twenty have issued a buy rating to the company. According to data from MarketBeat, the stock presently has a consensus rating of “Moderate Buy” and a consensus price target of $378.09.

Check Out Our Latest Stock Analysis on MongoDB

MongoDB Company Profile

(Get Free Report)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Amazon EC2 M7i and M7i-flex Instances Now Available for General-Purpose Workloads

MMS Founder
MMS Steef-Jan Wiggers

Article originally posted on InfoQ. Visit InfoQ

AWS recently announced the general availability (GA) of Amazon EC2 M7i and M7i-flex instances, equipped with custom 4th Gen Intel Xeon Scalable processors (code name Sapphire Rapids). The Amazon EC2 M7i and M7i-flex instances are instance types intended for general-purpose workloads providing a balance of compute, memory, and networking resources.

Amazon EC2 M7i and M7i-flex instances are designed for workloads that use resources equally, such as web servers and code repositories. Compared to the EC2 M6i instances announced two years ago, the company claims a 19% better price/performance for the M7i-flex instances and 15% better price performance for the M7i instances.

The M7i-flex instances are a lower-cost variant of the M7i instances. They are available in the five most common sizes with dimensions ranging from 2 vCPU and 8 GiB memory (m7i-flex.large) to 32 vCPU and 128 GiB memory (m7i-flex.8xlarge) – each having up to 12.5 Gbps network bandwidth and up to Gbps EBS bandwidth. Ideal, according to the company, for running general-purpose workloads such as web and application servers, virtual desktops, batch processing, micro-services, databases, and enterprise applications.

On a Reddit thread, the M7i-flex raises questions about being lower-cost, as a respondent seligman99 asks:

Given that it’s on the 4th gen Xeon, I wonder if it has something to do with how they’re selling the High/Low Priority Cores to users?

With other Mutjny responding:

That’s actually a great point. These only go up to 8xlarge, and there is a chart that lists them as “Base Performance / vCore: 40%,” so I think that’s very strongly what it is. Except these m7i-flex are Intel Xeon Platinum 8488C, which shows is a Sapphire Rapids chipset, which would seem to contra-indicate these are low-performance cores in a mixed core CPU.

On the other hand, M7i instances are available in nine sizes ranging from 2 vCPU and 8 GiB memory (m7i.large) to 192 vCPU and 768 GiB memory (m7i.48xlarge) – with increasing network bandwidth up to 50 GB and up to 40 Gb ESB bandwidth. According to the company, these instances are recommended for workloads such as large application servers and databases, gaming servers, CPU-based machine learning, and video streaming.

Also, with built-in accelerators like Intel Advanced Matrix Extensions (Intel AMX) in the Sapphire Rapids processors, Intel states in a press release:

Built-in accelerators like Intel Advanced Matrix Extensions (Intel AMX) offer a much-needed alternative in the market for customers with growing AI workload demand. 4th Gen Xeon with AMX can also meet inference performance metrics for large language models (LLMs) below 20 billion parameters, making LLMs both cost-effective and sustainable to run on general-purpose infrastructure.

The other built-in accelerators in the Sapphire Rapids processors are:

  • Intel’s Data Streaming Accelerator (DSA) enhances performance for storage, networking, and data-intensive tasks by efficiently handling data movement between CPU, memory, caches, network devices, and storage devices.
  • In-Memory Analytics Accelerator (IAA) boosts database and analytic workloads’ speed and potential power efficiency through high-throughput in-memory compression, decompression, and encryption.
  • QuickAssist Technology (QAT) relieves processor cores by offloading encryption, decryption, and compression tasks, reducing power consumption while facilitating merged compression and encryption within a single data flow.

Furthermore, future additions to the M7i family will include bare-metal sizes suited to high-transaction and latency-sensitive workloads.

Alongside AWS, Azure and Google Cloud offer a wide selection of instance types and varying combinations of storage, CPU, memory, and networking capacity, allowing organizations to scale their resources to match the demands of their specific workloads. For instance, Microsoft offers various Virtual Machines for general-purpose workloads; the latest is the Dv5-series, equipped with the third-generation Intel Xeon Platinum processor. In comparison, Google Cloud has E2, N2, N2D, and N1 general-purpose machines and C3 instances released last October, including Sapphire Rapids processors.

Currently, the EC2 M7i-flex and EC2 M7i instances are available in the AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), and Europe (Ireland). Furthermore, pricing details of EC2 instances can be found on the pricing page.

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Article: How Emotional Connections Can Drive Change: Applying Fearless Change Patterns

MMS Founder
MMS Mary Lynn Manns

Article originally posted on InfoQ. Visit InfoQ

Key Takeaways

  • Leaders of change must do more than simply communicate information about a new idea. In order to help people care about the information, you must connect with more than logic.
  • When you recognize how people are feeling during times of change, you are creating an emotional connection.
  • There are a variety of Fearless Change techniques that leaders can use to connect with their listeners on an emotional level. Hometown Story, Imagine That, and Wake-Up Call can help people listen with their imagination rather than simply their ears.
  • One-on-one conversations are important for understanding what key stakeholders are thinking. Personal Touch, Fear Less, and Shoulder to Cry On are among the patterns that will help individuals understand how the change will affect them.
  • People resist change for a variety of reasons that manifest fear. This can cause the leader of change to fear the resistors. But resistors can even be helpful during times of change. The Fear Less and Champion Skeptic patterns show us how they help us find the things that may not be going well.

When trying to bring innovation into an organization, communication is important. It is vital to share information in a clear and logical way but it is just as important to understand and accept how people are feeling about the innovation. To do this, leaders can make use of strategies that help them create an emotional connection.

Introduction

When you have an idea you want to introduce into your organization, you know that you will likely spend a significant amount of time trying to persuade others to accept it. We typically begin by communicating a lot of information about the innovation as often as possible to anyone who will listen. And when we think others are not understanding us, or believing us, we tend to give them even more information. Most of us are good at this – we’ve been taught this in school and we continue to improve our skills in our professional lives.

However, the practice of providing information is only the first step in trying to persuade people. We want them to understand what we are saying, but we also want them to take action.

People react to new ideas in a variety of ways – some will be interested, while others may pretend they are listening, push back and argue, or just ignore us. When we simply provide information, we are assuming our listeners are logical beings.

But humans are complex beings with complex emotions that often stand in the way of making a change happen. Therefore, in addition to communicating the information, we need to tap into how they are feeling about it. When we recognize and accept these feelings and connect on an emotional level, we will begin to help our listeners care about what we are saying.

Creating an emotional connection

There are a variety of Fearless Change patterns you can use to create an Emotional Connection, to connect with others on an emotional level. For example, rather than simply conveying all the facts about an innovation, try the Hometown Story pattern. To do this, list the points you want to communicate as well as the main point you hope to leave with the listener – then create a story that joins these together. The purpose is to bring together a collection of disjointed information into a format that is more interesting to hear and easier to remember.

People like to listen to stories. Rather than a collection of bullet points, people can “feel” a story and may even share it with others. An effective story is something others can easily relate to. The content includes desires, struggles and a lesson. Most of all, it has a storyteller (that’s you) who is willing to be authentic and perhaps a little vulnerable. Stories can be fun to write. Think about how you can help others connect with it – when you do this, your listeners will begin to care about the information you are sharing in that story.

Another technique you can use is the Fearless Change pattern called Imagine That. To do this, guide people in imagining a new world by first prompting a discussion about the present state of things. For example, “How would you describe your work in our current software development process?” People will likely bring up problems, which gives them a Wake-Up Call (this is another Fearless Change pattern, which is described on the website). Then, prompt a discussion of new possibilities with a question such as, “What would things look like if we did ?” ( is the new idea). A group discussion with the Imagine That and Wake-Up Call patterns help people feel the current problems and feel the potential world with the new idea – this is because they are using their imagination rather than simply their ears.

These two techniques work well in a team but a one-on-one conversation, with the Personal Touch pattern, is among the most powerful strategies a change leader can use. People take change personally. While we may be talking about the value the new idea will bring to the organization, each individual is likely thinking, “How will it affect me?”

Therefore, change leaders can discover great value in taking time for personal conversations to discuss how the new idea will affect individuals. It is the best way we can truly understand what others are thinking and, more importantly, what they are feeling. These conversations can be time-consuming in your busy schedule, so you may want to begin with Early Adopters, which are people who are seen as the opinion leaders in the organization as well as the Connectors, which are people who are good at connecting with others.

One way to conduct a Personal Touch conversation is to peel the onion – begin with a question such as “I’m curious about what you think about “. Once the person answers, ask your second question based on the response to the first. Ask your third question based on the answer to the second, and so on. When you do this, you will peel away the surface logic and reveal how this person feels about the . These are the feelings that often obstruct efforts to make change happen.

Another way to have a personal touch conversation is to listen, truly listen, to a person’s story about all the things that led up to what they are thinking and how they feel about the change initiative. Then you can share your story and compare the two stories.

An example of Personal Touch on a large scale can be found in One Small Step at National Public Radio (NPR) in the United States. This project brings together people with vastly different political views to talk one-on-one, not just to blast information at the other person, but to tell their stories in order to help each participant understand why the other person believes the things they do. I was one of the people selected to participate at WHQR public radio. I didn’t leave my conversation agreeing with the other person, but I did understand their point of view. (For more information on One Small Step, see What we learned taking one small step.)

What about the skeptics?

When using a personal touch with stubborn skeptics, you are likely to encounter some additional challenges. They are resisting because they may be tied to the present reality and are concerned about the inevitable uncertainty during the process of change. They may be anxious about how the change will affect what they do, what they will lose, and the new skills that will be needed. These are reasonable feelings but they are among the reactions that build fear in the individuals you are trying to persuade. In turn, you can’t help but have some fear about their resistance.

The Fear Less pattern suggests that you can appreciate their opposition. Ask for Help from the skeptic because they see the innovation in a different way than you do – therefore, they may be able to provide useful information you haven’t considered. You will learn from them and, in the process, they may begin to shift from the act of resisting to rethinking.

You may not be able to convince them and trying to do this will likely take more time than you have. But you can seek the places where you agree and, perhaps, create some unique ideas that begin with those points of agreement. Most importantly, when you ask for their thoughts on the upcoming change, they will begin to become involved in the initiative, rather than simply complaining on the sidelines. They will recognize you care about what they can contribute and, as one of our Fearless Change readers pointed out, it doesn’t make it as much fun for them to complain.

You may even want to seek out some skeptics to become a Champion Skeptic, taking on the official role of pointing out flaws and challenges at strategic points throughout the change initiative. Look for people who don’t simply complain to complain, but honestly complain because they want to make things better. These people can be the start of a challenge network that can help point out flaws throughout the initiative with the goal of continuous improvement.

Change involves loss

Whether or not an individual is a skeptic or not, it’s important to recognize that while we are talking about everything the organization will gain from an initiative, individuals will think about what they will lose. They may lose their identity, what they are accustomed to doing, and their valuable time in learning the new ways. Therefore, the Shoulder to Cry On pattern encourages leaders to acknowledge what people are losing during times of change. You may not have the power to make everyone happy, but taking the time to discuss their struggles will go a long way towards sending the message that you understand how they are feeling.

Even when you’re communicating good news about progress during a change initiative, others may not be as pleased with all the progress. So you may wish to start an ordinary meeting on a positive note by including some food. The Do Food pattern is one of the most recognized strategies in the collection. People have long realized the importance of building community by breaking bread together. Just like many of the other patterns, this simple act helps people see that you accept them as humans with feelings – in this case, a feeling of hunger.

Summary

All the patterns shown in italics allow you, as a leader of change, to move past the act of simply giving information to connecting on an emotional level with those you are trying to persuade. This will allow you to uncover how people are feeling about the innovation because this is what often stands in the way of making change happen.

We know that humans are not completely logical. They do not make decisions simply by evaluating what they think about an idea – they also consider how they feel about it. Although it’s rather easy to give information, this is not always effective. You may see polite listening but little action.  

Therefore, a “fearless” leader who is realistic about human behavior will need a variety of emotional connection patterns. When others see that you care about them, they will be more willing to consider taking action. They will not only connect with an innovation in a logical way so they understand it, but also connect with it emotionally so they care about it.

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Database Servers Market Future Outlook 2023-2030 | Emerging Trends and Innovations …

MMS Founder
MMS RSS

Posted on mongodb google news. Visit mongodb google news

PRESS RELEASE

Published August 8, 2023

The Database Servers Market shows promising future growth prospects 2023-2030 driven by its dynamic nature and expanding size. Report covers Market segmentation details by Type [Relational Database Server, Time Series Database Server, Object Oriented Database Server, Navigational Database Server], Applications [Relational Database Server, Time Series Database Server, Object Oriented Database Server, Navigational Database Server], and Top Regions.

Social-Media-Graph-01

A Recent Research on “Database Servers Market” presents comprehensive analysis of industry growth prospects and global trends of Top Manufacturers [ASG Technologies, Amazon, SAS Institute, MongoDB, Pimcore GmbH, Information Builders, Microsoft]. The report focuses on SWOT analysis, CAGR status and revenue projection of stakeholders. Moreover, the report [127 Pages] offers a detailed assessment of market segmentations, business development strategies, and expansion plans across various regions.

By highlighting influencing growth factors and the latest industry technologies, the report displays the dynamic nature of the Database Servers market. It combines strategic assessment of top players, historical and current market performance, and new investment opportunities, presenting a holistic view of the industry landscape. The analysis of supply-demand scope, trade statistics, and manufacturing cost structure further enhances the report’s reliability.

Get a sample PDF of the report at – https://www.marketresearchguru.com/enquiry/request-sample/23431249

Market Overview of Global Database Servers market:
According to our latest research, the global Database Servers market looks promising in the next 5 years. As of 2022, the global Database Servers market was estimated at USD million, and it’s anticipated to reach USD million in 2028, with a growing CAGR during the forecast years.

Research Scope:

This report provides an overview of the global Database Servers market and analyzes the segmented market by product type, downstream industry, and region, presenting data points such as sales, revenue, growth rate, explaining the current status and future trends of the Database Servers and its sub-markets in an intuitive way.

Top Key Players Are:

  • ASG Technologies
  • Amazon
  • SAS Institute
  • MongoDB
  • Pimcore GmbH
  • Information Builders
  • Microsoft
  • Dell
  • FUJITSU
  • TIBCO Software
  • Profisee Group
  • Oracle
  • IBM
  • NetApp
  • Redis Labs
  • The PostgreSQL Global Development Group
  • Tealium
  • SAP

These players are increasingly focusing on strategic collaborations, mergers and acquisitions, and new product launches to expand their market share and improve their competitive positioning. Additionally, they are investing heavily in research and development to develop innovative solutions and enhance their technological capabilities.

Get a sample PDF of the Database Servers Market Report

Industry Segments by Type:

  • Relational Database Server
  • Time Series Database Server
  • Object Oriented Database Server
  • Navigational Database Server

Industry Segments by Application:

  • Relational Database Server
  • Time Series Database Server
  • Object Oriented Database Server
  • Navigational Database Server

The report delivers a comprehensive study of all the segments and shares information regarding the leading regions in the market. This report also states import/export consumption, supply and demand Figures, cost, industry share, policy, price, revenue, and gross margins.

Key Factors Considered in this Report:

COVID-19 Impact:

Amid the COVID-19 crisis, the Database Servers market has definitely taken a hit. The report describes the market scenario during and post the pandemic in the vision of upstream raw materials, major market participants, downstream major customers, etc. Other aspects, such as changes in consumer behavior, demand, transport capacity, trade flow under COVID-19, have also been taken into consideration during the process of the research.

Regional Conflict / Russia-Ukraine War:

The report also presents the impact of regional conflict on this market in an effort to aid the readers to understand how the market has been adversely influenced and how it’s going to evolve in the years to come.

Challenges and Opportunities:

Factors that may help create opportunities and boost profits for market players, as well as challenges that may restrain or even pose a threat to the development of the players, are revealed in the report, which can shed a light on strategic decisions and implementation.

Inquire or Share Your Questions If Any Before the Purchasing This Report – https://www.marketresearchguru.com/enquiry/pre-order-enquiry/23431249

Here are Some Reasons to Buy Database Servers Market Report:

In-depth Market Insights: The Database Servers Market Report provides a comprehensive and detailed analysis of the market, including market size, share, trends, and growth drivers. It offers valuable insights into the current state of the market and its future potential.

Accurate Forecasting: The report offers reliable forecasts and projections based on robust research methodologies, industry spending, and market growth rates. This helps businesses make informed decisions and plan for the future effectively.

Competitive Analysis: The report includes a thorough examination of key players, their product portfolios, and market strategies. This enables businesses to understand their competitors and devise strategies to stay ahead in the market.

Market Scope and Segmentation: The report defines the market scope comprehensively, providing a clear understanding of various market segments and their potential growth opportunities.

Regional Analysis: The report analyzes the market’s performance in different regions, enabling businesses to identify lucrative markets and tailor their strategies accordingly.

Industry Technology Analysis: The report evaluates the technological advancements in the Database Servers market, giving businesses insights into cutting-edge technologies and innovation trends.

Supply and Demand Analysis: Understanding the supply-demand dynamics helps businesses optimize their production and distribution processes and manage inventory efficiently.

Investment Decision Support: The report provides critical information and data-driven insights that aid businesses in making informed investment decisions in the Database Servers market.

Long-term Growth Prospects: By analyzing trends and growth drivers, the report highlights the long-term potential of the Database Servers market, helping businesses identify sustainable growth opportunities.

Reasons to purchase the Database Servers market report:

  • The global Database Servers report comprises of precise and up-to-date statistical data.
  • The report will provide in-depth market analysis of Database Servers industry.
  • All the market competitive players in the Database Servers industry are offered in the report.
  • The business strategies and market insights will help readers and the interested investors boost their overall business.
  • The report will help in decision-making process for gaining momentum in the business growth in the coming years.
  • What are the challenges to the Database Servers market growth?
  • What are the key market trends impacting the growth of Database Servers market?

To Understand How Covid-19 Impact Is Covered in This Report https://marketresearchguru.com/enquiry/request-covid19/23431249

Geographically, the report includes the research on production, consumption, revenue, market share and growth rate, and forecast (2018 -2028) of the following regions:

  • North America (United States, Canada)
  • Europe (Germany, UK, France, Italy, Spain, Russia, Netherlands, Turkey, Switzerland, Sweden)
  • Asia Pacific (China, Japan, South Korea, Australia, India, Indonesia, Philippines, Malaysia)
  • Latin America (Brazil, Mexico, Argentina)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, South Africa)

Following Chapter Covered in the Database Servers Market Research:

Chapter 1 mainly defines the market scope and introduces the macro overview of the industry, with an executive summary of different market segments ((by type, application, region, etc.), including the definition, market size, and trend of each market segment.

Chapter 2 provides a qualitative analysis of the current status and future trends of the market. Industry Entry Barriers, market drivers, market challenges, emerging markets, consumer preference analysis, together with the impact of the COVID-19 outbreak will all be thoroughly explained.

Chapter 3 analyzes the current competitive situation of the market by providing data regarding the players, including their sales volume and revenue with corresponding market shares, price and gross margin. In addition, information about market concentration ratio, mergers, acquisitions, and expansion plans will also be covered.

Chapter 4 focuses on the regional market, presenting detailed data (i.e., sales volume, revenue, price, gross margin) of the most representative regions and countries in the world.

Chapter 5 provides the analysis of various market segments according to product types, covering sales volume, revenue along with market share and growth rate, plus the price analysis of each type.

Chapter 6 shows the breakdown data of different applications, including the consumption and revenue with market share and growth rate, with the aim of helping the readers to take a close-up look at the downstream market.Chapter 7 provides a combination of quantitative and qualitative analyses of the market size and development trends in the next five years. The forecast information of the whole, as well as the breakdown market, offers the readers a chance to look into the future of the industry.

Chapter 8 is the analysis of the whole market industrial chain, covering key raw materials suppliers and price analysis, manufacturing cost structure analysis, alternative product analysis, also providing information on major distributors, downstream buyers, and the impact of COVID-19 pandemic.

Chapter 9 shares a list of the key players in the market, together with their basic information, product profiles, market performance (i.e., sales volume, price, revenue, gross margin), recent development, SWOT analysis, etc.

Chapter 10 is the conclusion of the report which helps the readers to sum up the main findings and points.

Chapter 11 introduces the market research methods and data sources.

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Detailed TOC of Database Servers Market Forecast Report 2023-2028:

1 Database Servers Market Overview

1.1 Product Overview and Scope of Database Servers Market

1.2 Database Servers Market Segment by Type

1.2.1 Global Database Servers Market Sales Volume and CAGR (%) Comparison by Type (2018-2028)

1.3 Global Database Servers Market Segment by Application

1.3.1 Database Servers Market Consumption (Sales Volume) Comparison by Application (2018-2028)

1.4 Global Database Servers Market, Region Wise (2018-2028)

1.5 Global Market Size of Database Servers (2018-2028)

1.5.1 Global Database Servers Market Revenue Status and Outlook (2018-2028)

1.5.2 Global Database Servers Market Sales Volume Status and Outlook (2018-2028)

1.6 Global Macroeconomic Analysis

1.7 The impact of the Russia-Ukraine war on the Database Servers Market

2 Industry Outlook

2.1 Database Servers Industry Technology Status and Trends

2.2 Industry Entry Barriers

2.2.1 Analysis of Financial Barriers

2.2.2 Analysis of Technical Barriers

2.2.3 Analysis of Talent Barriers

2.2.4 Analysis of Brand Barrier

2.3 Database Servers Market Drivers Analysis

2.4 Database Servers Market Challenges Analysis

2.5 Emerging Market Trends

2.6 Consumer Preference Analysis

2.7 Database Servers Industry Development Trends under COVID-19 Outbreak

2.7.1 Global COVID-19 Status Overview

2.7.2 Influence of COVID-19 Outbreak on Database Servers Industry Development

3 Global Database Servers Market Landscape by Player

3.1 Global Database Servers Sales Volume and Share by Player (2018-2023)

3.2 Global Database Servers Revenue and Market Share by Player (2018-2023)

3.3 Global Database Servers Average Price by Player (2018-2023)

3.4 Global Database Servers Gross Margin by Player (2018-2023)

3.5 Database Servers Market Competitive Situation and Trends

3.5.3 Mergers and Acquisitions, Expansion

4 Global Database Servers Sales Volume and Revenue Region Wise (2018-2023)

4.1 Global Database Servers Sales Volume and Market Share, Region Wise (2018-2023)

4.2 Global Database Servers Revenue and Market Share, Region Wise (2018-2023)

4.3 Global Database Servers Sales Volume, Revenue, Price and Gross Margin (2018-2023)

5 Global Database Servers Sales Volume, Revenue, Price Trend by Type

5.1 Global Database Servers Sales Volume and Market Share by Type (2018-2023)

5.2 Global Database Servers Revenue and Market Share by Type (2018-2023)

5.3 Global Database Servers Price by Type (2018-2023)

5.4 Global Database Servers Sales Volume, Revenue and Growth Rate by Type (2018-2023)

6 Global Database Servers Market Analysis by Application

6.1 Global Database Servers Consumption and Market Share by Application (2018-2023)

6.2 Global Database Servers Consumption Revenue and Market Share by Application (2018-2023)

6.3 Global Database Servers Consumption and Growth Rate by Application (2018-2023)

7 Global Database Servers Market Forecast (2023-2028)

7.1 Global Database Servers Sales Volume, Revenue Forecast (2023-2028)

7.2 Global Database Servers Sales Volume and Revenue Forecast, Region Wise (2023-2028)

7.3 Global Database Servers Sales Volume, Revenue and Price Forecast by Type (2023-2028)

7.4 Global Database Servers Consumption Forecast by Application (2023-2028)

7.5 Database Servers Market Forecast Under COVID-19

8 Database Servers Market Upstream and Downstream Analysis

8.1 Database Servers Industrial Chain Analysis

8.2 Key Raw Materials Suppliers and Price Analysis

8.3 Manufacturing Cost Structure Analysis

8.4 Alternative Product Analysis

8.5 Major Distributors of Database Servers Analysis

8.6 Major Downstream Buyers of Database Servers Analysis

8.7 Impact of COVID-19 and the Russia-Ukraine war on the Upstream and Downstream in the Database Servers Industry

9 Players Profiles

10 Research Findings and Conclusion

11 Appendix

11.1 Methodology

11.2 Research Data Source

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ReSharper 2023.2: New Features, AI Assistant, and Predictive Debugger Mode

MMS Founder
MMS Almir Vuk

Article originally posted on InfoQ. Visit InfoQ

Beginning this month, the JetBrains team announced the release of ReSharper version 2023.2, a popular Visual Studio extension, bringing significant new features for C# and C++ programming languages, and performance improvements, alongside productivity tools related to unit testing, assembly difference analysis, AI Assistant and predictive debugger mode.

ReSharper 2023.2 introduces a wave of enhancements to C# language support. Notable improvements include handling raw string literals with new inspections and formatting options. The update also provides solutions for common Entity Framework issues, ensuring smoother development. Developers will benefit from enhanced code readability with two new inspections targeting local functions.

Improved nullability support, improved navigation for var declarations, and support for default parameter values in lambda expressions. Noteworthy additions encompass primary constructor support for non-record classes and structs, better object disposal control, improved C# discard support, and inlay hints for elevated code comprehension.

The C++ language receives notable support in the 2023.2 release. Among the key highlights are integrating C++23 features, including support for if consteval, static operator(), and operator[], along with support for the C++23 standard library modules. Noteworthy additions are also for C++20 [[no_unique_address]] attribute, support for modules such as the recognition of .cppm files as module interfaces. 

Regarding the C++, this release also introduces a Safe Delete refactoring tool, optimized Blueprint indexing for Unreal Engine solutions, improved type completion with respect to concepts or traits, a more intuitive Go to Declaration feature, and for recursive calls, ReSharper C++ will now mark them in the gutter, making it more visible.

When it comes to the performance the author of the original release post, Sasha Ivanova, states the following:

During this release cycle, we’ve done a lot of work to improve the solution loading speed in ReSharper. We’ve revised our approach to data caching and refactored some of ReSharper’s internal component construction logic. As a result, even the largest of solutions load noticeably faster.

A significant addition is the AI Assistant in the 2023.2 versions of IntelliJ-based IDEs and .NET tools. This addition brings AI-powered features, including an integrated chat function. The AI Assistant’s capabilities are explaining selected code segments, identifying potential issues, and generating XML documentation. Notably, AI Assistant requires separate installation and is currently accessible through a waiting list. As reported, for those who have previously used AI Assistant during the 2023.2 EAP cycle, the functionality will be reinstated upon installation of the product.

Other notable changes are related to unit testing, alongside Assembly diff in the decompiler. As reported, this functionality proves particularly valuable when examining variances between two iterations of a particular assembly and identifying potential vulnerabilities that might have been introduced.

Also, the new predictive debugger mode has been included. This mode allows the system to anticipate potential program states without requiring actual execution. The predictive debugger is currently in beta and only available in ReSharper. The original blog post references the detailed blog post about the usage of predictive debugger mode and the exploration of it.

In addition to the release post, a recent Twitter thread revealed optimistic expansions of the predictive debugger for the Rider as well. In response to a query about the potential integration of a specific feature, the official account confirmed ongoing efforts. The tweet affirmed that the team is actively working on incorporating the feature into Rider.

For additional insights, readers are encouraged to explore the details available on the What’s New in ReSharper 2023.2 page. The array of resolved requests can be found through the public bug tracker. Also, it is stated that user feedback is eagerly awaited and greatly valued.

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Ai4 2023 Day One Main Stage Recap

MMS Founder
MMS Anthony Alford

Article originally posted on InfoQ. Visit InfoQ

Day One of the Ai4 2023 conference was held on August 8th, 2023, at the MGM Grand hotel in Las Vegas, Nevada. This two-day event is organized by Fora Group and includes tracks focused on various industries, including automotive, financial, healthcare, and government. The day began with six mainstage presentations from leaders in AI technology.

The first speaker was Nikola Todorovic, Co-Founder and CEO of visual effects firm Wonder Dynamics. Todorovic’s talk was “AI Through The Lens Of Filmmaking,” which began with a short history of filmmaking, framing the theme that advances in the industry were often driven by increasing accessibility, to both the filmmakers and the audience. Todorovic’s company uses AI to advance that goal of increasing accessibility, reducing the cost for filmmakers to use CGI effects in their films by automating much of the “grunt work” these effects require, “up to 80 to 90% in some instances.”

Next up was Conor Jensen, Americas Field CDO at Dataiku. In his talk “Natural Selection In Everyday AI,” Jensen gave three examples of successful adaptations that companies make when successfully adopting AI, and three “evolutionary dead ends.” The successful adaptations are: building a “data science lifecycle” process of deciding what projects to work on; build an AI friendly culture, from both top-down and bottom-up in the organization; and investing in talent at all levels of the organization, which includes training front-line workers to interpret AI model output. The dead ends were: overly complex tech stacks; ineffective organizational structures; and siloed people and data.

Joann Stonier, EVP and Mastercard Fellow of Data and AI, followed with a talk on “Next Generation Innovation: A Responsible Road Map For AI.”  Her roadmap consisted of eight components: principles, governance, data examination, analytics and risk, outcome assessment, interaction with LLMs, distance and evaluation, and review boards and committees. The fundamental principle of this roadmap is that “an organization’s data practices must be guided by the rights of individuals.” Furthermore, the higher the risk of a possible negative outcome from using an AI model, the more “distance” there should be between the model’s output and the actual outcome; she gave an example of a person being accused of a crime based solely on a facial recognition model output.

Arijit Sengupta, Founder and CEO of Aible, and Daniel Lavender, Senior Director of Advanced Analytics Insights and Architecture at Ciena, gave a case study of Ciena’s adoption of Aible’s generative AI platform. Aible’s platform introduces the “information model,” the equivalent of the vector database for structured data. It also uses an explainable AI to “double-check” generated natural language statements against actual data, and presents users with charts of real data that are linked to natural language statements.

Next on the stage was a “fireside chat” on “Cracking An Outdated Legal System With AI” between Brandon Deer, Co-founder and General Partner at Crew Capital and Joshua Browder, CEO of DoNotPay. Browder’s company began as a simple repository of template letters to help users dispute traffic and parking citations; now the company uses generative AI agents to automate over 200 consumer rights processes. Browder noted that DoNotPay uses the open-source GPT-J model for generative AI because OpenAI “wouldn’t be happy” with some of their use cases. Browder drew applause toward the end of his talk when he mentioned that his company offers a product that can help users sue robo-callers.

The final talk was Luv Tulsidas, Founder and CEO of Techolution, on “Building The Enterprise Of Tomorrow With Real-World AI.” Tulsidas noted that according to Forbes, 91% of companies are investing in AI, but fewer than 1% of AI projects are providing RoI. To address this, Tulsidas offered five “secrets” of AI: any commercial AI product will only solve about 80% of your business use cases; you should focus only on specific-purpose AI projects; there are four categories of AI, from lab-only to fully-autonomous; that companies should create AI centers of excellence consisting of six core personas; and finally, that autonomous AI requires reinforcement learning with expert feedback (RHEF).

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S&P 500, Nasdaq Pare Losses Near Key Support; MongoDB, Nvidia, SLB In Focus – Video

MMS Founder
MMS RSS

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Notice: Information contained herein is not and should not be construed as an offer, solicitation, or recommendation to buy or sell securities. The information has been obtained from sources we believe to be reliable; however no guarantee is made or implied with respect to its accuracy, timeliness, or completeness. Authors may own the stocks they discuss. The information and content are subject to change without notice. *Real-time prices by Nasdaq Last Sale. Realtime quote and/or trade prices are not sourced from all markets.

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Presentation: Building Typesafe APIs with tRPC & TypeScript

MMS Founder
MMS Brian Douglas

Article originally posted on InfoQ. Visit InfoQ

Transcript

The Basketball Analogy

Brian Douglas: I’m going to give a quick little history lesson all the way from 2020. In 2020, there was this documentary called, The Last Dance, and it was based on Michael Jordan. Here pictured are the very first Air Jordan 1’s. Actually, these are not the exact copy, because there was way more white than that. What I’m getting at is, I put some shoes there just to represent that documentary, because that’s what I could put there. I spent a lot of time watching that documentary. I’m a big basketball fan. I watched Michael Jordan during his last season. I might have been 8 or 9 years old at that point. Going through that I got to relive the experience of his last season. Ironically, it wasn’t his last season because he actually retired multiple times after the fact. What I’m getting at is, during that time in the documentary, they were talking about what made Michael Jordan the greatest.

One thing they mentioned was actually, not just the fact that he was the greatest player of all time, but he was in the greatest system of all time too as well. I wanted to explain, this is a basketball court. Object of the game is to get the ball in the opposing team’s hoops, like soccer, but with your hands. During this era, it was like the Shut Up and Jam era. The same year when he was drafted, Charles Barkley was drafted, and there was a game called Shut Up and Jam, Sega Genesis. I bring that up, because it was always about getting the ball to the opposing team’s hoop. Eventually, they discovered years later, using statistics, that there are certain parts of the court are just really hard to defend. This one part which is on the right side of the foul line, it’s called Area 31. The reason for that is because 31% of the shots go in at that point.

I bring this up, because now in basketball, it’s very clear that everyone plays place to the best place to get the best shot. There’s 5 people on the court, so 1, 2, 3, 4, 5. If you are the number 1, you have an option to pass it to number 2, or if they’re double guarded, there’s probably someone else wide open. Number 5 is going to be in the triangle as well. Number 4 is going to be in the other triangle. Basically, what I’m getting at is, the best players, they don’t have to think about what’s happening next, they always know there’s someone to the left or to the right. Getting back to Michael Jordan, he was the best player because he trained himself and he practiced enough that he always knew what situation he was in, and had to get the ball either to the hoop or to a teammate on the court. I bring this up as more of an anecdote analogy, because I honestly think that when it comes to DevOps, or engineering, or building APIs, it’s all about having the most at-bats. Then when you come up with the issues like solving bugs, it’s all about having that familiarity or running plays, or frameworks.

Outline

This talk is going to walk through three different sections. One, we’re just going to talk about building modern APIs, really in the context of Node. I don’t have context in building APIs in C or C++, or if that’s something you do in C++. It’s all going to be around in the context of JavaScript, really talking to frontend engineers, but also people who support frontend engineers as well. If you work on the backend, looking for next level stuff to build your APIs with, this is a talk for you. We’re going to talk about prototyping quicker with tRPC. tRPC is the framework that makes it a lot easier to approach building strongly typed APIs. Then I’ll touch briefly at the end about t3-app. This is a CLI tool to build apps quicker. At the very least, we should have some context of these libraries, these frameworks on how to build APIs.

Background

My name is Brian Douglas. I’m the Michael Jordan of open source. I mention that because I’m actually building a project, open source. The theme is taken from the company that I’m working at, called OpenSauced. We’re building a tool to empower the best developers who work in open source. If you feel like you’re the greatest, or if you want to find insights on who is the greatest in open source, that’s what we’re hoping to build. Currently, it’s just a platform to index Hacktoberfest, but we’re looking to build more stuff in the future. I bring this up really for the sake of, I was exploring tRPC for one of our newer products. It was more like an exploration. I didn’t really spend a lot of time with the rest of the team. It’s more of like I heard tRPC multiple times in different contexts and conversations, and I wanted to try it out myself. That’s what this talk is. It’s me evaluating tRPC for my use case, which is building APIs that multiple people can consume.

Last year, I had a conversation with Mike Cavaliere talking about rethinking your stack. Because just like where I’m at right now is every single piece of your stack, there’s always that opportunity to introduce something new, or level up another piece. Because sometimes code gets stale, sometimes libraries get undermaintained. That was a conversation I had with Mike on my podcast called Jamstack Radio. Also, my current implementation for the API, api.opensauced.pizza, it’s the API that we built a platform on top of, really so that way anybody can build their own tooling on top of OpenSauced. We’re actually currently using REST API, and using the OpenAPI spec, Swagger. That’s the context that I can give on where I’m coming from, and how I’m approaching evaluating these APIs or these tools.

Building Modern APIs

Let’s talk about how people are building modern APIs today. If you’re currently working, at least in the JavaScript ecosystem, maybe you’re in Rails or Django, you have a couple options. Swagger, GraphQL, are probably the most two popular options. Swagger has been around a bit. It gives you structure around building your REST APIs. It also helps you to maintain and document those APIs as well, by giving you some hooks and automation to create that documentation. GraphQL is a spec. It came out of Facebook. It is just that, it’s a spec but a standard. It’s actually prescribed ways on how to grab data from the server. It takes a lot of work to set up. Once you’re set up, you do get a lot of benefit from having GraphQL. I spend a lot of time using both of these a lot, really a lot of GraphQL.

OpenSauced, the original project that I built years ago as a side project was built on top of the GitHub GraphQL API. It was the reason I learned GraphQL is because of that project, and because the GitHub GraphQL API is so open and ready to be used. I use the analogy of peanut butter and jelly, describing clients and servers. Because those separation of concerns is how I know and how I operate when developing my code bases. I tend to have a backend and a frontend. I tend to have them pretty separate in different folders. I also deploy them separately as well. That really just keeps us nimble to be able to have the backend deploy independent of the frontend. It also enforces being able to have a public API. If we ever want to build tooling, or if we have to worry about security, that’s all built in. We take that into consideration upfront.

TypeScript Remote Procedure Call (tRPC)

I do want to jump into tRPC. tRPC is TypeScript Remote Procedure Call. The RPC, remote procedure call, is not something brand new, it’s something that’s been used multiple times in different languages and frameworks. You might have heard of gRPC. gRPC and tRPC, they share the remote procedure call, but they’re slightly different. tRPC is something I’ve actually only heard of in the last year. Didn’t really give it much of a look. I felt like it was a little early.

We actually built our new project, which I showed earlier, just using Swagger and a REST API. The reason for that, it’s like tRPC, I didn’t really feel like it was ready for me to use. I feel like a lot has changed in the last couple months. tRPC, very similar. You have a structure. You do have a client. You have a server. You have a frontend and backend. What you’re doing is you’re actually doing remote function call. Instead of having some spec to consume or some resolver to consume your backend into your frontend, you’re actually just calling JavaScript functions. What I love about this, is the fact that you can actually have the full-on experience directly in the same project, or in the same repository. Historically, I’ve always had separate repositories. This has been a paradigm shift back into where I was before, prior to separating my concerns using the Jamstack. I am very much excited about the possibilities for tRPC and where it’s taken us.

TypeScript

I want to really just touch on TypeScript. If you’re still running JavaScript, or maybe you haven’t touched TypeScript, at all, I just want to mention that TypeScript is a superset of JavaScript. There’s a compiler involved. What that does, it takes your types and compiles it to something that’s safe, which means that if you write unsafe code, it won’t deploy. Also, a majority of your linter will probably fail, you get warnings. This gives you a huge heads-up. There’s some people in the TypeScript ecosystem that say, you don’t actually have to write tests. I actually recommend you still write tests. It does give you a lot of safety, like maybe tests aren’t as needed. Still write tests.

What I’m getting at is that, so in TypeScript, you have these interfaces, or you have these objects that you can identify, and you can parse those objects into your functions. That way, every time you parse in an object to the function, it is confirmed that this interface itself is always going to have ID, it’s always going to have a first name, last name, or a role. It just knows undefined or unexpected side effects, because now you’re confirming this is the exact format that your code should be formatted in. A thing I want to point out is that TypeScript gives JavaScript devs the ability to do bold things. I missed a word there, but what I’m getting at is like, you can now take chances if you know that your code is going to be running, and it’s going to work at the time that it gets deployed.

Because if you’re in development mode, or if you deploy this, you’re going to get a failure. You have a little bit more confidence to try bolder and bigger things. I’ve only been using TypeScript for the past, really full-time, six months, really off and on the last year. I love it. I love the fact that I know that if I’m going to write some bad code, I’ll find out before I get to production. You’re always going to have bad code no matter what, but it actually removes some of those undefineds and those common edge cases which you hit whenever you write this normal JavaScript.

The Zod Library

I also want to bring in just a quick context around this library called Zod. Zod is a library to introduce some standard objects or standard types into your code base. That’s what the import z, you’ll see that in some of the other code that I have. What I’m getting at is that it’s inferring.

If you have an object that looks like this, it’s going to infer that, so you don’t actually have to handwrite this every single time. You can just wrap this on object.z.object around your object itself, and it will just confirm the type for getting it back. There’s a lot of type inferences. This is what really gives us the end-to-end type safety, is through Zod as well. TypeScript is not always easy to work with. If you’re not using something like Zod, and you’re building your own interfaces by hand all the time, or you may be consuming a third-party API where you don’t know what to expect, or you’re working with the standard JavaScript library that don’t have types built in.

Things like local storage is something I’ve run across. There’s a lot of edge cases you get to leverage. My recommendation is use something like Zod so that way you can infer the types, you don’t have to fight TypeScript, or fight the compiler the entire time.

What is tRPC?

tRPC allows you to easily build and consume type safe APIs. You don’t have to write a schema before starting to write code, or you don’t have to create code generators or resolvers. It’s all built in by default. tRPC is making it easier for you to write just JavaScript code and infer the types in the TypeScript along with them. Schema-less API development is the bread and butter of tRPC. It’s how I wrote JavaScript back in 2014, when I first was writing server-side JavaScript. You just write JavaScript in the backend, write JavaScript in the frontend, and you’re good to go.

This is an example of a CRUD request. Basically, this is a post request, adding a mutation under the router. Here I’m wrapping the Zod object, and then shipping that to the frontend. I’ll show you how to ship that to the frontend. This is a GIF taken directly from trpc.io, so it’s actually an example above the fold. Here they’re changing the attribute in the backend. That attribute is getting a validation right away, telling you that the name itself, undefined, it’s going to get updated in the frontend.

I’ll run that one more time so we can see that because it went faster than I can explain, so, msg, change to name, msg, now input that name. This is my frontend client code, changing that to name, everything compiles. It’s good to go. This is a VS Code experience right here. In VS Code, they’re going to infer all the types and give you autocompletion as well. The developer experience is amazing.

tRPC vs. REST

Now that I gave you the quick intro into tRPC, the benefit really is that you get a very clean end-to-end experience where you’re writing JavaScript for the backend and the server, JavaScript for the frontend, and you’re consuming that just as you would consume any other JavaScript library. Everything’s all in-house. tRPC vs. REST, this is a question that comes up a lot. Me personally, I know how to build REST APIs. Why wouldn’t I just use that until the end? Really, it just comes down to this. REST is the most common. One of the benefits right now is most common, most familiar. It’s been around since I think 1995. It came out in 1995. Been around for a while, tried and true. A couple cons is, not a standard, not a style.

A lot of what you get in CRUD actions, it’s been spec’d out in REST, a lot of people don’t use that. What we end up using is we use a lot of libraries and addons that are needed. Things like Swagger, that enforces a standard and a style. There’s also a bunch of other standards and styles that come through that, like JSON being the other one. You hope everyone is using the same thing, for the most part, at least in the JavaScript world I live in. A lot of the folks I work with, we all use the same stuff, so it’s helpful. Once you move outside that realm, or outside that world, or into a new company, you’re not guaranteed everyone’s writing APIs the same way. That means that you have different standards between the companies you work for.

tRPC vs. GraphQL

tRPC vs. GraphQL. I’m not going to break down GraphQL line by line because this is really focused around tRPC. Same deal. GraphQL does give you a standard and a spec out of the gate. Facebook, I think did really good when they offered GraphQL back in 2016, is that they said this is a standard, this is a spec. Everyone built their libraries and their interactions and their frameworks around that standard. The other thing is that it’s a strong community. The folks who have chosen to use GraphQL and start building on GraphQL pretty early, including GitHub, they are now in touch with each other and they’re building a strong community around GraphQL and GraphQL’s tooling. I highly recommend checking out the Guild and what they’ve been building.

Oracle is another good consumer of GraphQL. Apollo, another great one as well. The cons end up being the syntax. It is a learning curve for understanding the syntax of GraphQL. A lot of it is expectation of you just start typing and autocomplete your way into using the query-based language. That could be detrimental for folks who aren’t familiar with GraphQL, or maybe someone’s naming other things differently, maybe they structure their data.

GitHub was an early adopter of GraphQL, so everything has nodes inside of edges inside of nodes. It becomes very challenging to know how to get the data you want, at least the first couple times. Eventually, you get it, you get used to it. There’s a lot of blog posts and a lot of Stack Overflow questions around the implementation of GitHub’s GraphQL API, because it’s a learning curve at the end of the day. The other thing, it’s all post requests, because GraphQL has one endpoint, and they use parsed in queries into that endpoint.

The challenge is post requests, which means error handling is quite different. If you ever have to handle errors in GraphQL, there’s a lot to be desired. There’s a lot of tools out there that handle this for you, so you have to include the ecosystem of tooling along for the ride. For the most part, GraphQL is great. It’s a great solution. I love it. When it comes to tRPC, tRPC stands above GraphQL quite a bit.

tRPC vs. gRPC

The biggest difference is that gRPC ships a binary. With tRPC, it ships JSON. JSON, for me, it’s pretty easy to work with. JSON is pretty familiar for me. I don’t know how to manage that binary. Usually, with this binary, you have to use extra tooling as well. There’s another learning curve for managing gRPC. For the most part, it’s a fast way to consume APIs.

The Benefits of tRPC

tRPC, end-to-end type safety. You saw on the backend, I can write a function. I could match that function and the endpoint and the attributes on the client. Expectation that tight connection between the server and client exists in a way that hasn’t existed at least a while for me, when I’ve been working with REST and GraphQL. Some downsides is, note only, for the most part, you’re going to expect to write JavaScript.

There’s not really other languages that you’re going to be involving. It’s TypeScript base as well. It’s in the name. It’s also very much early adoption. This is part of the reason why I didn’t actually end up leveraging this for the thing I just built. Because very early, so most of the stuff you’ll have to figure out yourself.

At this point, I’m currently trying to build a project base with users. I don’t have the time to basically build tooling on top of the project that I’m building. I’m looking to benefit out of a community ecosystem, which doesn’t mean I won’t end up using tRPC in the future, it just meant the last project I built, we did not use tRPC for. Also, early adoption. The developers that are working on it currently are mostly React devs. Most examples are React.

There are some Vue examples or some other examples from other frameworks, in like React Native. I mention that because if you’re going to get involved in tRPC, understand it’s early adopter ecosystem, and expect that you’ll have to solve some problems, and be expected to probably write some blog posts, or libraries to help support the ecosystem itself. Not for everybody. Not for the faint of heart. Definitely, if you want to basically make a name for yourself in the API movement, definitely check it out.

tRPC Setup

tRPC, the setup. The difference is that instead of consuming through GraphQL, or through a REST API, you’re going to be consuming the tRPC server consuming through function call. Importing the function into the client directly, and you’re just clearly calling function calls in the client. Still going to be a server-client setup, the difference is now the setup all lives in the same ecosystem, the same folder structure. There we go. tRPC server, we got the client, that’s the biggest difference.

Blog Example

I want to walk through building a blog. I won’t build a blog from scratch. I’m just going to walk through some code that exists. This example code came directly from the docs from trpc.io. Definitely, you can get a closer look, check it out. This is the blog example from the docs. I’ll show you how this is set up. This is my blog example. We’ve got the server here, and I’m going to walk through some of this server code. It is quite a bit of code but I’ll break this down for us. tRPC comes out of the box. The reason I’m not really showing the setup is most setup actually gets done through generation.

You’re not expected to actually go and install all these different pieces like the Prisma client, or Zod, or the router itself. All this stuff will actually be built for you through things like create-t3-app. My recommendation is, use the tooling. Don’t try to build this stuff from scratch, unless you’re trying to build a workshop or something like that.

Right off the bat, we have Prisma. Prisma itself, it’s become the default ORM for JavaScript devs. It’s pretty popular. Prisma is going to be your TypeScript ORM. There are other solutions like SQLite, or TypeORM. Prisma is the one that’s been chosen by the tRPC community. Here you’re going to be able to get end-to-end validation for your types.

Prisma also has type safety built in as well. Prisma has a validator, similar to what Zod was doing for the validation of your types. Prisma is doing this right here with the Prisma.PostSelect right there. Then, this defaultPostSelect, this is what gets reused in multiple places. In your client, you have a defaultPostSelect. What’s specifically named in the server, that’s what it’s going to be called on the frontend. tRPC wraps Prisma for database interactions.

Adding records to the database, removing records, updating records, all that gets wrapped in Prisma. You don’t have to hand wire that yourself. You end up passing that a database URL. Here’s some basic CRUD operations. Here we’ve got mutations. We’ve got the router that we have established on the line one. We have a mutation where we add the flag or the string add. That’s just distinguishing that this is what it’s going to be named. We’re going to add the input itself, which will be wrapping the Zod object. We’re heavily inferring the TypeScript type to get safety on the server so that when we call it on the frontend, we’ll get safety as well on the frontend.

Here’s our resolver here, and this resolver is the interaction for the asynchronous request for creating the post. This is a post operation for creating a record on the blog. The other thing I want to point out is that I just collapsed a mutation to make it fit on the screen. Then now we have our read, fetch all data from there. Because we’re using Prisma, Prisma is our ORM for interacting with the database, we’re just going to find all. We’re going to find all the posts inside the database. I would go through the rest of them. The update is going to look very much like the add. The delete is going to look very much like the add except it’s removing the record. It’s just another interaction for Prisma.

This is what our frontend looks like. We’ve moved ourselves from the backend to the server to move to the frontend. The frontend and the client itself, is going to call an add. This is the actual add post function that you would add into your React client. It’s pretty straightforward. There’s not really much up here, other than the fact that you maybe put this in the onclick handler inside a button, or onsubmit, and that’s going to add a post inside the form. This is our read operation. This is a little more showing some of the React code.

Here we have our post query. We’re just calling a function directly from the server. We’re importing this from the utilities. This utility function is called useQuery. useQuery is going to fetch using the post all function that we’ve described in our server, it’s going to fetch all the data. Then, that data is going to be rendered on the page using React. I want to point out that this is actually just React Query. tRPC is wrapping React Query internally, and that’s where the useQueries come from. The majority of the ecosystem is currently mostly React, so that’s what’s being built under the hood. React Query is not React specific, it just happens to be heavily leveraged in the React community and built for a React app.

If you wanted to use React Query outside of React, I believe it’s possible. There’s a lot of inferred React stuff in there as well, performing a powerful data synchronization. We can now take our post query in only one line. Then we can render that in a map, so postsQuery.data, all those items will be rendered. Every post will be rendered on the page using React. Pretty straightforward. There’s not really much more past this. We will go into more details on how tRPC is built under the hood. That’s it. That’s how we get our posts added to the page. That’s how we get the posts rendered on the page. This is all within the same monorepo between the server and the client.

tRPC Router

Going back to what I was mentioning before, we have the tRPC setup, it’s all built around this tRPC router. The router itself is what’s doing the work, the glue between the peanut butter and the jelly and making your full stack type safe API in application. This is the setup for setting up the tRPC server and then creating the router itself. It is specifically looking for that context, context, in the context of React apps.

This is our global datastore. That’s what’s being attached to the router itself. By doing it this way, and by using the tRPC router and the tRPC server and the tRPC client, you get things like caching, JSON distribution, and all that magic between the frontend and the backend to work out of the box. If you haven’t got it already, tRPC is not meant to have a lot of boilerplate setup. You’re going to get most of your tRPC setup when you start from either an example, or use a generator. For the most part, you should be able to start with tRPC, write your backend code, write your frontend code and then deploy. That’s the hope.

This is your router here. This is in our setup for our server. We’re creating our server, createRouter. There’s some other stuff under the hood that we’ve just shown with the context being attached to the router, and then adding the router into your server itself, and then adding the mutation as functions, and then also adding the reads and the updates and the deletes. They will all get attached as a function. So far, I had not had to do any promise handling, had not had to do any JSON handling.

The benefit of this is that it just works out of the box. Even though you’re creating endpoints, you’re just creating like this mutation add. It technically is an endpoint, but it’s really just a function. The beauty of this is that you’re building an API without actually not even knowing you’re building an API. Again, the beauty of this is like, everything’s put together in a nice sandwich, which is our full stack application.

Postgres Database?

The one other thing I’d mention is the Postgres database, not only can you get the validation, the types, the safety, the validation from your database all the way to your client, you can also set this up in SQLite, Postgres, just patch in your database URL directly in the schema.prisma, and there you go. You’re not hand building any schema for your project, it’s all getting generated for you, thanks to Prisma. tRPC works hand in hand with things like Prisma. It also works hand in hand with things like React. All that is being inferred. We’re very early stage when it comes to tRPC, so the assumption is that you’re going to be doing React applications.

Benefits

Some things that I didn’t have time to basically go through are things like batch queries. Imagine you want to fetch ID 1, 2, and 3, you can actually batch those queries, so that way, they’re all in one network request. This is actually how you would batch those queries, just by wrapping it into one promise. tRPC knows how to handle that, and it will batch that query in one request. Not only do you get caching for free, you also can batch queries for free as well. In addition to that, I’d mentioned in passing, you’re going to have your best experience. tRPC is going to work best with the Next adapter. Next.js is basically a framework to build APIs that connect to your frontend. That’s not how they sell it. It’s Next level web applications. One of the best features of Next.js is that you can actually build an API pretty quickly in Next.js. I’ve actually used Next.js.

The original open source API was built on top of Next.js, and it had no frontend. It was actually rendering JSON only. The beauty of that is that because they are a file-based system, but also the resolvers that are built are serverless functions built in Next.js, it made it easy to generate an API pretty quickly. I don’t think it’s actually the best use case for Next.js, but I love it. tRPC in Next.js, they work very well together. If you want to try tRPC today, probably reach for building a quick prototype in Next.js, and see if you like it. React is going to be a pretty good experience as well.

Outside of that, you’re going to probably have to build some tooling or join a community somewhere to figure out some edge cases based on the project you’re working with. Just want to be upfront with that. Again, still early adoption. Make sure you know what you’re getting into if you’re going to be shipping this stuff to production.

t3-app

tRPC recommendations. They recommend out of the box, use an example app for setup. Start with the boilerplate that’s already set up, so you don’t have to do all the wiring of Prisma and everything else. My recommendation is, use t3-app. t3-app is actually a CLI to generate your tRPC application. It’s not tRPC specific, but it’s a Next application that adds in some extra features that are not included with Next. It’s an addon on top of Next.js. The way to get started, the barrier is very low, npx create-t3-app. It will actually prompt you to make some decisions about your application. It’s really focused on simplicity and modular approach to building full stack applications. It includes Next.js, tRPC, Tailwind, TypeScript, Prisma, and nextAuth.

This is what it looks like when you first run create-t3-app. It will ask you what options you want to include. I mostly include tRPC. Actually, I include all these except nextAuth. I end up using other things for authentication. For the most part, out of the box, it’s a pretty good experience. Which gets me to the point, this is all you need to know about t3-app, because everything I’ve shown you before you get out of the box from a t3-app. You don’t get the blog post stuff, but you do get all the connections, all the setup, so you don’t have to worry about things like the router. It’s good to know that the router exists in case you needed to debug or migrate to future versions.

This really loops you back into landing this plane, is that t3-app, it is the framework. It’s that system. It’s literally what the greatest developers of all time, when you think about building apps over again, are being really good at seeing situations and getting yourself out of bad situations, in the context of bugs and trying to ship features. You want to put yourself in a framework, in a system that no developer has to think too hard about how to connect their backend to their frontend.

No developer should think too hard about end-to-end testing, or about type safety. I honestly think that t3-app is going to be the future of how we build applications. With like Rails and Django giving a lot out of the box, I think that was the way the developer experience should be all the time. I think as we’ve developed our React ecosystem, the JavaScript ecosystem, TypeScript, we’ve leveled up to the point where now we can see more of these frameworks get shipped. Whether t3-app is your solution, or something similar, I do recommend starting with a framework, could be able to start net new. Because otherwise, you’re going to be building your own framework from scratch. My recommendation is, use t3-app for setup, if you want to mess around tRPC.

Limitations

I do also want to mention some limitations specifically for tRPC. Mostly React, Next.js examples, that’s what you’re going to find a majority. You’re going to find a few Vue, React Native examples using tRPC. These are cutting edge folks who decided they want to use tRPC for whatever situation. This is how ecosystems, how frameworks build on top of each other is that folks, they take a chance, they build something.

If you don’t have a solution for tRPC, I highly recommend, go ahead and build it, if you have the bandwidth. At least we didn’t have the bandwidth when we built the last app, we might have in the future. I do see this ecosystem building and growing. They do have a Discord. They do have an active community. I do see quite a few blog posts on tRPC coming out as of late, so definitely worth checking out and trying for sure.

The other thing is, I tweeted out in preparation for this talk, just looking for, what was the de facto onboarding experience tutorial for building the tRPC ready app? I actually didn’t get a lot of feedback. I didn’t get a lot of replies to my tweet. I did find a dev2 post about building a Discord chat application using tRPC. That was worth checking out. Shoubhit Dash, definitely check out their post. I found that pretty enlightening. The other thing, I would also mention a limitation for tRPC, which is no longer a limitation anymore, is that up until a couple months ago, it wasn’t really good for use for public APIs.

This is the reason why open source, why I personally decided not to pursue tRPC is because it was not available to build public APIs on. We really wanted to build a public API for people to consume. tRPC OpenAPI, which is also Swagger, was shipped. It’s still early adoption. It’s something I’ll be playing around with in the future and building, at least the future of open source tooling with. Might just be side projects for now. You’re going to find a lot of content and context around not building public APIs with tRPC, now you can. The beauty is that it’s open source. It could definitely use contribution.

The Best Framework

I feel like I’m in my last dance right now. Just recently started a company running as a founder. I’m actually doing a lot of tooling and coding myself. I’m doing a lot of coding myself, but we’re not doing it in production. I’m building a lot of side projects, testing out things like tRPC for fun. I get to see my team grow around me and build up whatever the future of the product that I’m working on.

Similar to all engineers, you go to senior, to principal, to distinguished, or whatever the latter is, and you want to find ways for people to plug into the system. In the event that you go move on to another company, or you go to FAANG or whatever it is, you now have an opportunity for people to slide into the mix, and not have to relearn all the best practices. Because at the end of the day, like though Michael Jordan was the greatest player of all time, the greatest player from the Bulls was Steve Kerr. He played with Jordan during The Last Dance, during his last season on the Bulls.

Steve Kerr has won nine rings. Steve Kerr won nine rings with the Warriors and the Bulls, but he was able to take that system and apply that same framework into the Golden State Warriors, where no longer you’re working towards Area 31. Actually, they’re shooting three-point shots, and all sort of stuff. What I’m getting at is, it’s all about the system. It’s about the framework. That’s where legends live on.

Why OpenSauced Utilized tRPC over GraphQL

Benjamin Dunphy: You evaluated tRPC for use in your own startup, OpenSauced. You mentioned that you ended up going with GraphQL after evaluating tRPC versus GraphQL. You’ve since told me that your engineering team convinced you to use tRPC over GraphQL. Can you tell me about why and how they convinced you that tRPC was right for you?

Douglas: My exploration started in June, around tRPC. tRPC just actually released version 10. A lot has happened, since me working on this talk and exploring this for the company project. I just didn’t have all the information. I’d say, even there was a question around streaming support. That was more of a conversation earlier this year. That also has launched as well. tRPC is advancing pretty quickly. There’s a lot of Band-Aids on the bleeding edge. I think where I’ve been on the bleeding edge for a lot of stuff, I was just a little more reserved on jumping in production on something that’s dependent on a couple thousand users.

GraphQL is something I’ve been using on open source since 2016 as a side project, now it’s a full-time project. It’s a little more comfortable, familiar for myself. The summary that I probably should have ended off is, tRPC is really good for small up and coming teams that need to move really quickly. GraphQL is for teams that you’re going to have maybe more engineers, and maybe like a separation between the backend and frontend, who owns it. Also, our biggest push was a public API, and tRPC then wasn’t really set up yet for a public API usage. If you ever get internal, closed, private API, it’s a better use case today. Hundred percent will change in the next six months to a year. For today, if you’re shipping something in production, consider GraphQL for a public API. Small, fast, up and coming startups, tRPC.

Will tRPC Supersede GraphQL?

Dunphy: That’s a very interesting conclusion, because GraphQL has been trending lately and not in a good way. Many people are seeing tRPC as its just overall successor. Not exactly in the way that you put it, where you said that smaller teams, tRPC, move fast. Larger teams, if you got a dedicated team, integrate GraphQL. Given your extensive experience with GraphQL, what are your thoughts on this? Do you think that GraphQL is dead? Do you think that tRPC will have all of the features required to supersede it?

Douglas: I don’t think GraphQL is anywhere close to being dead. When I was doing GraphQL like, even four years ago, everyone was waiting for more tooling, like Apollo has Federation. That used to be schema stitching back when I was trying to stitch different schemas together. The advancement of the tools have now picked up, to now you’re seeing more of like a maturity in the GraphQL ecosystem. When you see how quickly tRPC is now, like WebSocket streaming support is only just an idea and an issue, now it’s shipped to production pretty quickly. It’s just the age-old adage, once you get big enough, it’s a lot harder to move a ship to a certain direction. When you have a small team working on something, which is the tRPC team, you can move a bit quicker, especially now there’s a lot of attention and excitement in it.

GraphQL for Public APIs vs. tRPC for Private APIs

Dunphy: What about your recommendation for using GraphQL for public APIs, and tRPC for private APIs? Do you see this changing any time soon? Anything on the horizon, on the roadmap of tRPC that would change your mind on this conclusion?

Douglas: I even tried the OpenAPI stuff. It’s still pretty early. If I had to use tRPC, I’d probably just pick up REST, because just the combinations is not working with the GraphQL side. This is also just a limitation of me. My prediction is, there will be a public API use case for tRPC in the future, because there’s a lot of conversation around that discussion. All the bit of GraphQL is still there.

The learning curve for setting up the server side and creating GraphQL’s API, there’s a learning curve there as far as tooling, but consuming as a frontend developer or a mobile developer using GraphQL, the learning curve is actually not as steep. There’s a lot of context around there. When I was talking about Vue, and how there’s opportunity for people to create tRPC in example apps, in different languages and frameworks besides Next.js, that’s a really good opportunity.

The magic of tRPC is like, you start with create-t3-app, you have an app, you’re ready to go, and you can ship to production on Vercel, or Netlify pretty quickly. That magic, it also abstracts a lot of that setup. If you started from scratch, there’s a lot of setup and connecting different tools, which is why that my recommendation and tRPC’s recommendation is use the CLI tool instead. It just takes you away from hands in the code of building the library itself. It’s a balance. It’s a push and pull. Do you want full control on what you’re building? Yes, GraphQL gives you that. tRPC will get you to ship pretty quickly and make a lot of decisions for you.

Brownfield Development with tRPC

Dunphy: What are your thoughts on brownfield development with tRPC? Let’s assume for this question, the brownfield in question is REST APIs.

Douglas: I wouldn’t recommend it. tRPC is probably going to do the best if you start with tRPC. It is the same thing if you were having this conversation back in 2016 when GraphQL was brand new. It’s going to be a better experience when you start from scratch, because there’s a lot of tooling that doesn’t exist for existing applications adopting tRPC. If you’re using something like the Jamstack methodology, like you can’t start server first, build that separately, and then plug and play. That is a lot of tooling and a lot of education.

It’s like a rabbit hole you have to go down. If you have some antipatterns in how you’re consuming API code, then you have to also rewrite code. It’s going to be a heavy lift. My recommendation is, probably start out with a new, smaller project for your team, maybe like an internal tool. Start with that with tRPC so that way you can learn the pattern, and then having something to have a reference, then you’ll be able to adopt that in a brownfield app. Again, you’ll probably be writing a lot of blog posts and doing a lot of community interaction in conference talks, adding tRPC to a brownfield application, because you’ll be one of the first.

tRPC Support in Microservices

Dunphy: Can you talk about tRPC support in the world of microservices, where the frontend will be using multiple instances of backend services?

Douglas: tRPC provides a strong, tightly coupled situation from your backend and frontend. If you’re doing microservices, you have to have a strong connection between the frontend and the backend. That backend having multiple clients, that’s something that tRPC is not set up well to do. If you’re going to approach tRPC, you have the frontend and the backend, they talk to each other, there’s no more talking to another application. Which is why I would reach for GraphQL instead, or just build a generic REST API instead.

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Should You Add Mongodb Inc (MDB) Stock to Your Portfolio Tuesday? – InvestorsObserver

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Tuesday, August 08, 2023 12:39 PM | InvestorsObserver Analysts

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Should You Add Mongodb Inc (MDB) Stock to Your Portfolio Tuesday?

Mongodb Inc (MDB) is around the top of the Technology sector according to InvestorsObserver.

MDB received an overall rating of 89, which means that it scores higher than 89% of stocks. Additionally, Mongodb Inc scored a 82 in the Technology sector, ranking it higher than 82% of stocks in that sector.

Overall Score - 89
MDB has an Overall Score of 89. Find out what this means to you and get the rest of the rankings on MDB!

What do These Ratings Mean?

Searching for the best stocks to invest in can be difficult. There are thousands of options and it can be confusing on what actually constitutes a great value. *Investors Observer* allows you to choose from eight unique metrics to view the top industries and the best performing stocks in that industry. A score of 89 would rank higher than 89 percent of all stocks.

This ranking system incorporates numerous factors used by analysts to compare stocks in greater detail. This allows you to find the best stocks available in the technology sector with relative ease.

These percentile-ranked scores using both fundamental and technical analysis give investors an easy way to view the attractiveness of specific stocks. Stocks with the highest scores have the best evaluations by analysts working on Wall Street.

What’s Happening With Mongodb Inc Stock Today?

Mongodb Inc (MDB) stock is trading at $371.69 as of 12:30 PM on Tuesday, Aug 8, a loss of -$25.86, or -6.51% from the previous closing price of $397.55. The stock has traded between $369.01 and $388.50 so far today. Volume today is 1,432,958 compared to average volume of 1,430,030.

Click Here to get the full Stock Report for Mongodb Inc stock.

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