Month: September 2022

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2022-09-30 09:16:04

Why You Need a Mobile Database

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Providing a fast, reliable experience is key to the success of your mobile app. Using a database built for mobile apps is key to achieving it.

Sep 30th, 2022 9:16am by

Mark Gamble

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Mobile app usage is increasing worldwide, not only in user volume, but in daily time spent on apps. TechCrunch recently reported that mobile users now spend between four and five hours — roughly a third of waking hours — on smartphone apps daily. And when it comes to which apps they use, there is no shortage of choices. Business of Apps reported that Google Play currently offers around 2.8 million apps and games, and the Apple App Store offers around 4.5 million.

But this huge volume of apps — and the increasing length of time that users are spending on them — means that users can be extremely selective with the apps they choose to engage with. In fact, according to a research study by mobile marketing analytics vendor AppsFlyer, nearly one in every two apps are uninstalled within 30 days, and a study by Andrew Chen found that losing 80% of mobile users is “normal” for all but the most popular apps.

A big reason for the high rate of app abandonment is bad experience, particularly slowness and unreliability. A report by Think Storage Now found that 70% of mobile app users will abandon an app that takes too long to load. And an older but still often cited Compuware study found that 84% of app users will abandon an app if it fails just two times.

These facts help emphasize that the margin of error is small when it comes to keeping users happy and engaged. Providing a fast, reliable experience is key to the success of your mobile app, and using the right database — one built for mobile apps — is key to achieving it.

Database Options for Mobile Apps

You may wonder what we mean by “mobile database.” A database is a database, right?

Not exactly. When it comes to developing a mobile application, whether native, web based or hybrid, you need to choose a database that can provide the speed and reliability your users demand. Let’s examine the options.

Relational vs. NoSQL

A relational database stores data in highly organized tables maintained by a rigid and fixed schema designed for consistency. But relational rigidity comes with a price — app developers must conform their code to the schema, and any required changes to data results in time-consuming schema updates, making app updates challenging.

In contrast, a NoSQL database stores data as JSON documents, eliminating the need for a schema and allowing developers to change the database on demand as app requirements evolve. Additionally, NoSQL databases are distributed, meaning they can be deployed across multiple nodes, providing superior performance and reliability for applications.

In general, because of their distributed nature and data model flexibility, NoSQL databases are ideally suited for mobile apps because of their speed and reliability.

Cloud Database

In a cloud database model, mobile and IoT apps use a database that runs in the cloud.

The cloud database model offers a quick on-ramp to a standardized and distributed infrastructure that provides scale, elasticity and flexibility for mobile apps.

The problem is that the model depends on the internet, which is inherently unreliable. If the internet slows, so do the apps that depend on it. And if the internet goes down, apps stop working altogether, frustrating users and costing business downtime.

Because of internet dependencies, the cloud database model presents challenges in meeting mobile app users’ expectations. When they go into areas with poor network connectivity, such as a subway station, airplane or tunnel, their apps become unreliable at best and useless at worst.

To guarantee a fast and reliable mobile app experience, you need to eliminate internet dependencies.

Embedded Database

Embedded database technology such as SQLite is another option for mobile app development. This is where the database runs within the application installed on the mobile device. By embedding the database in the app itself, you completely remove the need for a network connection while gaining the highest guarantees of speed and availability.

Using an embedded database can be great for apps that are standalone in nature and that have data that rarely changes, but their lack of connectivity presents challenges for multiuser apps, where data changes frequently and must be shared with other users. With an embedded database, developers either have to build in data-sharing capabilities in their embedded apps or do without.

Mobile Database

The mobile database model is a merging of the cloud and embedded database models, combining the best aspects of each into an architecture that powers offline-first apps impervious to internet slowness and outages.

The model consists of a central cloud database and an embedded database running on mobile clients that share data via automatic synchronization — the embedded databases sync data between the cloud database and other embedded devices as it is captured or modified. When a network connection isn’t available, data is stored and processed locally, and when the connection is restored, sync resumes automatically.

Mobile databases also offer a peer-to-peer feature, allowing multiple embedded devices in close proximity to sync data using private area networks such as Bluetooth, enabling collaboration in a disconnected environment.

The mobile database model is able to meet the expectations of mobile app users by providing the fast and reliable experience they demand. Embedded local data processing ensures real-time responsiveness and high availability regardless of internet connectivity, and cloud-to-edge sync ensures that the user experience is always current and consistent.

The Couchbase Mobile Database

Couchbase provides a mobile database that brings the power and flexibility of a cloud NoSQL database to the edge.

The Couchbase mobile stack includes:

• Couchbase Capella — A fully managed cloud NoSQL Database-as-a-Service (DBaaS) with support for SQL, search, analytics and eventing.
• Capella App Services — Fully managed services for bidirectional sync, authentication and access control for mobile and edge apps.
• Couchbase Lite — An embedded mobile database with broad mobile platform support.

A Fortune 500 and one of the world’s largest oilfield services companies, Halliburton uses Couchbase’s mobile database technology to automate various processes and workflows aiming to achieve new business opportunities and efficiencies across well sites and drilling operations. With our unique mobile and data synchronization capabilities, Halliburton can sync data across devices with limited to no internet connectivity, increasing efficiency for field workers by preventing both data duplication and redundancy of efforts.

Test drive Couchbase Capella and App Services for free.

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Mark Gamble is marketing director for Product and Solutions at Couchbase. He has more than years of experience in enterprise and open source technology.

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THE NEW STACK UPDATE
A newsletter digest of the week’s most important stories & analyses.

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MongoDB Inc. (NASDAQ:MDB)’s traded shares stood at 1.26 million during the last session, with the company’s beta value hitting 1.08. At the close of trading, the stock’s price was $194.82, to imply a decrease of -3.22% or -$6.49 in intraday trading. The MDB share’s 52-week high remains $590.00, putting it -202.84% down since that peak but still an impressive 3.41% since price per share fell to its 52-week low of $188.17. The company has a valuation of $13.62B, with an average of 1.92 million shares in intraday trading volume over the past 10 days and average of 1.66 million shares over the past 3 months.

Analysts have given a consensus recommendation of an Overweight for MongoDB Inc. (MDB), translating to a mean rating of 2.00. Of 23 analyst(s) looking at the stock, 1 analyst(s) give MDB a Sell rating. 2 of those analysts rate the stock as Overweight while 5 advise Hold as 15 recommend it as a Buy. 0 analyst(s) have given it an Underweight rating. Estimates put the company’s current-quarter earnings per share at -$0.28.

After registering a -3.22% downside in the last session, MongoDB Inc. (MDB) has traded red over the past five days. The stock hit a weekly high of 203.68 this Thursday, 09/29/22, dropping -3.22% in its intraday price action. The 5-day price performance for the stock is -1.71%, and -41.09% over 30 days. With these gigs, the year-to-date price performance is -63.20%. Short interest in MongoDB Inc. (NASDAQ:MDB) saw shorts transact 3.87 million shares and set a 2.34 days time to cover.

Analysts on Wall Street suggest a consensus price target of $353.50, implying an increase of 44.89% to the stock’s current value. The extremes give us $210.00 and $445.00 for target low and target high price respectively. As such, MDB has been trading -128.42% off suggested target high and -7.79% from its likely low.

MongoDB Inc. (MDB) estimates and forecasts

Looking at statistics comparing MongoDB Inc. share performance against respective industry, we note that the company has outperformed competitors. MongoDB Inc. (MDB) shares are -54.72% down over the last 6 months, with its year-to-date growth rate higher than industry average at 45.76% against 1.90%. Revenue is forecast to shrink -16.70% this quarter before falling -27.30% for the next one. The rating firms project that company’s revenue will grow 36.40% compared to the previous financial year.

Revenue forecast for the current quarter as set by 16 analysts is $282.4 million. Meanwhile, for the quarter ending Oct 2022, a total of 16 analyst(s) estimate revenue growth to $294.85 million.

MDB Dividends

MongoDB Inc. has its next earnings report out between December 05 and December 09. However, it is important to take into account that this dividend yield ratio is just an indicator to only serve the purpose of guidance. Investors interested to invest in the stock should ponder company’s other fundamental and operations related aspects too. MongoDB Inc. has a forward dividend ratio of 0, with the share yield ticking at 0.00% to continue the rising pattern observed over the past year. The company’s average dividend yield trailing the past 5-year period is 0.00%.

MongoDB Inc. (NASDAQ:MDB)’s Major holders

MongoDB Inc. insiders hold 3.68% of total outstanding shares, with institutional holders owning 90.86% of the shares at 94.33% float percentage. In total, 90.86% institutions holds shares in the company, led by Price (T.Rowe) Associates Inc. As of Mar 30, 2022, the company held over 8.23 million shares (or 12.09% of shares), all amounting to roughly $3.65 billion.

The next major institution holding the largest number of shares is Capital World Investors with 6.45 million shares, or about 9.46% of shares outstanding. As of the market price on Mar 30, 2022, these shares were worth $2.86 billion.

We also have Growth Fund Of America Inc and Vanguard Total Stock Market Index Fund as the top two Mutual Funds with the largest holdings of the MongoDB Inc. (MDB) shares. Going by data provided on Jun 29, 2022, Growth Fund Of America Inc holds roughly 4.62 million shares. This is just over 6.78% of the total shares, with a market valuation of $1.2 billion. Data from the same date shows that the other fund manager holds a little less at 1.83 million, or 2.69% of the shares, all valued at about 812.81 million.

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09/30/2022 | 09:10am EDT

© MT Newswires 2022

All news about MONGODB, INC.

Analyst Recommendations on MONGODB, INC.

Chart MONGODB, INC.

Duration : Period :

Sell Buy
Mean consensus	OUTPERFORM
Number of Analysts	25
Last Close Price	198,56 $
Average target price	360,55 $
Spread / Average Target	81,6%

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What happened

Shares of MongoDB (MDB 1.92%) were moving higher Friday after the cloud software stock received a bullish analyst note from Truist. As of 1:43 p.m. ET, the stock was up 5.4%.

So what

Truist analyst Miller Jump initiated coverage on MongoDB Friday morning with a buy rating and a price target of $300, implying an upside of more than 50% from the stock’s closing price Thursday. Jump argued that the provider of NoSQL database software is at the crossroads of several converging database markets, and said Wall Street was underestimating its revenue growth potential over the next decade. He also believes the stock will attract a wider investor pool once the company reaches profitability.

Like many software-as-a-service (SaaS) stocks, MongoDB shares have fallen sharply this year as valuations in the sector have compressed due to rising interest rates, fears of a recession, and concerns about a lack of profitability among cloud companies.

MongoDB stock is now down by nearly two-thirds from its late 2021 peak, and the stock plunged following its most recent earnings report as concerns about ongoing losses trumped its strong revenue growth.

Now what

MongoDB has been spending aggressively on sales and marketing to drive adoption of Atlas, its cloud-based database product. In its second quarter, which ended July 31, revenue from Atlas jumped 73% and accounted for 64% of the company’s $303.7 million in revenue.

Management remains confident in its long-term growth opportunity, and said in its recent earnings call that even with the macroeconomic headwinds, it has seen no changes in its sales cycle, indicating strong demand for its products.

Though an analyst upgrade isn’t going to change the trajectory for the stock, the bull case for MongoDB remains sound despite the shift in investor sentiment.

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Technical debt can be quantified in various ways, but you cannot precisely quantify the associated financial debt. According to Kevlin Henney, we can quantify things like how many debt items we have, the estimated time to fix each debt item, a variety of metrics associated with our code, such as cyclomatic complexity, degree of duplication, number of lines of code, etc. All of these are identifiable and countable, but quantifying how much financial debt is present in the code doesn’t work.

Henney gave a keynote about Six Impossible Things at QCon London 2022 and at QCon Plus May 10-20, 2022.

There are many things about technical debt that can be quantified. Henney mentioned that we can list off and number specific issues in code and, if we take the intentional sense in which technical debt was originally introduced, we can track the decisions that we have made whose implementations need to be revisited. If we focus on unintentional debt, we can look at a variety of metrics that tell us about qualities in code.

There’s a lot that we can quantify when it comes to technical debt, but the actual associated financial debt is not one of them, as Henney explained:

The idea that we can run a static analysis over the code and come out with a monetary value that is a meaningful translation of technical debt into a financial debt is both a deep misunderstanding of the metaphor – and how metaphors work – and an impossibility.

According to Henney, quantifying how much financial debt is present in the code doesn’t work. At the very least, we need a meaningful conversion function that takes one kind of concept, e.g., “percentage of duplicate code” or “non-configurable database access”, and translates it to another, e.g., euros and cents:

What is the debt value of “non-configurable database access”? What is the debt value of basing your architecture on one third-party framework versus another? What is the debt value of a decision that may (or may not) have short-term benefits but will (probably) need updating in the long-term? The question is so abstract and malleable that trying to squeeze a financial figure out of it seems not only premature, but smacks of bad science.

Henney stated that just because we may have a number for one kind of thing, such as a code metric, that doesn’t mean we can convert it to a number of another kind of thing, such as money:

It has no more meaningful correspondence to a financial value than the RGB value of my eye colour has to my height.

While we might agree that excessive code duplication represents a code smell, it doesn’t mean that duplication has a simple debt rate, as Henney explained:

In some contexts, duplication can be introduced to reduce coupling, meaning that it can reduce other debts and, therefore, it acts as a credit. Dead code can be recognised as a debt arising from obsolescence, but what about duplication in dead code? In such cases duplication is zero-rated.

Of course, we can always fabricate values and conversions, but we must recognise those as fiction rather than science, Henney concluded. It’s a little like the calculation of Blue Monday, the third Monday in January, which is allegedly the most depressing day of the year according to a “pseudoscience formula that is pure nonsense on stilts”.

This article concludes the series on Henney’s Six Impossible Things keynote:

1. It’s impossible to directly represent infinity or to hold infinite precision on a discrete physical computer, as storage and representations are bounded.
2. Not every question has an answer; developers should increase awareness of unexpected failure modes, advertise the possibility of failure, and use time-outs to recover from waiting for an answer that will never come.
3. The truth can’t always be established where it applies as not all preconditions can be checked in code due to the definitional constraints of the programming language.
4. The future isn’t knowable before it happens; to deal with unknowable unknowns, a solution is to be more experimental and hypothesis-driven in our development.
5. A distributed system is not knowable; failure is normal and distributed systems can provide only two of the three guarantees in consistency, availability, and partition tolerance.

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In this study, we’ll look into the vast bulk of international Big Data and Analytics Market . The research’s overarching goal is to identify the variables that influence the degree to which the study’s intended audience can adapt to a market that is undergoing severe upheaval at the present time. In addition, it examines how consumer habits and preferences evolve over time, as well as how the current market and its expected future expansion affect these factors. Results from keyword-based international research suggest that sales and marketing, supply chain, product development, and cost structure all have a say in the company’s top and bottom lines.

Companies operating in the Market

Microsoft
MongoDB
Predikto
Informatica
CS
Blue Yonder
Azure
Software AG
Sensewaves
TempoIQ
SAP
OT
IBM
Cyber Group
Splunk

The global market for Big Data and Analytics will be the primary focus of our investigation, and we will be paying special attention to growing derivatives. This study serves as a jumping off point for additional research into the global trade by examining the elements that have the biggest impact on the industry’s overall growth. The article zeroes down on the elements that are most consequential to the expansion of the market. The international trade research study COVID-19 analysis aims to investigate aims to analyse the issues and impacts that have surfaced as a direct result of the epidemic.

Metric and trade share comparisons are conducted before the pandemic, and the immediate post-epidemic industry situation is used to assess the severity of the pandemic’s impact. Analysis of the global businesses for the Big Data and Analytics also considers things like a drop in production and the potential for a shaky asset situation.

We Have Recent Updates of the Market in Sample [email protected] https://www.orbisresearch.com/contacts/request-sample/4611279?utm_source=Pooja5Rsept28

By the product type, the market is primarily split into:

Data Intergration
Data Storage
Data Presentation

By the end-users/application, this report covers the following segments:

LoT
M2M

The study concluded that the global segments and sectors needed a solid backup strategy for its numerous business components. In the next section, we’ll talk about the kinds of businesses and individuals that make use of the products and services provided by the international Big Data and Analytics market. The report details the current market share and overall size of each organization in relation to their respective industry and classification. After the universal trade has been segmented into countries, a national analysis is conducted to identify which regions are most important when considering economic, political, social, and geographical factors. Examining the offer-making capabilities of various market actors is another focus of this study.

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In this piece, we’ll take a look at the cutting-edge products and services that are doing well in the globally competitive Big Data and Analytics industry right now. This article examines how various players and participants can benefit from new technologies, business models, and promotional strategies. The research zeroes focused on how various participants in the market might profit from technological developments. This research not only investigates potential new avenues of profit, but also estimates future earnings.

Table of Content
Chapter 1. Preface
1.1 Report Description and Scope
1.2 Research scope
1.3 Research methodology

Chapter 2. Executive Summary
2.1 Big Data and Analytics Industry, (2022 – 2028) (USD Million)
2.2 Big Data and Analytics Market : snapshot

Chapter 3. Big Data and Analytics Market – Industry Analysis
3.1 Dynamics
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Challenges

Chapter 4. Big Data and Analytics Market- Competitive Landscape
4.1 Company market share analysis
4.2 Strategic development
4.3 Price trend analysis

Chapter 5. Big Data and Analytics Market keyplayers analysis
5.1 Overview
5.2 Financials
5.3 Product Portfolio
5.4 Business Strategy
5.5 Recent Developments

Chapter 6. Big Data and Analytics Market Segmentation
6.1 Application
6.2 Product type

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The research looks at what is expected to drive investment in Big Data and Analytics around the world during the course of the pandemic.

• If a vaccine or treatment is developed for COVID-19, do you think the global industry will increase? How can the regional and international markets recover their elasticity, focus on customers’ needs, and competitiveness after the pandemic?

• Answering the question, “What kinds of businesses do you think will be the driving force behind expansion in the global market?”

• To what end and by whom have the governments of the countries that dominate the global industry established the plans and policies necessary to ensure the continuous usage and growth of their product?

• How have the world’s largest companies in the respective sectors responded to the pandemic?

About Us:
Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

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Global NoSQL Software Market Overview :

The global NoSQL Software market is expected to grow at a significant pace, according to a verified market research. The latest research report, titled NoSQL Software Market, offers a unique perspective on the global market. Analysts believe that changing consumption patterns should have a big impact on the market as a whole. For a brief overview of the Global NoSQL Software market, the research report contains a summary. It explains the various factors that make up an important part of the market. It includes the definition and coverage of the market with a detailed explanation of the market drivers, opportunities, constraints and threats.

Global NoSQL Software Market Segmentation :

Segmentation chapters allow readers to understand aspects of the market, such as its products, available technologies and their applications. These chapters are written in such a way as to describe how they have evolved over the years, and what course they are likely to choose in the coming years. The research report also provides detailed information on emerging trends that may determine progress in these segments in the coming years.

NoSQL Software Market is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2021 to 2028.

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Global NoSQL Software Market : Competitive rivalry

The research report includes an analysis of the competitive environment present in the Global NoSQL Software market. It includes an assessment of current and future trends in which players can invest. In addition, it also includes an assessment of the financial prospects of the players and explains the nature of the competition.

Key Players mentioned in the Global Market Research Report NoSQL Software Market:

• Amazon
• Couchbase
• MongoDB Inc.
• Microsoft
• Marklogic
• OrientDB
• ArangoDB
• Redis
• CouchDB
• DataStax.

Market segmentation of NoSQL Software market:

NoSQL Software market is divided by type and application. For the period 2021-2028, cross-segment growth provides accurate calculations and forecasts of sales by Type and Application in terms of volume and value. This analysis can help you grow your business by targeting qualified niche markets.

NoSQL Software Market, By Type

• Document Databases
• Key-vale Databases
• Wide-column Store
• Graph Databases
• Others

NoSQL Market, By Application

• Social Networking
• Web Applications
• E-Commerce
• Data Analytics
• Data Storage
• Others

Global NoSQL Software Market: Research methodology

The research methodologies used by analysts play a crucial role in how the publication was compiled. Analysts used primary and secondary research methodologies to create a comprehensive analysis. For an accurate and accurate analysis of the Global NoSQL Software market analysts use ascending and descending approaches.

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NoSQL Software Market Report Scope 

 
Global NoSQL Software Market: Regional segmentation

For further understanding, the research report includes a geographical segmentation of the Global NoSQL Software Market. It provides an assessment of the volatility of political scenarios and changes that may be made to regulatory structures. This estimate provides an accurate analysis of the regional growth of the Global NoSQL Software Market.

Middle East and Africa (GCC countries and Egypt)
North America (USA, Mexico and Canada)
South America (Brazil, etc.)
Europe (Turkey, Germany, Russia, Great Britain, Italy, France, etc.)
Asia-Pacific region (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia and Australia)

Table of Contents

Report Overview: It includes major players of the global NoSQL Software Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends: This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global NoSQL Software Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global NoSQL Software Market are discussed.

Market Share by Manufacturers: Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type: This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application: Besides an overview of the global NoSQL Software Market by application, it gives a study on the consumption in the global NoSQL Software Market by application.

Production by Region: Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region: This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles: Almost all leading players of the global NoSQL Software Market are profiled in this section. The analysts have provided information about their recent developments in the global NoSQL Software Market, products, revenue, production, business, and company.

Market Forecast by Production: The production and production value forecasts included in this section are for the global NoSQL Software Market as well as for key regional markets.

Market Forecast by Consumption: The consumption and consumption value forecasts included in this section are for the global NoSQL Software Market as well as for key regional markets.

Value Chain and Sales Analysis: It deeply analyzes customers, distributors, sales channels, and value chain of the global NoSQL Software Market.

To Gain More Insights into the Market Analysis, Browse Summary of the Research Report @ https://www.verifiedmarketresearch.com/product/nosql-software-market/ 

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The stock of MongoDB Inc. (NASDAQ:MDB) increased by $8.42 on Tuesday to $202.86, up 4.33 percent. The last five days have seen an average of 1,772,526 shares of common stock traded. 1 time new highs were reached in the current year, with a fall of -$328.41. The average number of shares traded over the last 20 days was 2,427,066, while the average volume over the last 50 days totaled 1,661,199.

MDB stock dropped -45.01% since last month. On 09/26/22, the company’s shares reached a one-month low of $188.17. The stock touched a high of $590.00 on 01/03/22, after rallying from a low of $188.17 in 52 weeks. The price of MDB stock has declined by -63.27% or -$328.41 this year, reaching a new high 1 time. Still, the stock price is down -65.62% from the 52-week high.

Insider Transactions

There have been 19 days since MongoDB Inc. (MDB) last reported insider trading activity on Sep 09. On Sep 09, President & CEO Ittycheria Dev sold 16,991 shares at $250.66 each. This transaction resulted in the insider making $4,259,002. On Sep 08, Ittycheria Dev sold 68,009 shares at a price of US$250.00. After the transaction, the insider now owns 199,753 shares. Chief Revenue Officer Pech Cedric had earlier sold 11,340 shares on Sep 08 for $250.00 a share. The transaction was completed for $2,835,000.

Valuation Metrics

Beta for the stock is 1.08. There are also a few other valuation ratios worth considering, including the trailing price-to-sales (P/S) ratio of 12.56, the price-to-book (PB) ratio of 19.88.

Financial Health

For the three months ended July 30, MongoDB Inc.’s quick ratio was 4.00, while its current ratio was 4.00, indicating its ability to pay off its debt. The company’s long-term debt to equity ratio for the quarter ending July 30 is 1.77, and the total debt to equity ratio is 1.78. As far as profitability goes, gross margin for the trailing twelve months is 71.30% percent. MongoDB Inc.’s operating margin stood at -32.00% for the same period. Based on annual data, it had gross profit of $614.29 million and revenue of $873.78 million.

Investors will also look at the performance of the company’s management in order to determine the potential profitability of their investment. MDB’s return on assets (ROA) during the last 12 months has been -14.90%. There was a -15.80% return on investment (ROI) in the past year. In the meantime, the return on equity (ROE) for the last 12 months was -54.20%.

Earnings Surprise

According to MongoDB Inc.’s quarterly financial report for the quarter that ended July 30, it had $1.14 billion in cash and short-term investments. A higher net income was reported in the quarter under review than the previous quarter. Net income for the quarter came in at $303.66 million, while revenues rose by 34.55% to $285.45 million. It was predicted that MongoDB Inc.’s quarterly earnings would be -$0.23, but it ended up being -$0.28, beating the consensus by 17.90%. EBITDA was -$106.61 million for the quarter. At the end of MongoDB Inc.’s most recent quarter ended July 30, its liabilities totaled 1.78 billion, while its total debt was $1.24 billion. Equity owned by shareholders amounts to $68.71 million.

Technical Picture

Here’s a quick look at MongoDB Inc.’s (MDB) price momentum from a technical perspective. As of 27 September, the RSI 9-day stood at 30.46%, suggesting the stock is Neutral, with a 46.25% historical volatility rate.

The stochastic %K and %D were 7.57% and 5.22% respectively, while the average true range (ATR) was 14.20. Based on the 14-day stochastic reading of 14.11%, the RSI (14) reading is 31.80%. On the 9-day MACD Oscillator, the stock is at -10.23, and the 14-day reading is at -28.34.

Analyst Ratings

In its analyst report released on July 13, 2022, Robert W. Baird began covering MongoDB Inc. (NASDAQ: MDB). The stock was rated as an Outperform by the brokerage firm.

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Article originally posted on InfoQ. Visit InfoQ

Oracle has released Project Helidon 3.0, featuring support for JDK 17, Jakarta EE 9.1, and MicroProfile 5.0. Also included in this release is the new Helidon Starter for generating custom Helidon applications, an updated command-line tool, and a security hardening of Java serialization through JEP 290: Filter Incoming Serialization Data.

Helidon is a cloud-native Java framework for microservices, supporting both imperative and reactive applications. Due to security concerns, Helidon doesn’t use Java serialization and disables it by default for applications. Helidon 3.0 also improves the routing of reactive applications.

Upgrading to version 3.0 is straightforward for reactive applications. Imperative applications, on the other hand, “need to change to jakarta.* dependencies and address backwards compatibility issues.”

Helidon recently demonstrated early support for the virtual threads of Project Loom with Helidon Níma. Níma is part of Helidon 4.0, scheduled for a formal release by the end of 2023. Virtual Threads (preview) and Structured Concurrency (incubator), under the auspices of Project Loom, were delivered in JDK 19.

Helidon joins Quarkus and Micronaut as Java frameworks that officially support native Java with GraalVM in production today. Spring Boot 3.0, planned for a GA release in late 2022, will add built-in native Java support.

InfoQ spoke to Helidon Project lead Dmitry Kornilov, director of software development at Oracle and Project Helidon lead, about this project.

Since the release of Spring 1.0 in 2004, only one significant new Java framework has appeared: DropWizard in 2011. All that changed when Micronaut and Helidon were introduced in 2018 and Quarkus in 2019. Why do you think that happened?

Kornilov: Microservice development was growing in popularity back then, increasing demand for lightweight cloud-native Java microservice frameworks. These new frameworks have a much smaller footprint than Java/Jakarta EE application servers and are optimized for modern microservices development. Helidon provides an open implementation based on the MicroProfile standard that gives developers compatibility and portability.

How is writing a Java microservice with Helidon different from writing it with Spring Boot?

Kornilov: The declarative development experience with Helidon is identical to Spring Boot and Quarkus. But the runtime is different: We are trying to make Helidon as effective as possible by reducing the number of third-party dependencies and through various performance optimizations.

Helidon supports imperative (Helidon MP) and reactive programming (Helidon SE). In your estimation, what percentage of Helidon users pick reactive programming?

Kornilov: I don’t have statistics, but I estimate 30 percent. It’s a trade-off: If your focus is on business logic, you can use imperative programming with Helidon MP. If you need the highest possible performance and are willing to invest in the additional complexity of asynchronous development, use the reactive model of Helidon SE. Many users have chosen Helidon SE for that reason.

Helidon allows deploying Java applications to native executables through the GraalVM Native Image compiler. These native executables start faster and use less memory than regular Java applications. In your experience, what percentage of Helidon users runs native Java in production?

Kornilov: I don’t have the numbers. I think native Java is a great choice for applications requiring fast startup time, like serverless functions. For long-running services, which is the most common use case for Helidon applications, HotSpot JRE is preferred because of its excellent runtime optimization.

Helidon also provides an option to build a JLink image with smaller footprint and faster startup time.

How do you think Helidon compares with Quarkus and Micronaut?

Kornilov: Only Helidon supports all features of the Jakarta CDI dependency injection specification in native Java, including portable extensions. Helidon uses Red Hat’s Weld CDI implementation and improved it for native Java.

Micronaut uses its own injection mechanism and doesn’t support CDI. Quarkus’ implementation doesn’t support some CDI features, such as portable extensions, but is optimized for build-time processing.

If there’s one thing you could change about native Java with GraalVM, what would that be?

Kornilov: I asked the Helidon developers for suggestions. First, it would be great to have public APIs for custom extensions such as features, substitutions, etc. There are private APIs for that, but it’s a bad practice to use them. Another suggestion is giving more informative error messages to help fix compatibility issues.

In which areas do you see native Java used most with Helidon? And where is native Java not suitable for Helidon users?

Kornilov: Helidon fully supports GraalVM Native Image. We recently published some Helidon success stories (more are coming). Oracle CX Industry Framework (CXIF) uses Helidon with native Java:

Together with Helidon, GraalVM is also a critical technology to the CXIF architecture. CXIF uses GraalVM Native Image to create minimum-size, precompiled executable images of its microservices. These images are further compressed using UPX compression to yield Docker container images of size <50MB for use with Oracle Cloud Infrastructure Container Engine for Kubernetes (OKE). This enables extremely fast startup time for containerized microservices, allowing CXIF to dynamically start containers running microservices on-demand as requests come in!

More details on this latest release may be found on this GitHub repository. Also, developers interested in a deep-dive of native Java may peruse through this InfoQ six-article series.

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Spring Boot Migrator (SBM) is an experimental Spring project first released in March 2022. SBM allows developers to convert existing, non Spring Boot applications, which are based on technologies such as JAX-RS, EJB and JMS to Spring Boot or upgrade older Spring Boot applications to the latest version.

SBM is based on OpenRewrite, a general purpose tool for source code and configuration refactoring. OpenRewrite uses Recipes to change existing files for Kubernetes, Gradle, Maven, Java and others. The recipes allow, for example, to upgrade an existing application to a newer version of Java. SBM uses OpenRewrite specifically for Spring Boot migrations.

After downloading the latest version of SBM, the command-line interface (CLI) can be started with:

java -jar spring-boot-migrator.jar

After a few seconds, the user is presented with an SBM-specific prompt: migrator:>.

The list command displays the thirty recipes currently available. The recipes, for example, support upgrading Spring Boot to a new version, change XML Bean configuration to Java configuration and migrate various Java EE / Jakarta implementations to Spring Boot.

With the scan [directory] command, an application is analyzed and the applicable recipes are displayed. The following shows an example, older JAX-RS application without Spring Boot support analyzed by SBM, displaying the following results:

scanning 'JAXRS'

Checked preconditions for '.../JAXRS'
[ok] Found pom.xml.
[ok] 'sbm.gitSupportEnabled' is 'true', changes will be committed to branch [master] after each recipe.
[ok] Required Java version (17) was found.
[ok] Found required source dir 'src/main/java'.

Maven    	100% │███████████████████████████│ 2/2 (0:00:09 / 0:00:00)

Applicable recipes:

	= 'automated recipe'
  = 'partially automated recipe'
	= 'manual recipe'

  - initialize-spring-boot-migration []
 	-> Initialize an application as Spring Boot application.
  - migrate-jax-rs []
 	-> Any class has import starting with javax.ws.rs
  - cn-spring-cloud-config-server []
 	-> Externalize properties to Spring Cloud Config Server

Based on the source code of the application, SBM displays the recipes matching the preconditions. When no recipe matches, the list of applicable recipes will remain empty. In this case, one of the recipes listed above may be applied, for example to convert the existing code to a Spring Boot application:

migrator:> apply initialize-spring-boot-migration

This results in a new Git commit with the description SBM: applied recipe ‘initialize-spring-boot-migration’.

Analyzing the commit shows the following changes in the pom.xml: the packaging was changed from WAR to JAR, the spring-boot-starter and spring-boot-starter-test dependencies and the spring-boot-maven-plugin were added and the dependencyManagement section now contains the spring-boot-dependencies of type pom. Depending on the original indentation of the pom.xml file, the indentation may change as well to reflect the recipe.

The source code was altered as well and now contains a SpringBootApp.java and a SpringBootAppTest.java:

@SpringBootApplication
public class SpringBootApp {

	public static void main(String[] args) {
    	SpringApplication.run(SpringBootApp.class, args);
	}
}

@SpringBootTest
class SpringBootAppTest {

	@Test
	void contextLoads() {
	}

}

Now the application is converted to a Spring Boot application. The next step is to migrate the JAX-RS source code to Spring Boot with the command:

migrator:> apply migrate-jax-rs

The command results in a new Git commit with the description: SBM: applied recipe ‘migrate-jax-rs’. Analyzing the commit shows the JAX-RS imports were removed and replaced with Spring imports and the JAX-RS @Path annotation on the class file was replaced with the Spring Boot @RestController and @RequestMapping. The various methods now have Spring Boot’s @RequestMapping, @RequestParam, @PathVariable instead of the JAX-RS annotations such as @Get, @Post, @Path, @Produces, @QueryParam and @PathParam. Lastly the return type of the methods is no longer of type Response, but of type ResponseEntity.

Running the Spring Boot application after the migrations unfortunately failed as the maven-compiler-plugin defined in the pom.xml file used an older version of Java. Manually changing the pom.xml to use the currently installed Java version fixes the problem, but it’s also possible to automate the step with the Change Maven plugin configuration of OpenRewrite.

Analyzing the resulting code and configuration showed some dependencies which were no longer needed by Spring Boot. The obsolete dependencies may be removed manually or this step may be automated as well with SBM or OpenRewrite.

SBM currently supports Maven as OpenRewrite’s support for Gradle is not yet complete. More information on SBM can be found in the User Documentation and Developer Documentation.