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Guggenheim Starts Couchbase Inc (BASE) at Buy, ‘Capella-zing Beyond the Enterprise Base’

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Posted on nosqlgooglealerts. Visit nosqlgooglealerts

January 26, 2023 4:10 PM EST

(Updated – January 26, 2023 4:18 PM EST)

Guggenheim analyst Howard Ma initiates coverage on Couchbase Inc (NASDAQ: BASE) with a Buy rating and a price target of $20.00.

The analyst comments “We are initiating coverage of Couchbase (BASE) with a Buy rating and price target of $20, about 45% above the current share price. Couchbase has created an enterprise-grade NoSQL database with a differentiated architecture designed to power modern applications that relational databases were not designed for. To date, Couchbase has been successful penetrating large enterprises, but growth has slightly lagged overall NoSQL market growth. We believe that Capella, the recently launched, fully managed, and cloud-native version of Couchbase’s database, will be the engine to accelerate growth and is a strategy that has worked for Software peers. We estimate that if Capella ramps successfully, then subscription revenue could grow 23% in FY24 and 21% in FY25 vs. consensus at 16% growth and 18% growth, respectively. Higher Capella CAGR could result in high-20s% growth in each of FY24 and FY25. At only 2.9x EV/NTM Recurring Revenue, the potential upside far outweighs limited downside, in our view.”

For an analyst ratings summary and ratings history on Couchbase Inc click here. For more ratings news on Couchbase Inc click here.

Shares of Couchbase Inc closed at $14.01 yesterday.

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Graph Database Market Statistical Forecast, Trade Analysis 2022 –IBM, Tigergraph, Tibco …

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Posted on mongodb google news. Visit mongodb google news

A recent market research report added to repository of MR Accuracy Reports is an in-depth analysis of global Graph Database. On the basis of historic growth analysis and current scenario of Graph Database place, the report intends to offer actionable insights on global market growth projections. Authenticated data presented in report is based on findings of extensive primary and secondary research. Insights drawn from data serve as excellent tools that facilitate deeper understanding of multiple aspects of global Graph Database. This further helps user with their developmental strategy.

Click Here to Get Free Sample Copy of Graph Database Report @ https://www.mraccuracyreports.com/report-sample/506709

Global Graph Database: Top Key Players

IBM, Tigergraph, Tibco Software, Teradata, Stardog, Sparcity Technologies, Orientdb, Oracle, OpenLink Software, Ontotext, Objectivity, Neo4j, MongoDB, Microsoft, Memgraph, Marklogic, Franz, Fluree, Datastax, Cray, Cambridge Semantics, Blazegraph, Bitnine, AWS, Arangodb

This report examines all the key factors influencing growth of global Graph Database, including demand-supply scenario, pricing structure, profit margins, production and value chain analysis. Regional assessment of global Graph Database unlocks a plethora of untapped opportunities in regional and domestic market places. Detailed company profiling enables users to evaluate company shares analysis, emerging product lines, scope of NPD in new markets, pricing strategies, innovation possibilities and much more.

Product types uploaded in the Graph Database are:

RDF, Property Graph

Key applications of this report are:

BFSI, Telecom and IT, Retail and E-commerce, Healthcare and Life Sciences, Manufacturing, Government and Public, Transportation and Logistics, Energy and Utilities, Others

Global Graph Database: By Countries

United States

Canada

Germany

UK

France

Italy

Spain

Russia

China

Japan

South Korea

Australia

Thailand

Brazil

Argentina

Chile

South Africa

Egypt

UAE

Saudi Arabia

Access full Report Description, TOC, Table of Figure, Chart, etc. https://www.mraccuracyreports.com/reportdetails/reportview/506709

Graph Database: Regional analysis includes

  • Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)
  • Europe (Turkey, Germany, Russia UK, Italy, France, etc.)
  • North America (the United States, Mexico, and Canada.)
  • South America (Brazil etc.)
  • The Middle East and Africa (GCC Countries and Egypt)

Some Points from Table of Contents

Chapter 1 Toll Like Receptor 8 Introduction and Market Overview

Chapter 2 Executive Summary

Chapter 3 Industry Chain Analysis

Chapter 4 Global Graph Database, by Type

Chapter 5 Graph Database, by Application

Chapter 6 Global Graph Database Analysis by Regions

Chapter 7 North America Graph Database Analysis by Countries

Chapter 8 Europe Graph Database Analysis by Countries

Chapter 9 Asia Pacific Graph Database Analysis by Countries

Chapter 10 Middle East and Africa Graph Database Analysis by Countries

Chapter 11 South America Graph Database Analysis by Countries

Chapter 12 Competitive Landscape

Chapter 13 Industry Outlook

Chapter 14 Global Graph Database Forecast

Chapter 15 New Project Feasibility Analysis

Direct Purchase Graph Database Research Report Now @ https://www.mraccuracyreports.com/checkout/506709

Report includes Competitor’s Landscape:

➊ Major trends and growth projections by region and country
➋ Key winning strategies followed by the competitors
➌ Who are the key competitors in this industry?
➍ What shall be the potential of this industry over the forecast tenure?
➎ What are the factors propelling the demand for the Toll Like Receptor 8?
➏ What are the opportunities that shall aid in significant proliferation of the market growth?
➐ What are the regional and country wise regulations that shall either hamper or boost the demand for Toll Like Receptor 8?
➑ How has the covid-19 impacted the growth of the market?
➒ Has the supply chain disruption caused changes in the entire value chain?

Thank you for taking the time to read our article…!!

ABOUT US:

Mr Accuracy Reports is an ESOMAR-certified business consulting & market research firm, a member of the Greater New York Chamber of Commerce and is headquartered in Canada. A recipient of Clutch Leaders Award 2022 on account high client score (4.9/5), we have been collaborating with global enterprises in their business transformation journey and helping them deliver on their business ambitions. 90% of the largest Forbes 1000 enterprises are our clients. We serve global clients across all leading & niche market segments across all major industries.

Mr Accuracy Reports is a global front-runner in the research industry, offering customers contextual and data-driven research services. Customers are supported in creating business plans and attaining long-term success in their respective marketplaces by the organization. The industry provides consulting services, Mr Accuracy Reports research studies, and customized research reports.

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

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Investors Buy Large Volume of MongoDB Call Options (NASDAQ:MDB) – ETF Daily News

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Posted on mongodb google news. Visit mongodb google news

MongoDB, Inc. (NASDAQ:MDBGet Rating) was the target of some unusual options trading activity on Wednesday. Investors acquired 36,130 call options on the stock. This represents an increase of 2,077% compared to the average daily volume of 1,660 call options.

Analysts Set New Price Targets

MDB has been the topic of several recent research reports. Credit Suisse Group lowered their price objective on shares of MongoDB from $400.00 to $305.00 and set an “outperform” rating on the stock in a research note on Wednesday, December 7th. Canaccord Genuity Group decreased their target price on shares of MongoDB from $300.00 to $270.00 and set a “buy” rating for the company in a report on Wednesday, December 7th. JMP Securities upgraded shares of MongoDB from a “market perform” rating to an “outperform” rating and set a $215.00 target price for the company in a report on Wednesday, December 7th. UBS Group raised their price target on shares of MongoDB from $200.00 to $215.00 and gave the stock a “buy” rating in a report on Wednesday, December 7th. Finally, Morgan Stanley raised their price target on shares of MongoDB from $215.00 to $230.00 and gave the stock an “equal weight” rating in a report on Wednesday, December 7th. Three analysts have rated the stock with a hold rating and nineteen have issued a buy rating to the stock. Based on data from MarketBeat.com, the stock has an average rating of “Moderate Buy” and an average target price of $266.90.

MongoDB Trading Down 2.4 %

Shares of MDB stock opened at $195.14 on Thursday. MongoDB has a 1 year low of $135.15 and a 1 year high of $471.96. The firm’s fifty day simple moving average is $182.29 and its 200 day simple moving average is $226.72. The firm has a market capitalization of $13.52 billion, a P/E ratio of -36.34 and a beta of 0.89. The company has a current ratio of 4.10, a quick ratio of 4.10 and a debt-to-equity ratio of 1.66.

Want More Great Investing Ideas?

MongoDB (NASDAQ:MDBGet Rating) last announced its earnings results on Tuesday, December 6th. The company reported ($1.23) earnings per share (EPS) for the quarter, topping analysts’ consensus estimates of ($1.48) by $0.25. MongoDB had a negative net margin of 30.73% and a negative return on equity of 52.50%. The business had revenue of $333.62 million for the quarter, compared to analyst estimates of $302.39 million. As a group, sell-side analysts predict that MongoDB will post -4.65 EPS for the current year.

Insider Activity

In other news, CTO Mark Porter sold 635 shares of the company’s stock in a transaction on Tuesday, January 3rd. The shares were sold at an average price of $187.72, for a total value of $119,202.20. Following the completion of the transaction, the chief technology officer now directly owns 27,577 shares in the company, valued at $5,176,754.44. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available through the SEC website. In other MongoDB news, CTO Mark Porter sold 635 shares of the stock in a transaction on Tuesday, January 3rd. The shares were sold at an average price of $187.72, for a total transaction of $119,202.20. Following the transaction, the chief technology officer now directly owns 27,577 shares in the company, valued at approximately $5,176,754.44. The transaction was disclosed in a legal filing with the Securities & Exchange Commission, which is available at this hyperlink. Also, CRO Cedric Pech sold 328 shares of the stock in a transaction on Tuesday, January 3rd. The shares were sold at an average price of $199.31, for a total transaction of $65,373.68. Following the transaction, the executive now owns 33,829 shares in the company, valued at $6,742,457.99. The disclosure for this sale can be found here. Over the last three months, insiders sold 58,074 shares of company stock worth $11,604,647. 5.70% of the stock is currently owned by company insiders.

Hedge Funds Weigh In On MongoDB

A number of institutional investors and hedge funds have recently bought and sold shares of the stock. Prentice Wealth Management LLC acquired a new stake in MongoDB during the second quarter worth $26,000. Venture Visionary Partners LLC acquired a new stake in MongoDB during the second quarter worth $28,000. Sentry Investment Management LLC acquired a new stake in MongoDB during the third quarter worth $33,000. Lindbrook Capital LLC boosted its stake in MongoDB by 350.0% during the fourth quarter. Lindbrook Capital LLC now owns 171 shares of the company’s stock worth $34,000 after buying an additional 133 shares in the last quarter. Finally, Alta Advisers Ltd acquired a new stake in MongoDB during the third quarter worth $40,000. Institutional investors own 84.86% of the company’s stock.

MongoDB Company Profile

(Get Rating)

MongoDB, Inc engages in the development and provision of a general-purpose database platform. The firm’s products include MongoDB Enterprise Advanced, MongoDB Atlas and Community Server. It also offers professional services including consulting and training. The company was founded by Eliot Horowitz, Dwight A.

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How to Assess Software Quality

MMS Founder
MMS Ben Linders

Article originally posted on InfoQ. Visit InfoQ

The quality practices assessment model (QPAM) can be used to classify a team’s exhibited behavior into four dimensions: Beginning, Unifying, Practicing, and Innovating. It explores social and technical quality aspects like feedback loops, culture, code quality and technical debt, and deployment pipeline.

Janet Gregory spoke about assessing quality using this model at Agile Testing Days 2022.

The quality practices assessment model has ten quality aspects, described in Helping Team Deliver With a Quality Practices Assessment Model.

The behaviour exhibited by teams for each quality aspect, falls into one of four dimensions: Beginning, Unifying, Practicing and Innovating. This does not mean that every quality aspect for a team falls into the same dimension, Gregory mentioned.

Teams in the Beginning dimension have few quality practices in place and lack structure, Gregory explained:

Low-quality code is deployed to production, defects are logged, and the invisible backlog of defects grows. Not all teams are in the same place, some will be more chaotic than others, but pretty much every team knows they want to improve.

In the Unifying dimension, the organization has adopted one or more agile methods forming cross-functional delivery teams:

The teams follow rituals like having daily standups, keeping a product backlog that they regularly refine, or time-boxing their work into iterations. They try to take smaller chunks of work that they can finish by the end of each iteration and are learning to work together as a cross-functional team.

In Practicing, team members feel good because the practices they have learned feel natural, and they consistently deliver value to their customers:

Teams have developed fast and effective feedback loops to pivot quickly when needed. The emphasis is on preventing code defects, so few are found. Those found early are fixed immediately or as high priority in the next iteration. They build quality into the product from the beginning by bringing testing activities forward early in the cycle, and use feedback from their customers to improve their product.

Innovating teams are high performing. Their cycle time is short with customer and business value delivered frequently:

The team knows their market and has high quality defined in identified aspects. They experiment where appropriate and adapt their practices. Self-learning and self-discipline are the norms, with the team consistently striving to learn and improve. Because psychological safety is high, failure is seen as a learning opportunity. The feature development is focused on flow but is thoughtful and based on value to the customer. The team understands and monitors the impact of changes using continual feedback from production usage. Quality is built-in from the start.

The hard part is consolidating all the information gathered from the different sources and to figure out discrepancies, Gregory explained:

I like to use a spreadsheet with the different quality aspects and the practices that go with them. That makes it easier for me to compare the findings with each of the dimensions.

The model is only as good as the person using it, and facilitation is a skill, Gregory mentioned. Often when teams try to self-assess, they rank themselves higher than others may see them. That doesn’t mean it’s not a good exercise for teams to try, Gregory said.

The quality practices assessment model is described in the book Assessing Agile Quality Practices with QPAM which Janet co-authored with Selena Delesie and is listed on Gregory’s publications page.

InfoQ interviewed Janet Gregory about assessing quality.

InfoQ: What tips do you have for assessment facilitators?

Janet Gregory: There are many ways to get information. I use all I can – a combination of process retrospectives, interviewing, observing meetings or workshops, and examining artifacts like user stories and tests.

In our book, we list open-ended questions for facilitators to use. A facilitator needs to listen and observe carefully to be able to extract the information – often, what is not said is as important as what is said.

We are creating a follow-up book as a guide for facilitators which will help anyone conducting the assessment – no promises when, but hopefully in the first half of 2023.

InfoQ: How can we present the results of an assessment?

Gregory: What a facilitator shares will depend on the context, but it is important that the information is anonymous.

If you are an internal facilitator, you likely will gather all your observations, and share what you found so the team can choose what to improve on.

If you are an external facilitator (like I am), you will likely share observations and provide suggestions and recommendations.

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Article: The Six Ways of Optimizing WebAssembly

MMS Founder
MMS Matt Butcher Radu Matei

Article originally posted on InfoQ. Visit InfoQ

Key Takeaways

  • While many languages support Wasm, some are faster than others.
  • Some compilers natively support optimizing Wasm for efficiency and speed.
  • The wasm-opt tool can optimize a Wasm binary regardless of the original language it was used to create it.
  • Using a JIT-enabled runtime can improve runtime performance depending on the hardware platform you are using.
  • Some Wasm runtimes can even compile applications ahead-of-time (AOT) to reach native execution speed.
  • The experimental Wizer project achieves a further performance boost by pre-initializing a Wasm binary to reduce the time it takes to launch it.
  • In our practice, we saw good optimization can reduce Wasm binary size by a factor of ten.

WebAssembly (often abbreviated to Wasm) is a binary executable format. Many different languages can be executed via Wasm, including Rust, C, JavaScript, Python, Ruby, and the .NET languages.

Additionally, Wasm can run on a huge range of hardware and operating systems. The specification is designed to be fast, compact, and above all secure. 

In 2022, Wasm has cropped up in many different contexts. While it was originally designed for the browser, it turns out to be useful for embedded programming, plugins, cloud, and edge computing.

One thing these different use cases have in common is that performance is tremendously important. Since loading an executable quickly is part of performance, file size often has a direct impact on raw performance.

In this article, we’ll look at six ways to optimize Wasm for performance and file size.

Language Choice

Each programming language has its own nuances, and one of those is how large a runtime the language requires in order to execute. On the lightweight side, low-level system languages like C and Rust require small runtime overhead.

Other compiled languages like Swift bring a hefty runtime along for the ride. A Swift binary may be substantially larger simply because it includes a lot of built-in behavior. Java and .NET also tend to bring larger binary sizes for a similar reason.

To illustrate, let’s take a look at a “hello world” program in Rust and in Swift.

In Rust, a basic “hello world” program looks like this:

fn main() {
    println!("Hello, world!");
}

Compiled with cargo build —target wasm32-wasi, this binary is 2.0M. (This is an unoptimized binary. We’ll return to this file size later.)

Here’s a similar program in Swift: 

print("Hello, World!n")

Compiling this to Wasm  with the Swiftwasm project using the command swiftc -target wasm32-unknown-wasi hello.swift -o hello.wasm produces a 9.1M image. That makes the Swift version over 4x larger than the equivalent Rust version.

So which language you choose will impact the file sizes of your binaries as well as the startup time  (at least to some degree). This is not the final word on file sizes, though. There are ways to optimize the binary sizes further.

Using Compiler Flags to Optimize

Some compilers offer built-in compiler flags that can optimize the binaries they produce. Long-time C and C++ users are accustomed to this. And new languages like Rust and Zig also provide optimization options.

In the previous section, we looked at a simple three-line Rust program. When we compiled it with the default cargocommand, it produced a 2.0M binary. But by adding another flag, we can trim that size down: cargo build --target wasm32-wasi --release. This produces a 1.9M binary. On a small program like this, and with Rust’s svelte runtime, not much can be shaved off. On bigger projects, though, the –release flag can drastically reduce the file size. For example, compiling the Bartholomew CMS without the release flag yields an 84M binary, while using the release flag reduces it to 7M. That’s a huge savings. 

Rust’s release target does more than merely reduce the file size. It can also speed up execution because it removes symbols that are used by debuggers and analysis tools. This is almost always a worthwhile feature when you are running code in production. Launching the full 84M version of Bartholomew may take up to a second to execute, but that reduces to a mere couple milliseconds when using the optimized version.

Optimizing Size with wasm-opt

In the above section, we saw how some compilers provide optimization flags. But not all of them do. Furthermore, even compilers that can produce some optimizations might not aggressively optimize.

Wasm optimization tools can perform robust analysis of a Wasm binary and further optimize the file size and even the performance characteristics of a Wasm executable. The Binaryen project provides a number of command line tools for working with Wasm, including the wasm-opt optimizer.

Before, we looked at a Swift program that was 9.1M in size.  Let’s take a look at what happens when we run wasm-opt -O hello.wasm -o hello-optimized.wasm. This command will produce an optimized binary named hello-optimized.wasm. The resulting size is 4.0M, a reduction of over 50%.

The wasm-opt tool performs dozens of optimizations on a binary, ranging from removing duplicate code to re-organizing the code. Code, here, means the Wasm instructions, not the source code you edit. So running wasm-opt won’t change the source Swift code. It just rewrites the Wasm binary. While optimizing this way definitely cuts down the file size, it also improves runtime performance. On my system, the optimized “hello world” program executed twice as fast as the unoptimized one.

Indeed, wasm-opt can even further optimize the already-optimized Rust code. Running it on the 1.9M Rust binary from the previous section generates an even more compact 1.6M binary. In such a simple case performance did not improve. Both run in a tenth of a second. But larger Rust binaries likely also gain speed improvements with wasm-opt.

The Runtime Matters

Wasm is a flexible binary format. It can be executed by an interpreter such as wasm3, which will read and execute small chunks of the code in sequence. But other Wasm runtimes like Wasmtime use a technology called JIT (Just-In-Time) compiling to speed up execution.

For small programs, like our “hello world” examples, or on devices with constrained resources such as a Raspberry Pi, an interpreter is often desirable since it does the least amount of work and uses the fewest resources.

But for larger programs like the Bartholomew CMS, a JIT-style runtime will outperform an interpreter. The reason for this discrepancy is that a JIT compiler does extra work at startup and during early execution in order to optimize the in-memory representation of the program. And this optimization shows up as the code continues to run. Because the JIT process takes time, though, this can appear to be a performance penalty for small programs that only run for a moment.

How do you choose? The traditional rule of thumb is this: If you are running on a constrained device smaller than a Raspberry Pi, use an interpreter. Otherwise, favor a JIT-enabled runtime.

When it comes to runtimes, there’s one more trick.

Ahead-Of-Time (AOT) Compiling

A JIT runtime performs in-memory optimizations at startup time. But what if we could perform optimizations once, write those optimizations back out to disk, and then take advantage of those optimizations the next time the program is run? This strategy is called Ahead-Of-Time (AOT) compiling.

There’s a big drawback to AOT compiling: the optimizations done during this stage are different-in-kind than the ones we saw earlier with wasm-opt. With AOT, optimizations are machine-specific. These optimizations take into account operating system and processor architecture, which means once we perform these optimizations, our Wasm binary is no longer portable. Furthermore, each runtime has its own format for these optimizations, so a program AOT-compiled with one Wasm runtime will no longer be runnable by other Wasm runtimes.

The Wasmtime runtime can compile a Wasm module to an AOT format. For example, we can run wasmtime compile hello.wasm to compile our Swift example. This will produce a new file named hello.cwasm that can be executed by Wasmtime.

Again, for trivial programs like our “hello world” example, AOT compiling will not have a large benefit. But when working with non-trivial programs, AOT compiling will achieve higher performance numbers than either interpreter or JIT-enabled runs. Note, however, that most AOT compilers produce binaries that may be larger than their Wasm equivalent because many elements of the Wasm runtime itself are compiled into the binary to improve performance.

There is a very specific rule of thumb for knowing when to use an AOT compiler for Wasm: only use it when you know that the program will only ever be run with exactly the same configuration of Wasm runtime, operating system, and architecture. Wasm modules should be distributed in their normal Wasm form, and only AOT-compiled at or after the installation step.

Pre-Initializing a Binary

The fifth and final optimization technique is the most peculiar of the lot. Wasm is a stack-based virtual machine, and at any given time it can be stopped and even written out to disk, to be resumed later. (There are a few limitations to this, but these limitations are not important here.) This feature of Wasm has an interesting application.

Sometimes there are parts of your code that you need to run every single time on startup. This code may do banal things like setting the default value of a variable or creating an instance of a data structure. Every time the code is run, this same bit of initialization logic must be performed. And with each run, the resulting state of the program is the same. The variable is initialized to the same value, or the data structure is initialized into the same state.

What if there was a way to run that first initialization, then freeze the Wasm state and write it back out to disk? Then the next time the program is executed, it wouldn’t have to run the initialization step. That would already be done!

This is the idea behind the Wizer project. Wizer provides a way to annotate your code with initialization blocks that can then be executed once and then written out to a new post-initialization Wasm binary. Unlike AOT compiling, the resulting binary is still a plain old Wasm binary, so this technique is portable.

Wizer can be a little finicky to use. But systems like .NET can benefit greatly from Wizer.

Bringing It All Together

Based on our experience at Fermyon, optimization is important for both developer tools and Cloud runtime, but the two cases differ substantially. 

On the developer side, the best practice is to use as many optimization tools as your compiler gives you. For example, we always use the —release flag when compiling our Rust code. Our open source Spin tool, which allows developers to build WebAssembly microservices and web applications using several languages, includes these optimizations in per-language templates. We have also found including wasm-opt in the local compile pass to be useful, especially with languages that have a large runtime.

During the development process, we use a JIT-enabled runtime. There is little value in AOT compiling during the development phase.

The server side is different. For example, our SaaS-based Wasm runtime platform, Fermyon Cloud, only accepts Wasm binaries, but when it deploys them to the cloud cluster, those binaries are AOT-compiled. This is possible in a reliable way because that is the moment we know exactly what the host runtime’s configuration is. If the Wasm file is deployed to an Arm64 system, it can be AOT-compiled accordingly, without the concern that it will be executed on an Intel architecture.

When it comes to Wizer, we really only use it in the case of .NET, which benefits tremendously from this optimization.

Conclusion

We’ve picked our way through six different ways of optimizing Wasm for performance and for file size. Each method has pros and cons, and many of these methods can be combined for added benefit. Employing these techniques for production Wasm environments can be beneficial.

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Stock Traders Purchase High Volume of Call Options on MongoDB (NASDAQ:MDB)

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

MongoDB, Inc. (NASDAQ:MDBGet Rating) was the target of unusually large options trading activity on Wednesday. Stock investors purchased 36,130 call options on the company. This is an increase of 2,077% compared to the average daily volume of 1,660 call options.

Wall Street Analyst Weigh In

MDB has been the topic of a number of recent analyst reports. Canaccord Genuity Group cut their price target on shares of MongoDB from $300.00 to $270.00 and set a “buy” rating on the stock in a report on Wednesday, December 7th. Tigress Financial lowered their price target on shares of MongoDB from $575.00 to $365.00 and set a “buy” rating for the company in a research report on Thursday, December 15th. Credit Suisse Group decreased their target price on shares of MongoDB from $400.00 to $305.00 and set an “outperform” rating for the company in a report on Wednesday, December 7th. KeyCorp initiated coverage on shares of MongoDB in a report on Monday, November 21st. They set an “overweight” rating and a $215.00 price objective for the company. Finally, Barclays boosted their price objective on shares of MongoDB from $233.00 to $240.00 and gave the company an “overweight” rating in a report on Wednesday, December 7th. Three analysts have rated the stock with a hold rating and nineteen have given a buy rating to the company’s stock. Based on data from MarketBeat, the company has a consensus rating of “Moderate Buy” and a consensus price target of $266.90.

Insiders Place Their Bets

In other news, CRO Cedric Pech sold 328 shares of the stock in a transaction that occurred on Tuesday, January 3rd. The shares were sold at an average price of $199.31, for a total transaction of $65,373.68. Following the completion of the transaction, the executive now directly owns 33,829 shares of the company’s stock, valued at approximately $6,742,457.99. The transaction was disclosed in a legal filing with the Securities & Exchange Commission, which can be accessed through this hyperlink. In other MongoDB news, CRO Cedric Pech sold 328 shares of the stock in a transaction that occurred on Tuesday, January 3rd. The shares were sold at an average price of $199.31, for a total value of $65,373.68. Following the sale, the executive now directly owns 33,829 shares in the company, valued at approximately $6,742,457.99. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is accessible through the SEC website. Also, CFO Michael Lawrence Gordon sold 2,060 shares of the stock in a transaction that occurred on Tuesday, January 3rd. The stock was sold at an average price of $199.31, for a total transaction of $410,578.60. Following the completion of the sale, the chief financial officer now owns 88,302 shares in the company, valued at $17,599,471.62. The disclosure for this sale can be found here. In the last ninety days, insiders sold 58,074 shares of company stock valued at $11,604,647. Corporate insiders own 5.70% of the company’s stock.

Institutional Investors Weigh In On MongoDB

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A number of hedge funds have recently bought and sold shares of the business. abrdn plc raised its stake in MongoDB by 0.6% during the second quarter. abrdn plc now owns 6,745 shares of the company’s stock valued at $1,750,000 after purchasing an additional 41 shares in the last quarter. Carnegie Capital Asset Management LLC raised its stake in shares of MongoDB by 1.9% in the second quarter. Carnegie Capital Asset Management LLC now owns 2,310 shares of the company’s stock valued at $599,000 after acquiring an additional 43 shares during the period. Lindbrook Capital LLC raised its stake in shares of MongoDB by 41.5% in the second quarter. Lindbrook Capital LLC now owns 174 shares of the company’s stock valued at $45,000 after acquiring an additional 51 shares during the period. CWM LLC raised its stake in shares of MongoDB by 2.6% in the third quarter. CWM LLC now owns 2,144 shares of the company’s stock valued at $426,000 after acquiring an additional 55 shares during the period. Finally, Allworth Financial LP raised its stake in shares of MongoDB by 12.9% in the fourth quarter. Allworth Financial LP now owns 508 shares of the company’s stock valued at $100,000 after acquiring an additional 58 shares during the period. Hedge funds and other institutional investors own 84.86% of the company’s stock.

MongoDB Price Performance

MDB stock opened at $195.14 on Thursday. MongoDB has a one year low of $135.15 and a one year high of $471.96. The company’s fifty day moving average price is $182.29 and its 200-day moving average price is $226.72. The company has a debt-to-equity ratio of 1.66, a quick ratio of 4.10 and a current ratio of 4.10. The company has a market capitalization of $13.52 billion, a PE ratio of -36.34 and a beta of 0.89.

MongoDB (NASDAQ:MDBGet Rating) last released its earnings results on Tuesday, December 6th. The company reported ($1.23) EPS for the quarter, topping the consensus estimate of ($1.48) by $0.25. The firm had revenue of $333.62 million for the quarter, compared to the consensus estimate of $302.39 million. MongoDB had a negative net margin of 30.73% and a negative return on equity of 52.50%. On average, equities analysts anticipate that MongoDB will post -4.65 EPS for the current fiscal year.

MongoDB Company Profile

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MongoDB, Inc engages in the development and provision of a general-purpose database platform. The firm’s products include MongoDB Enterprise Advanced, MongoDB Atlas and Community Server. It also offers professional services including consulting and training. The company was founded by Eliot Horowitz, Dwight A.

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MongoDB, Inc. (NASDAQ:MDB) Receives Consensus Rating of “Moderate Buy” from Analysts

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MongoDB, Inc. (NASDAQ:MDBGet Rating) has earned a consensus recommendation of “Moderate Buy” from the twenty-two ratings firms that are covering the company, Marketbeat reports. Three investment analysts have rated the stock with a hold recommendation and sixteen have issued a buy recommendation on the company. The average 12 month price target among brokerages that have issued ratings on the stock in the last year is $270.75.

A number of analysts recently issued reports on the stock. Canaccord Genuity Group dropped their target price on shares of MongoDB from $300.00 to $270.00 and set a “buy” rating for the company in a research report on Wednesday, December 7th. Tigress Financial lowered their price objective on shares of MongoDB from $575.00 to $365.00 and set a “buy” rating for the company in a research report on Thursday, December 15th. Credit Suisse Group lowered their price objective on shares of MongoDB from $400.00 to $305.00 and set an “outperform” rating for the company in a research report on Wednesday, December 7th. Citigroup increased their price objective on shares of MongoDB from $295.00 to $300.00 in a research report on Wednesday, December 7th. Finally, Truist Financial decreased their price target on shares of MongoDB from $300.00 to $235.00 in a research report on Monday, January 9th.

Insider Buying and Selling

In other MongoDB news, Director Hope F. Cochran sold 1,175 shares of the firm’s stock in a transaction dated Thursday, December 15th. The stock was sold at an average price of $208.65, for a total transaction of $245,163.75. Following the completion of the sale, the director now directly owns 7,674 shares in the company, valued at approximately $1,601,180.10. The sale was disclosed in a filing with the Securities & Exchange Commission, which is available at this hyperlink. In other news, CEO Dev Ittycheria sold 39,382 shares of MongoDB stock in a transaction dated Tuesday, January 3rd. The stock was sold at an average price of $199.96, for a total transaction of $7,874,824.72. Following the completion of the sale, the chief executive officer now directly owns 190,264 shares in the company, valued at approximately $38,045,189.44. The sale was disclosed in a filing with the SEC, which can be accessed through this link. Also, Director Hope F. Cochran sold 1,175 shares of MongoDB stock in a transaction dated Thursday, December 15th. The shares were sold at an average price of $208.65, for a total value of $245,163.75. Following the sale, the director now owns 7,674 shares of the company’s stock, valued at $1,601,180.10. The disclosure for this sale can be found here. Insiders have sold a total of 58,074 shares of company stock valued at $11,604,647 over the last three months. Company insiders own 5.70% of the company’s stock.

Institutional Investors Weigh In On MongoDB

Institutional investors and hedge funds have recently added to or reduced their stakes in the stock. Prentice Wealth Management LLC acquired a new stake in MongoDB in the 2nd quarter worth approximately $26,000. Venture Visionary Partners LLC acquired a new stake in MongoDB in the 2nd quarter worth approximately $28,000. UMB Bank n.a. lifted its holdings in MongoDB by 422.6% in the 2nd quarter. UMB Bank n.a. now owns 162 shares of the company’s stock worth $42,000 after purchasing an additional 131 shares during the last quarter. Sentry Investment Management LLC acquired a new stake in MongoDB in the 3rd quarter worth approximately $33,000. Finally, Lindbrook Capital LLC lifted its holdings in MongoDB by 350.0% in the 4th 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. 84.86% of the stock is currently owned by institutional investors and hedge funds.

MongoDB Trading Down 2.4 %

Shares of MDB stock opened at $195.14 on Thursday. The firm has a market cap of $13.52 billion, a P/E ratio of -36.34 and a beta of 0.89. MongoDB has a 1-year low of $135.15 and a 1-year high of $471.96. The company has a debt-to-equity ratio of 1.66, a current ratio of 4.10 and a quick ratio of 4.10. The firm’s 50-day simple moving average is $182.29 and its 200-day simple moving average is $226.72.

MongoDB (NASDAQ:MDBGet Rating) last announced its quarterly earnings data on Tuesday, December 6th. The company reported ($1.23) earnings per share for the quarter, topping the consensus estimate of ($1.48) by $0.25. The business had revenue of $333.62 million for the quarter, compared to analyst estimates of $302.39 million. MongoDB had a negative net margin of 30.73% and a negative return on equity of 52.50%. As a group, equities analysts forecast that MongoDB will post -4.65 EPS for the current year.

MongoDB Company Profile

(Get Rating)

MongoDB, Inc engages in the development and provision of a general-purpose database platform. The firm’s products include MongoDB Enterprise Advanced, MongoDB Atlas and Community Server. It also offers professional services including consulting and training. The company was founded by Eliot Horowitz, Dwight A.

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Analyst Recommendations for MongoDB (NASDAQ:MDB)



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MongoDB, Inc. (NASDAQ:MDB) Receives $270.75 Average Price Target from Analysts

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Shares of MongoDB, Inc. (NASDAQ:MDBGet Rating) have received a consensus recommendation of “Moderate Buy” from the twenty-two ratings firms that are currently covering the firm, Marketbeat.com reports. Three research analysts have rated the stock with a hold rating and sixteen have issued a buy rating on the company. The average 12 month target price among brokerages that have updated their coverage on the stock in the last year is $270.75.

Several brokerages have recently weighed in on MDB. Robert W. Baird increased their price objective on shares of MongoDB from $205.00 to $230.00 in a report on Wednesday, December 7th. Piper Sandler cut their price objective on shares of MongoDB from $350.00 to $270.00 and set an “overweight” rating on the stock in a report on Thursday, October 20th. Truist Financial cut their price objective on shares of MongoDB from $300.00 to $235.00 in a report on Monday, January 9th. Wedbush assumed coverage on shares of MongoDB in a research report on Wednesday, December 14th. They set an “outperform” rating and a $240.00 target price for the company. Finally, Morgan Stanley increased their target price on shares of MongoDB from $215.00 to $230.00 and gave the stock an “equal weight” rating in a research report on Wednesday, December 7th.

MongoDB Price Performance

NASDAQ:MDB opened at $195.14 on Thursday. The firm has a market cap of $13.52 billion, a price-to-earnings ratio of -36.34 and a beta of 0.89. The company has a current ratio of 4.10, a quick ratio of 4.10 and a debt-to-equity ratio of 1.66. The company’s 50-day moving average is $182.29 and its two-hundred day moving average is $226.72. MongoDB has a 12-month low of $135.15 and a 12-month high of $471.96.

MongoDB (NASDAQ:MDBGet Rating) last released its earnings results on Tuesday, December 6th. The company reported ($1.23) earnings per share (EPS) for the quarter, beating analysts’ consensus estimates of ($1.48) by $0.25. The company had revenue of $333.62 million for the quarter, compared to analyst estimates of $302.39 million. MongoDB had a negative net margin of 30.73% and a negative return on equity of 52.50%. Research analysts expect that MongoDB will post -4.65 EPS for the current year.

Insider Buying and Selling at MongoDB

In other news, CTO Mark Porter sold 635 shares of the firm’s stock in a transaction on Tuesday, January 3rd. The stock was sold at an average price of $187.72, for a total transaction of $119,202.20. Following the transaction, the chief technology officer now directly owns 27,577 shares of the company’s stock, valued at $5,176,754.44. The sale was disclosed in a filing with the Securities & Exchange Commission, which is available through the SEC website. In related news, CTO Mark Porter sold 635 shares of MongoDB stock in a transaction dated Tuesday, January 3rd. The stock was sold at an average price of $187.72, for a total transaction of $119,202.20. Following the transaction, the chief technology officer now directly owns 27,577 shares of the company’s stock, valued at $5,176,754.44. The transaction was disclosed in a filing with the SEC, which is available through this hyperlink. Also, CFO Michael Lawrence Gordon sold 2,060 shares of MongoDB stock in a transaction dated Tuesday, January 3rd. The stock was sold at an average price of $199.31, for a total value of $410,578.60. Following the transaction, the chief financial officer now directly owns 88,302 shares in the company, valued at $17,599,471.62. The disclosure for this sale can be found here. Over the last three months, insiders have sold 58,074 shares of company stock worth $11,604,647. 5.70% of the stock is owned by insiders.

Institutional Investors Weigh In On MongoDB

Institutional investors have recently bought and sold shares of the business. Prentice Wealth Management LLC acquired a new stake in shares of MongoDB during the 2nd quarter valued at about $26,000. Venture Visionary Partners LLC acquired a new stake in shares of MongoDB during the 2nd quarter valued at about $28,000. UMB Bank n.a. lifted its holdings in shares of MongoDB by 422.6% during the 2nd quarter. UMB Bank n.a. now owns 162 shares of the company’s stock valued at $42,000 after buying an additional 131 shares during the last quarter. Sentry Investment Management LLC acquired a new stake in shares of MongoDB during the 3rd quarter valued at about $33,000. Finally, Lindbrook Capital LLC lifted its holdings in shares of MongoDB by 350.0% during the 4th quarter. Lindbrook Capital LLC now owns 171 shares of the company’s stock valued at $34,000 after buying an additional 133 shares during the last quarter. 84.86% of the stock is currently owned by institutional investors and hedge funds.

MongoDB Company Profile

(Get Rating)

MongoDB, Inc engages in the development and provision of a general-purpose database platform. The firm’s products include MongoDB Enterprise Advanced, MongoDB Atlas and Community Server. It also offers professional services including consulting and training. The company was founded by Eliot Horowitz, Dwight A.

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Analyst Recommendations for MongoDB (NASDAQ:MDB)



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IOTA Distributed Ledger: Beyond Blockchain for Supply Chains – The New Stack

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IOTA Distributed Ledger: Beyond Blockchain for Supply Chains – The New Stack

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2023-01-25 11:55:14

IOTA Distributed Ledger: Beyond Blockchain for Supply Chains

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Explore how the IOTA Foundation is tackling supply chain digitization in East Africa, including the role of open source distributed ledgers and NoSQL.


Jan 25th, 2023 11:55am by


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The IOTA Foundation, the organization behind the IOTA open source distributed ledger technology built for the Internet of Things, envisions a future where every single trade item in the global supply chain is tracked and its provenance sourced using distributed ledgers. This vision is already becoming a reality in East Africa, thanks to the collaboration of the IOTA Foundation and TradeMark East Africa (TMEA). These organizations have teamed up to address the challenge of digitizing the export process for Kenya’s flower exporters, airlines and freight forwarders.

TMEA found that for just a single transaction, an African entrepreneur was completing an average of 200 communications, including 96 paper documents. The system developed by the IOTA Foundation and TMEA anchors the key trade documents on the Tangle, a new type of distributed ledger technology different from the traditional blockchain model, and shares them with customs in destination countries. This expedites the export process and makes African companies more competitive globally.

What’s behind this initiative from a technology perspective? That’s what José Manuel Cantera, technical analyst and project Lead at IOTA Foundation, recently shared. From a bird’s-eye view, it involves using:

  • EPCIS 2.0 data serialization formats for data interoperability
  • IOTA distributed ledgers to register every event happening within supply chains
  • ScyllaDB NoSQL for scalable, resilient persistent storage

Let’s dive into the details with a close look at two specific use cases: cross-border trade and end-to-end supply chain traceability. But first, Cantera’s perspective on the technical challenges associated with supply chain digitization.

Cantera crafted this talk for ScyllaDB Summit, a virtual conference for exploring what’s needed to power instantaneous experiences with massive distributed datasets. Register now (free + virtual) to join us live for ScyllaDB Summit 2023 featuring experts from Discord, Hulu, Strava, Epic Games, ScyllaDB and more, plus industry leaders on the latest in WebAssembly, Rust, NoSQL, SQL and event streaming trends. 

Supply Chain Digitization: Top Technical Challenges

Cantera began ins by introducing three of the most pressing technical challenges associated with supply chain digitization.

First, there are multiple actors and systems generating data and integrating data across the supply chain — and verifying the identity of each is critical. Suppliers, OEMs, food processors, brands, recycling agents, consumers, ports, carriers, ground transporters, inspectors/authorities, freight forwarders, customs, dealers, repairers, etc. are all involved, and all must be verified.

Second, there are multiple relationships across all these actors, and these relationships cross borders with no central anchor and no single source of truth. In addition to business-to-business and business-to-consumer, there are also business-to-government and government-to-government relationships.

Third, there are different functional needs related to maintaining trust between the different actors through verifiable data. Traceability is key here. It’s an enabler for compliance, product authenticity, transparency and provenance with a view to different kinds of applications. For example, traceability is essential for ethical sourcing, food safety and effective recalls.

Use Case 1: Cross-Border Trade

For his first example, Cantera turns to cross-border trade operations.

“This is a multilayered domain, and there are many different problems that have to be solved in different places,” he warns before sharing a diagram that reins in the enormous complexity of the situation:

The key flows here are:

  • Financial procedures: The pure financial transaction between the two parties
  • Trade procedures: Any kind of document related to a commercial transaction
  • Transportation procedures: All the details about transporting the goods
  • Regulator procedures: The many different documents that must be exchanged between importers and exporters, as well with the public authorities in the business-to-government relationships

So how is the IOTA Foundation working to optimize this complex and multilayered domain? Cantera explains, “We are allowing different actors, different government agencies and the private actors (traders) to share documents and to verify documents in one shot. Whenever a consignment moves between East Africa and Europe, all the trade certificates, all the documents can be verified in one shot by the different actors, and the authenticity and the provenance of the documents can be traced properly. And as a result, the agility of the trade processes is improved. It’s more efficient and more effective.”

All the actors in the flow visualized above are sharing the documents through the infrastructure provided by the IOTA distributed ledger using an architecture that’s detailed after the second use case below.

Use Case 2: End-to-End Supply Chain Traceability

In addition to tackling document sharing and verification for cross-border trade, there’s another challenge: tracing the provenance of the trade items. Cantera emphasizes that when we think about traceability, we need to think about the definition of traceability given by the United Nations: “The ability to identify and trace the history, distribution, location and application of products, parts and materials, to ensure the reliability of sustainability claims, in the areas of human rights, labor (including health and safety), the environment and anti-corruption.”

In principle, traceability implies the ability to follow history. In the case of trade items, this means knowing what has been happening with that particular trade item — not only its transportation, but also its origin. If one of the parties involved in the supply chain is making a claim about sustainability, safety, etc., the validity of that claim must be verifiable.

For example, consider a seemingly simple bag of potato chips. A farmer sells potatoes to a food processor, who turns the potatoes into a bag of potato chips. When growing the potatoes, the farmer used a fertilizer, which was produced by another manufacturer and contained raw materials from a different farmer. And when converting potatoes into potato chips, the food processor uses oils that stem from yet another source. And so on and so on. The history of all these things — the potatoes, the fertilizer, the oils, the bag containing the chips, and so on — needs to be known for traceability on that bag of potato chips.

All these details — from when the potatoes were harvested to the fertilizer used, where that fertilizer came from, and so forth — are all considered critical events. And each of these critical tracking events has key data elements that describe who, what, when, where, why and even how.

How IOTA Addressed the Top Technical Challenges

The IOTA Foundation applied several core technologies to address the top technical challenges across these use cases:

  • Data interoperability
  • Scalable data stores
  • Scalable, permissionless, feeless distributed ledger technology

Data Interoperability

In these and similar use cases, many different actors need to exchange data, so that requires a standard syntax, with reference vocabularies, for semantic interoperability. Plus, it all needs to be extensible to accommodate the specialized needs of different industries (for instance, the automotive industry and the seafood industry have distinctly different nuances). Some of the key technologies used here include W3C with JSON-LD, GS1 with EPCIS 2.0 and UN/CEFACT which provides edi3 reference data models. IOTA also used sectoral standards for data interoperability; for example DCSA (maritime transportation), MOBI (connected vehicles and IoT commerce) and the Global Dialogue on Seafood Traceability to name a few.

It’s worth noting that IOTA was deeply involved in the development of EPCIS 2.0, which is a vocabulary and data model (plus a JSON-based serialization format and accompanying REST APIs). It enables stakeholders to share transactional information regarding the movement and status of objects (physical or digital), identified by keys. Using this model, events are described as follows:

And that translates to JSON-LD in a format like this:

Scalable Data Stores with ScyllaDB NoSQL

Establishing a scalable data store for all the critical data associated with each supply chain event was another challenge. Cantera explained, “If we are tracking every single item in the supply chains, we need to store a lot of data, and this is a big data problem. And here, ScyllaDB provides many advantages. We can scale our data very easily. We can keep the data for a long period of time at a fine granularity level. Not only that, but we can also combine the best of the NoSQL and SQL worlds because we can have robust schemas for having robust data and trusted data.”

Cantera then continued to detail ScyllaDB’s role in this architecture, providing an example from the automotive supply chain. Consider an OEM with 10 million cars manufactured per year. Assume that:

  • Each car has 3,000 trackable parts.
  • Each part can have a lifetime of 10 years.
  • Each part can generate 10 business events.

This translates to around 300 billion active business events to store in ScyllaDB. Another example: Consider a maritime transportation operator that’s moving 50 million containers per year. Given 10 events per container and five years of operation, Cantera estimates about 2,500,000 active events here — just from the EPCIS 2.0 events repository. But there are also additional layers that require this level of data scalability.

He closes his discussion of this challenge with a look at the many applications for ScyllaDB across this initiative:

  • Events repository (EPCIS 2.0, DCSA, …)
  • Item-level tracking
  • Inventory
  • Catalog
  • Any DLT Layer 2 data storage

Scalable, Permissionless, Feeless Distributed Ledger Technology

Scalable, permissionless and feeless distributed ledger technology also played a key role in the solution that the IOTA Foundation architected. For this, it tapped the IOTA distributed ledger in combination with protected storages like IPFS to provide the functionalities around data and document verifiability, auditability and immutability within these peer-to-peer interactions.

For example, say you hire a particular transporter to move goods. When the activity begins, the transporter can generate an event that the trade items have started moving through the supply chain, and these events are committed to the IOTA distributed ledger. More specifically, the originator of the event generates a transaction on the distributed ledger, and that transaction can be later used by any participant in the supply chain to verify the authenticity of the event. And once the event is committed, the originator can no longer modify it. If the event was modified, the verification step would fail, and the supply chain partners might be understandably concerned.

Here’s how it all fits together:

Tip: For Cantera’s block-by-block tour of this reference architecture, see the video below, starting at 17:15.

Conclusions

Supply chain digitization is rife with technical challenges, so it’s not surprising that a nontraditional mix of technologies is required to meet the IOTA Foundation’s highly-specialized needs. Cantera sums it up nicely:

“It requires interoperability — which means it’s important to align with the open standards, EPCIS 2.0, the decentralized ID coming from W3C verifiable credentials. It requires a reference architecture to guarantee that semantic interoperability and some reusable building blocks are used. It requires decentralization, and decentralizing data requires distributed ledger technology — in particular, public, permissionless and feeless distributed layers like IOTA complemented with IPFS, and relying more and more on decentralized applications. It also requires data scalability and availability, and ScyllaDB is the perfect partner here. Last but not least, it requires trusted data sharing with technologies like decentralized IDs, distributed ledger technologies, and peer-to-peer.”

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NoSQL Market Outlook, Growth, Trends, Analysis and Forecast to 2022 – Digital Journal

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Technology Updates on the Future-NoSQL Market:

Quince Market Insights latest research report, titled “NoSQL Market: Global Industry Trends, Share, Size, Growth, Opportunity, and Forecast 2023-2032,” provides a thorough analysis of the market dynamics, segmentation, growth opportunities, trends, and competitive landscape to comprehend the present and foreseeable market conditions. The research offers a variety of market forecasts that take into account aspects like revenue size, production, CAGR, consumption, growth opportunities, industry trends and technologies, and other essential components. The report provides a comprehensive analysis of the next trends and developments in the NoSQL market while highlighting the important driving and restraint forces in this market. The important market trends with regard to the existing situation and anticipated advancements are included in the NoSQL industry assessment. The NoSQL market report is an in-depth analysis of the state of the global market at the moment, covering a number of industry variables. This NoSQL market research explains the thorough market study with contributions from industry experts.

Get FREE Sample PDF Copy of [email protected] https://www.quincemarketinsights.com/request-sample-69462

The NoSQL market research presents a dashboard overview of the historical and current performance of top organisations together with an analysis of successful marketing techniques, market contributions, and latest developments of leading companies. The research study uses a variety of approaches and analytics to give comprehensive and reliable information on the NoSQL Market. The SWOT and Porter Five Forces analyses, which aid in determining the market’s motivating and restraint factors, are also included in the study. Additionally, the market segmentation and growth analysis of the top market players currently engaged in business are included in the report. Understanding the changing market trends and how market participants might take advantage of them is made easier by the drivers and opportunities.

𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗜𝗻𝗰𝗹𝘂𝗱𝗲Aerospike, Inc., Amazon Web Services, Inc., DataStax, Inc., Microsoft Corporation, Couchbase, Inc., Google LLC, MarkLogic Corporation, MongoDB, Inc., Neo Technology, Inc., and Objectivity, Inc.

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Detailed segmentation by area (country), business, type, and application are included in the NoSQL market research analysis. The research offers comprehensive details on new products, regional investments, and market investments. The NoSQL market research report also monitors emerging technologies and trends. The most recent market dynamics, including motivating and inhibiting factors, as well as business news like mergers and acquisitions and investments, are also included in the study. This analysis of current market trends from 2023 to 2032 is provided for each of the sub-segments NoSQL Market, Type (Key-Value Store, Document Database, Column Based Store, and Graph Database), By Application (Data Storage, Mobile Apps, Data Analytics, Web Apps, and Others), and By Industry Vertical (Retail, Gaming, IT, and Others)),By Region (North America, Europe, Asia Pacific, Middle East & Africa, and South America) – Market Size & Forecasting To 2030

Regional Analysis:

The report also examines the current concerns and their Future Effects on the NoSQL  market by the region. The report covers all-region and countries of the NoSQL  Market. The market has been segmented into numerous primary regions and a Detailed evaluation of primary countries

◘ North America (U.S., Canada)
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◘ Asia-Pacific (China, India, Japan, Australia, Southeast Asia, Rest of Asia Pacific)
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◘ Middle East & Africa (GCC, Egypt, Nigeria, South Africa, Rest of Middle East and Africa)

Objectives of the Report:

• To accurately evaluate and project the value and volume of the NoSQL market.
• To assess the market shares of key NoSQL segments
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To gather verified material, the experienced research analysts engaged in thorough primary and secondary investigation. In order to create the NoSQL market figure, analyst conducts secondary research to validate data from sources including firm annual reports, investor presentations, articles, journals, and news channels. To verify the data from the secondary research with subject matter experts from the industry, thorough primary research is used to validate all of this information.

Reasons to buy this Report:

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Russia-Ukraine Conflict Impact Analysis:

Numerous market determinants, restrictions, and possibilities are explored in the NoSQL market research study, and it is almost clear that the Russia-Ukraine situation will have an influence on them. The study analyses cross-sectionally estimates of global demand while evaluating important sectors in various countries.

Frequently Asked Questions (FAQs):

➣ What is the anticipated market growth rate from 2023 to 2032?
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➣ What are the key industry players and what are their plans for gaining a strong foothold in the NoSQL sector?
➣ What are the key market trends impacting the NoSQL market’s growth in various regions?
➣ What are the biggest threats and hurdles that are anticipated to obstruct the growth of the NoSQL market?
➣ What are the key opportunities for market leaders to succeed and make profit?

Would you like to ask a questiaon? Ask Our [email protected] https://www.quincemarketinsights.com/enquiry-before-buying/enquiry-before-buying-69462

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

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