Month: June 2023

Posted on mongodb google news. Visit mongodb google news

Over in Vienna for the Temenos Community Forum 2023, we caught up with Boris Bialek, Managing Director for Industry Solutions at MongoDB. MongoDB is a database vendor that aims to empower innovators to create, transform and disrupt industries by unleashing the power of software and data.

“Today, there’s a lot of change. Historically, data was more in the classic area meaning the transaction, the account & the client. Today, it’s not the question of ‘What’s my client’s birth date?’ it’s ‘Who is my client? What is their true desire? What is their wish? What is their demand? And how can I do this in real-time?’”

“It’s not good enough anymore to say ‘Oh yeah, you get your transaction done, just wait for it for 5 days.’ No, the transaction has to happen immediately, has to be secure and has to be transparent to the client in the form of fees.”

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

Article originally posted on InfoQ. Visit InfoQ

At the end of last month, the Avalonia UI team made an announcement regarding the anticipated version 11. The team revealed the availability of the first Release Candidate (RC) for this significant release. In their official release post, they emphasized the significance of this milestone, as it signifies a crucial step forward for the project. The primary focus of this RC is to stabilize the API, bringing the final release of v11 even closer to completion.

Avalonia UI is an open-source and cross-platform UI framework for .NET developers, designed to facilitate the development of desktop applications that can run on Windows, macOS, Linux, iOS, Android, and WebAssembly. Correspondingly, last month, InfoQ also interviewed Mike James, the current CEO of the company, which readers can read and get more insights into the Avalonia UI platform.

Avalonia UI v11 places a keen focus on maintainability and incorporates the most substantial changes to the API in its history, ensuring a promising future for the project. As stated in the release post, extensive thought, time, and resources have been invested in guaranteeing that changes pave the way for a more robust and reliable Avalonia UI. While this release introduces the most significant breaking changes thus far, the team behind the Avalonia UI anticipates that future major releases will include far fewer alterations to the API.

The official blog post of the release, states the following:

This RC marks a significant milestone for us as it stabilises the API. It’s been a journey of intense work, learning, and collaboration. And now, we can confidently say that we’re closer than ever to the final release of v11

As part of these breaking changes, certain previously public classes have been transitioned to an ‘Internal’ designation. The adjustment has been made in order to protect the integrity of applications by allowing for modifications to implementations without introducing future breaking changes. Although some measures have been taken to ensure a smooth transition, it is acknowledged that there may be instances where critical classes are no longer visible. In such cases, developers are strongly encouraged to contact the Avalonia UI team for assistance. They are open to a dialogue regarding the possibility of restoring public visibility to certain classes based on valuable user feedback.

In addition to this release of the Avalonia UI v11 Release Candidate, developers and users alike can find a lot of community feedback and valuable insights on the project’s GitHub discussion page. Developers shared their experiences, offered suggestions, and seek assistance with migration and troubleshooting related to the RC release. To gain a wide understanding of the various perspectives surrounding the Avalonia UI v11 RC release, it is highly recommended for users dive into the comment section on the mentioned GitHub discussion page.

Besides the GitHub discussion page, the GitHub wiki page is called Avalonia 11 Porting Guide. This porting guide provides detailed instructions and recommendations on adapting existing codebases to leverage the new features and improvements introduced in version 11. It covers various aspects of the migration process, including breaking changes, API modifications, and best practices for a smooth transition.

According to a porting guide following changes are included, the framework has removed its dependency on System.Reactive. For developers who are utilizing reactive features, it is necessary to include a package reference to System.Reactive in your project. Also, the IStyleable interface has been deprecated, developers can read more about this on the related GitHub issue. Additionally, view in the form of .axaml/.axaml.cs (or .xaml/.xaml.cs) pairs now come with automatically generated C# code.

Furthermore, ItemsControl and its derived classes such as ListBox and ComboBox now possess both an Items property and an ItemsSource, similar to the WPF/UWP framework. Moreover, the IAssetLoader interface is no longer available, and it is advised to use the static AssetLoader class instead. Also, the virtual AvaloniaObject.OnPropertyChanged method has been modified to be non-generic.

Other notable changes are included with building elements like events, layout, focus, visual tree, rendering and many more, so it is highly recommended to take a look at the already mentioned Avalonia 11 Porting Guide.

The Avalonia UI team extends their invitation to all users to try out the RC, provide feedback, and contribute to making Avalonia UI v11 the best it can be. They express sincere gratitude to their users, contributors, and the entire Avalonia community for their continuous support.

Lastly, to get started with Avalonia UI v11 RC, users can download the Nuget package and also interested readers can learn more about Avalonia UI and get started exploring the official documentation and main Avalonia GitHub repository.

Article originally posted on InfoQ. Visit InfoQ

In recent years, there has been increased attention to neurodivergencies such as ADHD, hyper-sensitivity, autism, dyslexia, etc. More people seem to self-identify as being neurodivergent, and companies are starting to pay attention to becoming more neuro-inclusive and fostering the strengths of neurodiversity.

In this article, I’m telling my own story with ADHD while working as a software developer and becoming a team lead, what I learned, and what I found to be working well to help people with ADHD and more to thrive in their teams and companies.

My story about getting into software development with ADHD

From a very young age, I was attracted to computers and electronics, and when my parents bought me a computer at the age of 12, I started fiddling around with it, installing Linux and breaking the thing once in a while. Once we had a broadband internet connection at home, I pretty quickly started building small websites and web applications, mostly using PHP. This was around 2002 or 2003. At the age of 17, while in high school, I was running a free Open Source software project alongside my studies.

Back then I didn’t know I had ADHD – I learned that by coincidence when I was 27. Only in later years did I understand that my ADHD traits absolutely helped me get into the world of software development and run software projects end-to-end. The web was not that new in 2003, but the world of building applications in PHP was, and all the free PHP forum packages and CMS’es were so cool to fiddle with.

I went on to study software engineering and computer science and started working in several companies building web applications professionally. My ADHD was not much of a downside, except for needing to spend extra energy to stay focused when things got boring or repetitive. I think the biggest challenges were more around the social aspects. As a neurodivergent person, it can be trickier to fit in with a group, and there were (and still are) stigmas around ADHD. After disclosing my diagnosis, someone at work stated it couldn’t be true I have ADHD, as I was not jumping around all the time and was rather timid. That’s not something you want to hear, especially after you find the courage to talk about it at work.

Recurring challenges for people with ADHD

Let’s zoom in on ADHD and the challenges it may bring. Depending on the person, a variety of challenges can manifest themselves.

The most common and well-known challenge is having difficulties with staying concentrated and focused. In general, we could say that our minds are not naturally wired to focus on the most important task, but rather we are frequently on the lookout for a “dopamine hit”.

Research found that the dopamine levels in the brains of people with ADHD are lower than in other people’s brains. That’s unfortunate, as dopamine helps us to anticipate the benefits and rewards of long-term work. It motivates us to carry on and somehow stay focused. Hence, people with ADHD may be more drawn to seek out short-term rewards, get attracted to novelties, act more impulsively, and have difficulties starting or finishing up tasks and projects.

Another common trait is that many people with ADHD lack an input filter. This means that in a busy environment like an open space office, someone with ADHD tends to consciously hear every sound, conversation, noise, etc. The loud keyboard of a colleague three desks away, the conversation going on at the other end of the room, the noise of people passing by, traffic passing outside the building, etc. This “lack of input filter” of course does not help with staying focused and motivated on a task.

Over time while growing up, going to school or university, working in different environments, etc, it’s likely people with ADHD (and other neurodivergencies) build up negative experiences or some levels of trauma. These negative emotions of failure, not being “good enough”, being afraid of letting down people, and the like can form an additional challenge. We can call this a “wall of awful”, coined by ADHD expert Brendan Mahan and explained in the YouTube show “How to ADHD”. Needing to start some task, it feels like having to overcome those negative experiences, just like climbing a wall, before you actually get to work.

Strengths linked to ADHD and their value for organisations

As much as some traits make life more challenging, they can also be regarded as or turned into strengths.

I believe seeking out novelties makes many people with ADHD excellent problem solvers, thinking out of the box, and not afraid to experiment. In “Building a Neurodiverse High-tech Workforce” by Eleanor T. Loiacono and Huimin Ren, it is confirmed that people with ADHD score significantly higher on creativity tests than others and that this “outside the box” thinking is a benefit in the competitive environment of the high-tech industry.

This is a trait we tend to severely undervalue within organisations, unfortunately. Many people – and hence organisations – are risk averse, preferring to stick to the status quo rather than experimenting with higher uncertainty even if this might yield commercially more interesting results. Next to that, there seems to still be an over-focus on hard skills and short-term thinking, as opposed to soft skills and systems thinking.

By allowing people with ADHD to put their creativity to use, these “natural innovators” can seek out process or product improvements, discover market opportunities, or be more naturally suited to lead short-term projects. I’m thinking of short-term client work or consulting, organising company events, running innovation or change initiatives, and the like.

An example of a company that sees the strengths of people with ADHD and other neurodivergencies is IBM, who run their “DiversAbility community” that focuses on “hiring, supporting, educating and embracing people of all abilities”.

Working on exciting projects, especially with a looming deadline, also allows many to reap the benefits of entering hyper-focus. In that state, someone with ADHD is able to stay highly concentrated for a while, being undisturbed for hours on end and creating an enormous amount of value. I know, it sounds like the opposite of ADHD.

For example, while working on community software in the evenings and weekends I could build an entire feature in a few hours. I also built an entire website for an event from scratch in a very short time span because I wanted to get the thing online and share it with everyone.

Towards neuro-inclusive workplaces: how to value and include neurodivergent people

Reducing the stigmas is absolutely the first step for building neurodiverse-friendly workplaces.

ADHD and other neurodevelopmental conditions like autism and dyslexia are very much still regarded as disorders. And with disorders come various stigmas, unfortunately, that are often still present in companies and HR departments. There is sometimes a certain degree of fear for the unknown. “It’ll take a lot of time and energy to have someone with ADHD or autism on the team”, or even “The benefits will not outweigh the costs!”- statements that are usually simply untrue.

I can also safely say that, with research (“Neurodiversity at work: a biopsychosocial model and the impact on working adults” by Nancy Doyle) estimating the total number of neurodivergent people in the world to 15-20%, except for in smaller companies you’ll likely already have (un)diagnosed neurodivergent people in your teams, even if you’re unaware of them.

So while I understand that ADHD, autism, and more are from a purely biological or psychological perspective defined as disorders, I and many others from the global neurodiversity movement believe these differences should be treated as a form of natural diversity, just as we have many other forms of diversity among humans, and organisations and society as a whole should become more open to the values everyone can bring to the table. This diversity makes us develop better products and services, and grow as societies.

After all, we should never forget that neurodivergent people are people like everyone else, with their individual needs, preferences, strengths, interests and expertise.

To reduce the stigmas, dare to ask questions and be curious over making assumptions about ADHD or other neurodivergencies. This applies during hiring as well as when managing people in the workplace. Also, don’t set your own or your organisation’s behaviour as the standard – here again, different behaviours and viewpoints can be valuable. In general, it helps to get (neuro)diversity awareness training from experts, and to set a positive example yourself.

As a next step, it’s a good idea to focus on building a more “people-first” culture and building transparent and psychologically safe environments. Psychological safety is good for creating learning environments with more innovation and creativity. Furthermore, research (“Exploring the Relationship between Team Diversity, Psychological Safety and Team Performance: Evidence from Pharmaceutical Drug Development” by Henrik Bresman and Amy C. Edmondson) has found that psychological safety specifically helps unlock the potential of diverse teams.

And for software engineering teams – with and without ADHD – I’ve found that four additional principles help tremendously:

• Making many small steps in daily work.
• Limiting the cognitive load (or “mental workload”) of the team and individuals.
• Reflecting and experimenting with the entire team.
• Mentoring in the broad sense.

This creates better software teams while creating a productive environment where people with ADHD can better show their potential.

Fostering psychological safety in neurodiverse teams

To build a safe environment with a neurodiverse group, I’ve found it starts with ensuring all voices are heard within the team. I remember more than once being in or cooperating with teams where the most confident or dominant people are speaking the most. Many tend to attribute expertise to the most extroverted, social and confident people in the room, while there is no correlation between both. Team members that have trouble speaking up (because of for example the “wall of awful” I mentioned before), who are more introverted or have difficulty putting their thoughts to words, may keep silent more often or frequently get interrupted. As a manager it’s also key to ensure and stress that sharing ideas, concerns, and critiques, and openly talking about personal struggles is good and welcomed, and does not put the person at risk for getting a negative performance review.

Composing meeting agendas and allowing people to prepare upfront, using “round tables” to let everyone contribute their proper viewpoints, listening to understand rather than to respond, and especially avoiding a blame culture is essential.

Changing an environment can be hard and take a lot of time, but it’s worthwhile. Start by listening to everyone’s needs without judgement, and ensure thoughts, critiques and feedback can be shared without people fearing for negative consequences.

Why and how to limit a software development team’s cognitive load

Many software teams tend to work on multiple features or tasks at the same time. Sometimes this is because of client demands, big batches of work which eventually get blocked in the system, managerial viewpoints on efficiency, or simply because the team prefers it (“we’ve always done it like this”). Yet working on many items in parallel comes with hidden costs, not in the least the often forgotten cost of context switching (for example because of people needing each other’s help, code reviews, etc). In Lean software development terms – introduced by the book “Lean Software Development: An Agile Toolkit” by Mary and Tom Poppendieck – we also call these “muda”. In some cases, with continuous struggles or failure to deliver the agreed scope in time, it can lead to circles of distrust with the company’s management. This is a situation to avoid at all costs, of course.

A better approach is to maintain a healthy balance between focusing as a team, and focusing as an individual. To succeed, the team needs to first be aware of the company/client’s goals and objectives, and the value and impact of the possible jobs to be done. There are plenty of approaches here. User research, OKRs, prioritisation techniques, user story mapping, dimensional planning, etc. The team needs to find out what to do and why, preferably in small steps, but also what not to do (or rather: not at this point). With a previous client for example, we performed cross-team user interviews with people from Product, Engineering, Sales and Marketing to understand the client’s needs better before defining and ultimately splitting into small, valuable and prioritised user stories.

Once we have identified the first step (or user story), let’s try to tackle and deliver it as a team while leveraging “one-piece flow”. That means the team is going to finish this and only this small step as a group without distractions (bar any incidents, of course), before continuing with the next step.

Working like this avoids or diminishes the costs of getting interrupted or distracted, especially if we can park outside requests for the duration of that task. We protect what is dear to us: our focus, and we’re able to more quickly deliver working solutions while learning from and helping each other. As long as we keep delivering value to our clients or stakeholders, the team and any people with ADHD therein get their “dopamine hit”. “We finished something valuable!” Plus, we avoid our brains staying occupied with multiple ongoing tasks.

An ensemble can continue to work like this for as long as possible or useful, staying focused on the next step of the project or initiative. At the same time it’s, of course, a constant conversation with everyone in and outside the team. It’s okay to return to individual work once in a while, or switch to other tasks if necessary. Introverted people, and those with ADHD, autism, and many others may also find being in a working group the entire day tiring, or need to be able to work within their comfort zone every few hours. It’s important to be mindful of this and listen to each other’s needs. The benefits of people’s well-being are more valuable than a limited amount of extra work (i.e. reviewing and some interruptions). In my case with a previous client, at the start of the day, we agreed as a group on which user story or task we absolutely wanted to finish that day. This improved team morale and the can-do mentality.

Finally, depending on the company and environment, looking into domain-driven design and team topologies can further help drive down cognitive load.

What being a team lead taught me about leadership … and my ADHD

When I was promoted to the role of a lead engineer for the first time, I was very much focused on the functional and technical side of building software and IT solutions. That worked fine in some companies and environments, but less so in more complex ones or with bigger teams.

Over the years to come, I learned by trying, failing, and trying again that the focus of leading teams is really having that people-first mentality. This may sound logical. “Yes of course, Dietrich, you’re leading people!” Yet time and time again I see many others struggling with the same problems I dealt with: keeping people motivated, resolving interpersonal issues, staying on the same page with senior management, etc. Even more so when aspects of neurodiversity are at play within a team.

These are skills that are not always taught before or after people get promoted to management positions. For instance, what I encountered appears to also be a common problem among technical start-up founders when they move to CTO roles focused on strategy and people. All of a sudden, the technical work you have been doing for years is no longer the most important thing. You’re no longer spending all your time writing code, drawing technical diagrams, or doing code reviews.

Having ADHD did not make things easier for me. The “wall of awful” is also at play here and makes you doubt whether you can coach people at all. Then ADHD tends to make you miss out on essential company politics and unwritten rules of the job, a recurring struggle for many neurodivergent people. For example, I tend to easily miss insights that are shared verbally during work lunches or company after-work drinks because I have trouble following conversations in busy environments. And finally, with my tendency to experiment and push for change where I found it was needed, I had to learn how to deal with corporate inertia and understand the importance of change management. Oftentimes, people or companies fear change and will naturally push back, while I need some amount of it to thrive.

I was lucky enough to come into contact with the principles and values of agility, interact with peers and mentors in that space, and to enrol in leadership workshops. Over time I built my own toolbox of principles and practices, including the ones I mentioned before. And most of all: that I could apply the same principles of agility and management in my personal life. For instance, to (re)gain the insight into what matters most to me, that I needed to focus on my own well-being more, and how to set and achieve mid and long-term goals. It’s still hard from time to time, and becoming independent has also taught me more new things in the past 2.5 years.

Wrapping up – five principles for neurodiverse software development teams

Both for the teams as a whole and on a personal level for neurodivergent members, to achieve higher levels of resilience and productivity, refer to the five principles I introduced earlier. These help build high-performing teams that excel in creativity and innovation.

• First and foremost, foster psychological safety within the team. Agree on some ground rules together. For example: “It’s safe to express feedback and concerns, and we do not judge each other for that”. Or: “Don’t dominate conversations, and don’t interrupt”. Treat everyone equally, and avoid toxic management at all costs.
• Build trust and tackle uncertainty and risk by working with many small steps.
• Protect focus and avoid high mental loads, both for the team as well as for individual members, especially if they are neurodivergent.
• Find your ideal approaches. We all know about retrospectives, especially with Scrum being so popular. Truly listen to one another (remember psychological safety), find issues or bottlenecks, experiment, and update your ways of working. This also applies individually. And don’t forget to celebrate what you achieved!
• And lastly, provide internal or external coaching or mentoring, especially when changes are made to the team: new people joining, people leaving, someone being promoted to a leadership position, etc. As a senior or engineering manager, focus on maximising transparency while helping the team to navigate any company politics.

In the end, it’s all about making an equal playing field for all, including minorities such as neurodivergent people.

Article originally posted on InfoQ. Visit InfoQ

Today’s modern data architecture stacks look significantly different from the data architecture models from only a few years ago.

Data streaming and stream processing have become the core components of modern data architecture. Real time data streams are being managed as first-class citizens in data processing analytics solutions. Some companies are even shifting their architecture and technology thinking from “everything’s at rest” to “everything’s in motion.”

Change data capture (CDC) has become a critical design pattern in data engineering use cases. CDC can be used in event-driven microservices based applications, along with data streaming to implement robust solutions.

The emphasis on data streams  is also driving innovations in the data governance space such as the stream catalog and stream lineage.

Data mesh architecture, which has been getting a lot of attention recently, is built on four solid principles: domain ownership, data as a product, self-serve data infrastructure platform, and federated governance. Data mesh is expected to have a huge impact on the overall data management programs and initiatives in organizations.

Similar to many compute services in the cloud platforms, data storage services and databases now support serverless models where you only pay for what you use.

On the security and regulatory compliance side, data residency and data sovereignty  are getting a lot of attention to ensure the consumers’ data is protected and privacy is maintained throughout the life of the data.

Next-generation data engineering innovations will build on these recent trends to provide even more robust, secure, highly available and resilient data solutions to the development community.

In the InfoQ “Data Engineering Innovations” eMag, you’ll find up-to-date case studies and real-world data engineering solutions from technology SME’s and leading data practitioners in the industry.

Free download

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Vontobel Holding Ltd. grew its stake in MongoDB, Inc. (NASDAQ:MDB – Get Rating) by 11.6% in the first quarter, Holdings Channel.com reports. The firm owned 11,059 shares of the company’s stock after acquiring an additional 1,146 shares during the quarter. Vontobel Holding Ltd.’s holdings in MongoDB were worth $2,578,000 at the end of the most recent reporting period.

A number of other large investors have also recently added to or reduced their stakes in MDB. Bessemer Group Inc. purchased a new stake in shares of MongoDB during the fourth quarter worth $29,000. BI Asset Management Fondsmaeglerselskab A S purchased a new position in shares of MongoDB during the fourth quarter worth about $30,000. Lindbrook Capital LLC grew its holdings in shares of MongoDB by 350.0% during the fourth quarter. Lindbrook Capital LLC now owns 171 shares of the company’s stock worth $34,000 after purchasing an additional 133 shares during the last quarter. Y.D. More Investments Ltd acquired a new stake in shares of MongoDB during the fourth quarter worth about $36,000. Finally, CI Investments Inc. lifted its position in MongoDB by 126.8% in the fourth quarter. CI Investments Inc. now owns 186 shares of the company’s stock valued at $37,000 after purchasing an additional 104 shares during the period. 89.22% of the stock is currently owned by institutional investors and hedge funds.

Insiders Place Their Bets

In other news, CAO Thomas Bull sold 605 shares of the stock in a transaction dated Monday, April 3rd. The shares were sold at an average price of $228.34, for a total value of $138,145.70. Following the transaction, the chief accounting officer now directly owns 17,706 shares of the company’s stock, valued at $4,042,988.04. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available through the SEC website. In related news, CAO Thomas Bull sold 605 shares of the firm’s stock in a transaction that occurred on Monday, April 3rd. The shares were sold at an average price of $228.34, for a total value of $138,145.70. Following the completion of the transaction, the chief accounting officer now owns 17,706 shares in the company, valued at approximately $4,042,988.04. The sale was disclosed in a document filed with the SEC, which can be accessed through this link. Also, CRO Cedric Pech sold 720 shares of MongoDB stock in a transaction on Monday, April 3rd. The shares were sold at an average price of $228.33, for a total transaction of $164,397.60. Following the completion of the sale, the executive now owns 53,050 shares of the company’s stock, valued at approximately $12,112,906.50. The disclosure for this sale can be found here. Over the last three months, insiders have sold 108,856 shares of company stock valued at $27,327,511. 4.80% of the stock is currently owned by corporate insiders.

MongoDB Stock Performance

Shares of MDB opened at $379.98 on Tuesday. MongoDB, Inc. has a 12 month low of $135.15 and a 12 month high of $398.89. The firm has a fifty day simple moving average of $298.74 and a 200 day simple moving average of $240.54. The stock has a market capitalization of $26.61 billion, a price-to-earnings ratio of -81.37 and a beta of 1.04. 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:MDB – Get Rating) last posted its quarterly earnings results on Thursday, June 1st. The company reported $0.56 earnings per share (EPS) for the quarter, topping the consensus estimate 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 firm had revenue of $368.28 million for the quarter, compared to analyst estimates of $347.77 million. During the same period in the prior year, the firm posted ($1.15) earnings per share. The company’s quarterly revenue was up 29.0% compared to the same quarter last year. Research analysts forecast that MongoDB, Inc. will post -2.85 EPS for the current fiscal year.

Wall Street Analysts Forecast Growth

A number of brokerages have recently weighed in on MDB. Tigress Financial reissued a “buy” rating and set a $365.00 price objective on shares of MongoDB in a research note on Thursday, April 20th. Citigroup raised their price objective on shares of MongoDB from $363.00 to $430.00 in a research note on Friday, June 2nd. JMP Securities lifted their target price on shares of MongoDB from $245.00 to $370.00 in a research note on Friday, June 2nd. Sanford C. Bernstein lifted their price target on shares of MongoDB from $257.00 to $424.00 in a report on Monday, June 5th. Finally, Credit Suisse Group decreased their price target on shares of MongoDB from $305.00 to $250.00 and set an “outperform” rating on the stock in a report on Friday, March 10th. One research analyst has rated the stock with a sell rating, three have issued a hold rating and twenty-one have given a buy rating to the stock. According to MarketBeat.com, MongoDB presently has an average rating of “Moderate Buy” and a consensus target price of $353.75.

About MongoDB

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

Posted on mongodb google news. Visit mongodb google news

Prior to the kickoff of MongoDB .local NYC on June 22, the document-oriented database provider MongoDB Inc. saw its stock soar with blowout fiscal first-quarter earnings results.

That posed some interesting trends to watch as the conference got underway in New York: How was artificial intelligence contributing to the market interest and stock prices? And how was MongoDB utilizing AI to secure a competitive advantage? 

Some of those questions were answered during the conference, including the company announcing it would beef up its cloud database MongoDB Atlas with a series of new products and features.

“That Atlas Vector Search is really interesting, because it’s an alternative to having a separate standalone vector database,” industry analyst Dave Vellante said on a recent episode of theCUBE podcast. “Obviously that is something that is going to be interesting for generative AI.”

Insights during MongoDB .local NYC were provided by industry analyst John Furrier, host of theCUBE, SiliconANGLE Media’s livestreaming studio. Furrier, along with company executives, MongoDB customers and industry analysts, discussed the latest news and insights, as well as what comes next for AI, open-source computing and MongoDB. (* Disclosure below.) 

Here are three key insights you may have missed: 

1. MongoDB is on a journey to address developer needs.

MongoDB’s plans for its next-gen developer data platform was a top area of interest during the conference. It was emblematic of the company continuing its journey of addressing developer needs, according to Sanjeev Mohan, principal at SanjMo.

“They are adding new features, so I see two tracks happening: One is above the line, adding Stream Processing and Vector, so new capabilities,” he said. “And then below the line, which is enhancing what’s already there, like faster queries.”

The company’s goal with its developer data platform comes with a recognition that data is hugely important when it comes to developer workflow, a process that MongoDB has said it aims to simplify. The goal is to enable developers to be more productive and make a more significant impact, according to Dev Ittycheria (pictured), chief executive officer of MongoDB.

“Depending on the application that they’re building, they could spend between 30 to 70% of the time just working with data,” he said. “If you want to make them incredibly productive and make a big impact, it’s all about making it easy to work with data. So, our whole raison d’être is to be able to simplify working with data. We started with the document database, and now it’s with the developer data platform.”

MongoDB is following a trajectory that is familiar to many working in the space, according to Tony Baer, principal at dbInsight LLC. Internally, the company is going through a good sort of “tug of war” amid this transition, according to Baer.

“For years we never took SQL Server seriously, until we did. I would say that’s very much the path that Mongo is going internally within the company,” he said. “It’s always going to be a very developer-centric company. I’ve always said, though, at some point as they go more enterprise, they have to become more of a data-centric company.”

In the market today, data apps are all the rage, with Snowflake Inc. and Databricks Inc. trying to capture modern cloud-native applications. Mongo, meanwhile, has shown it has developer loyalty, and its earnings have been very sticky, according to Doug Henschen, vice president and principal analyst at Constellation Research Inc.

“They’re not seeing some of the fade that some of the analytics-focused companies are seeing. I think they’ve pursued a solid strategy,” he said. “It’s just, you know, [are] the new capabilities solid? Are they there? Are they things that really are going to resonate with loyal MongoDB users?”

Here’s the complete keynote analysis analyst panel with John Furrier, Sanjeev Mohan, Tony Baer and Doug Henschen, part of SiliconANGLE’s and theCUBE’s coverage of the MongoDB.local NYC event:

Here’s theCUBE’s complete video interview with Dev Ittycheri:

2. MongoDB sees opportunity amid the AI explosion.

DevOps democratization has surged over the past 20 years, but the rise of AI has posed some new questions. As developers want to go to the next level with their data, how it’s stored and how it’s run in their apps, how does AI factor into that? 

The first thing to remember is that AI isn’t the only thing to consider, according to Mark Porter, chief technology officer of MongoDB, during an interview with theCUBE.

“It is currently the thing that’s really exciting, and being able to build great apps that do great things with your core data is always going to be important,” he said. “But what’s happening is people are enhancing their apps with AI.”

Hundreds of companies are using MongoDB as the foundation of their AI apps right now, because it’s distributed, scalable, flexible and easy to work against, according to Porter. The company’s developer data platform is key to this approach, with the company’s document model viewed as critical when it comes to enabling the flexible data structures needed for AI applications.

The developer data platform was “prescient” in some ways, according to Mindy Lieberman, chief information officer of MongoDB.

“It’s all about data and applications on top of data,” she said. “I look at it, for IT, as a portfolio. There’s some that is built; there is some that is bought. When you can’t go to the market and find things that are fit for purpose, you have to build. And to have a developer platform available, and I get good pricing, that can’t be beat.”

And when it comes to technology such as ChatGPT, MongoDB’s focus on open-source technology comes into play, according to Tara Hernandez, vice president of developer productivity at MongoDB.

“One of the first things we said is, we took a quick pass and like, ‘You want to play with ChatGPT?’” Hernandez said during an interview with theCUBE. “Point it at the public repos, because the guardrail is already public.”

Open source is being viewed as a major driving force in the AI space right now, which presents some challenges and opportunities for organizations in a rapidly-expanding landscape.

But it’s been nice to see companies such as MongoDB step up to try to capitalize on the opportunity surrounding data right now, according to Maribel Lopez, founder and principal analyst of Lopez Research, during an interview with theCUBE.

“AI’s happening. There’s just no doubt about that. The only question for organizations is how do you make it happen fast and secure and how do you pivot if you got it wrong?” he said.

Here’s theCUBE’s complete video interview with Mark Porter:

Here’s theCUBE’s complete video interview with Maribel Lopez and Steve O’Grady, co-founder and principal analyst at RedMonk:

3. Collaboration is still key.

With that goal of a developer-centric world in mind, companies such as Microsoft Corp. have eyed partnerships with MongoDB and a shared focus on developer experience. AI plays a big role in that, according to Guido Govers, strategic account director of ISV and high-tech partnerships at Microsoft.

“Even the idea of Copilot; from that perspective, leveraging OpenAI and generative AI everywhere on every level,” he said during an interview with theCUBE. “It’s not just on the office side of the house, but it’s also on the developer experience. How can you make it easier for developers to find the content they need? No one wants to look anymore for that little tidbit of information when you can just get that generated and focus on the actual core business.”

In the end, it all comes down to data, which affects everything. That includes the insurance business, in which MongoDB and Databricks have sought to play a new role. One of the most prominent use cases involves an effort to assist customers to modernize away from what are the traditional constraints of legacy systems, according to Jeff Needham, principal of industry solutions at MongoDB.

“Fundamentally, insurance is a data processing organization, has been forever,” he told theCUBE. “For an organization to be able to process data more efficiently, more effectively with less hands-on keyboards is tremendously compelling, obviously, to the industry.”

Here’s theCUBE’s complete video interview with Guido Govers:

To watch more of theCUBE’s coverage of the MongoDB .local NYC event, here’s our complete event video playlist:

https://youtube.com/watch?v=videoseries&list=PLenh213llmcYkRfCxYT5e8MrA8UFXqrSk

(* Disclosure: TheCUBE is a paid media partner for the MongoDB .local NYC event. Neither MongoDB Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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

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If you’re interested in managing data more efficiently, SQL (Structured Query Language) may be the answer you’re looking for. In this article, we’ll dive into the basics of SQL and database management, as well as cover the essential commands you’ll need to know.

Understanding Database Management and SQL

What is Database Management?

Database management is a critical aspect of modern businesses. It involves organizing, storing, and retrieving data in a structured and systematic manner. A database is a collection of information that is well-organized, accurate, and easily accessible. The goal of database management is to help businesses meet their objectives by providing reliable, up-to-date, and relevant data.

Effective database management requires a deep understanding of the data that is being stored, as well as the business processes that rely on that data. Database administrators must ensure that the data is properly structured and organized, and that it can be easily accessed and manipulated as needed.

One of the key benefits of effective database management is that it enables businesses to make data-driven decisions. By having access to accurate and relevant data, businesses can better understand their customers, identify trends, and make informed decisions based on real data rather than intuition or guesswork.

Introduction to SQL

What is SQL? Structured Query Language (SQL) is a powerful tool for managing and manipulating data stored in databases. SQL commands are used to create, modify, and retrieve data. It’s a language that has become the de facto standard for managing relational databases.

SQL is a highly versatile language that can be used to perform a wide range of tasks, from simple data queries to complex data analysis. It’s also relatively easy to learn, making it accessible to both technical and non-technical users.

One of the key benefits of SQL is that it provides a standardized way of interacting with databases. This means that SQL commands can be used across different database platforms, making it easier to work with data across different systems.

SQL vs. NoSQL: Choosing the Right Database System

When it comes to database systems, there are two main types: SQL and NoSQL. SQL is a relational database system that uses tables to organize and store data. It’s highly structured and ensures data integrity. On the other hand, NoSQL is more flexible and scalable. It’s designed for handling large amounts of unstructured data and has become increasingly prevalent in recent years.

Choosing the right system depends on your specific needs and the type of data you’re dealing with. If you need to ensure data accuracy and consistency, you may want to stick with SQL. On the other hand, if you’re dealing with lots of unstructured data that needs to be processed quickly, NoSQL may be the better option.

Another factor to consider when choosing between SQL and NoSQL is scalability. SQL databases can be more difficult to scale horizontally, while NoSQL databases are designed for horizontal scaling. If you anticipate significant growth in your data needs, NoSQL may be the better choice.

Ultimately, the choice between SQL and NoSQL will depend on a variety of factors, including the nature of your data, your business objectives, and your technical requirements. By carefully evaluating your options and understanding the strengths and weaknesses of each system, you can choose the database system that best meets your needs.

Setting Up Your SQL Environment

Installing SQL Server

The first step to using SQL is installing a server. There are many different programs you can use, but we’ll focus on Microsoft SQL Server. Head to Microsoft’s website and download the appropriate version for your operating system. Follow the on-screen instructions to install the server. Once installed, open the application.

Configuring SQL Server

Configuring the server is essential to ensure it’s secure, and set up correctly. The first thing you’ll want to do is set the server’s authentication mode to SQL Server Authentication. This means you can use a username and password to access your data rather than using Windows login credentials. Setting up firewalls and ensuring that the server is only accessible on a secure network are other crucial steps.

Setting Up a Database and Tables

After setting up the server, you’re ready to start creating your first database. Right-click on the Databases folder, then select New Database to bring up a template. Give your database a meaningful name, then continue through the other options to finish creating your database.

With your database created, you can now create tables to store your data. A table is a collection of related data stored as a set of rows. You can create tables and add data manually or import data from other sources.

Basic SQL Commands and Syntax

SELECT: Retrieving Data from a Table

The SELECT command is used to retrieve data from one or more tables. It’s the most commonly used SQL command. To use the SELECT command, you must specify the columns you want to retrieve and the table from which you want to retrieve data. You can also specify conditions to filter the data you want to retrieve.

INSERT: Adding Data to a Table

The INSERT command is used to add new data to a table. You must specify the table and the columns you want to insert data into and provide the values for those columns. Once executed, the data is stored permanently in the table.

UPDATE: Modifying Data in a Table

The UPDATE command is used to modify existing data in a table. You need to specify the table, set the values you want to change, and specify the conditions for selecting the rows you want to update.

DELETE: Removing Data from a Table

The DELETE command is used to remove data from a table. You need to specify the table and the conditions for selecting the rows you want to delete. Once executed, the data is permanently removed from the table.

Advanced SQL Queries and Techniques

JOIN: Combining Data from Multiple Tables

The JOIN command is used to combine data from multiple tables. It’s a crucial concept to understand if you’re working with relational databases. You can join tables on columns that they share, and you must specify the type of join you’re performing. There are many types of joins, including inner join, left join, and right join. The choice of what join to use will depend on the data you’re working with.

GROUP BY and Aggregate Functions

The GROUP BY command is used to group rows into categories based on their values. You can then use aggregate functions to perform calculations on these groups of data, such as COUNT, SUM, or AVG. GROUP BY is useful for generating reports and summarizing large datasets.

Subqueries and Nested Queries

A subquery is a query within another query. It’s used to simplify complex queries, and it’s especially useful for working with large datasets. A nested query is a type of subquery that’s used to retrieve data from multiple tables.

Stored Procedures and Functions

A stored procedure is a precompiled SQL statement that’s stored in the database. It can be called multiple times, and it’s often used to perform complex operations or calculations. A function is a reusable block of code that returns a value. It’s similar to a stored procedure, but it takes input arguments and returns a value.

Conclusion

SQL and database management are essential skills for anyone working with data. Whether you’re a business owner, data analyst, or programmer, learning SQL can help you better manage and manipulate large datasets. By understanding the basics of SQL and database management, you can take advantage of the many benefits these technologies have to offer.

Posted on mongodb google news. Visit mongodb google news

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Nearly Half of Warren Buffett’s $366 Billion Portfolio Is Invested in Only 1 Stock

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

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Hasura has announced Hasura DDN, an edge network that automatically reroutes client requests to the closest Hasura instance, minimizing round-trip time and reducing latency. 

Hasura Data Delivery Network (DDN) was announced at HasuraCon 2023 alongside Hasura Schema Registry and its first data connector for NoSQL data.

Explaining the need for these new technologies, Paul Nashawaty, principal analyst at TechTarget Enterprise Strategy Group said: 

“Data and APIs are the backbone of the modern enterprise. APIs are the predominant mechanism for delivering data from the producers to the edge consumers in a fast, secure, and reliable way. And yet the task of writing, running, scaling, and maintaining APIs on data remains slow, tedious, and a major productivity and time sink for developers. The announcements made by Hasura today make GraphQL and REST APIs easier to author and operate, enhancing the developer experience and bringing the power of APIs to more data.”

Hasura DDN, an edge network of 100+ global regions, with 99.99% uptime guarantees, is intended to enable developers to run low-latency/high-performance data APIs at global scale, with no additional effort and no additional fees, by automatically rerouting client requests to the closest Hasura instance, which minimizes the round-trip time and reduces latency. It has been made posThasible by a major architecture change of the Hasura engine that reduced its cold start time to under 1 millisecond. As a result, the Hasura runtime can be instantiated on the edge region when the API is invoked, enabling instant auto-scaling to handle any spike in traffic, globally.  

It integrates with distributed databases, such as CockroachDB, Amazon Aurora, and YugaByte. DDN will be available soon on Hasura Cloud and as Private DDN for self-hosted Hasura customers.

DDN will also facilitate rapid API iteration and testing. According to Hasura developers can go from code to global-scale production (including building, validation, and testing) in less than one second commenting:

This rapid CI/CD is true regardless of the number of data models connected, across all changes and scenarios. 

You can sign up here to be notified about DDN and request to join the early access group.

The new Hasura Schema Registry makes managing, governing, and collaborating on a federated API simpler. Building on the existing Hasura Federation, which lets developers compose multiple domain APIs into a single, unified API, aka the supergraph, it allows developers across multiple teams to easily control and audit GraphQL schema changes across diverse data sources,  providing them more confidence in push changes in mission-critical production apps. 

The launch of the MongoDB Data Connector enables MongoDB developers can use the new connector to automatically create a GraphQL API from their collections and documents. It extends Hausura’s reach beyond SQL, which it supports with connectors to Snowflake, MySQL, MariaDB, and Oracle, to its first NoSQL data store.  Connectors for other NoSQL stores, including Cassandra/Datastax and Elasticsearch, will be available soon, as will support for existing NoSQL capabilities including PostgreSQL JSONB columns. 

Other new features include Native Queries and Logical Models which enable developers to incorporate the capabilities of their database’s query language in Hasura’s auto-generated APIs. 

The company also announced new open-source innovations in Open DDS and Native Data Connector. Open DDS (previously known as GraphQL Data Specification) allows developers to create enterprise-grade APIs using a domain model-driven approach while Native Data Connector (previously known as the GraphQL Data Connector) allows developers to create custom data agents. By open-sourcing the project the company hopes developers will have more support for building their agents. 

Hasura Cloud is now available on Microsoft Azure, in addition to its existing availability on AWS and GCP.

At HasuraCon, Tanmai Gopal, co-founder and CEO of Hasura claimed:

“This is the largest and most significant set of innovations that we have created to date in our journey to make data APIs available and beneficial to all developers. Hasura DDN introduces a number of industry firsts, and does more to reduce the time needed for data to make it from provider to consumer than any other Hasura capability. We are extremely proud of what we have accomplished since the last HasuraCon, and look forward to showing the community what else we have in store.”  

More Information

Hasura Website

Related Articles

Hasura GraphQL Adds REST Support

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Microsoft has recently introduced a domain-specific language called Guidance, created to improve developers’ ability to manage contemporary language models. The new framework integrates aspects such as generation, prompting, and logical control into a unified process for developers.

The GitHub repository notes that the programming language enables developers to “interleave generation, prompting and logical control into a single continuous flow matching how the language model actually processes the text”. It can be seamlessly integrated with providers like Hugging Face models and incorporates a smart seed-based generation caching system and token healing, which optimizes prompt boundaries and eliminates bias in tokenization. The inclusion of regex pattern guides further ensures the enforcement of formats, allowing for the natural completion of prompts.

Philippe Limantour, chief technology and cybersecurity officer at Microsoft France, wrote “users can seamlessly merge generation, prompting, and logical control, thereby creating a continuous flow that aligns with the inherent text-processing mechanism of the language model”.

Reaction to Guidance from outside Microsoft has also been relatively positive. Guidance looks to mitigate the complexity of LLMs by providing developers with “a simple yet comprehensive syntax for architecting complex language model workflows,” according to Jesus Rodriguez, a guest lecturer at Columbia University and Wharton.

The framework isn’t fully complete. Current extension requests for the framework include requests for additional LLM support, better integration with LangChain, and support for OpenAI function calling.

Guidance is part of a wider ecosystem of tools for extending the capabilities of language models. Frameworks like LangChain and Haystack have emerged to make it easier to integrate models into applications. Handlebars, Language Model Query Language (LMQL), and Nvidia’s NeMo Guardrails for mitigating the harmful impacts of LLMs.