Month: August 2023

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“Final Report will add the analysis of the impact of COVID-19 on this industry.”

The Global “NoSQL Market” Research, as presented in the 115 Pages report, provides comprehensive insights into the market based on types, applications, and geographical locations. These categories are further subdivided to offer a more profound comprehension of the NoSQL market dynamics. The study highlights the key players in the industry, outlining the top businesses based on market share, market size, growth rate, and other relevant factors, contributing to a comprehensive competitive landscape analysis. Additionally, the report takes into consideration the impact of COVID-19, emerging market trends, mergers and acquisitions, and regional conflicts, offering a holistic view of the market’s current state and future prospects.

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The global NoSQL market size was valued at USD 7520.13 million in 2022 and is expected to expand at a CAGR of 31.08% during the forecast period, reaching USD 38144.35 million by 2028.

Major Players in NoSQL market are:

• Microsoft Corporation
• Neo Technology, Inc.
• MarkLogic Corporation
• Aerospike, Inc.
• DataStax, Inc.
• Google LLC
• Amazon Web Services, Inc.
• PostgreSQL
• Couchbase, Inc.
• Objectivity, Inc.
• MongoDB, Inc.

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NoSQL Market by Types:

• Key-Value Store
• Document Databases
• Column Based Stores
• Graph Database
• DDDD

NoSQL Market by Applications:

• Retail
• Gaming
• IT
• Others

Key Factors Considered

COVID-19

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

Regional Conflict / Russia-Ukraine War

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

Challenges and Opportunities

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

To Understand How Covid-19 Impact Is Covered in This Report – https://www.absolutereports.com/enquiry/request-covid19/22376195

Geographically, the detailed analysis of consumption, revenue, market share and growth rate, historical data and forecast:

• United States
• Europe
• China
• Japan
• India
• Southeast Asia
• Latin America
• Middle East and Africa

Years considered for this report:

• Historical Years: 2018-2022
• Base Year: 2022
• Estimated Year: 2023
• Forecast Period: 2022-2029

Client Focus

1. Does this report consider the impact of COVID-19 and the Russia-Ukraine war on the NoSQL market?

Yes. As the COVID-19 and the Russia-Ukraine war are profoundly affecting the global supply chain relationship and raw material price system, we have definitely taken them into consideration throughout the research, and in Chapters 1.7, 2.7, 4.X.1, 7.5, 8.7, we elaborate at full length on the impact of the pandemic and the war on the NoSQL Industry.

2. How do you determine the list of the key players included in the report?

With the aim of clearly revealing the competitive situation of the industry, we concretely analyze not only the leading enterprises that have a voice on a global scale, but also the regional small and medium-sized companies that play key roles and have plenty of potential growth.

Please find the key player list in Summary.

3. What are your main data sources?

Both Primary and Secondary data sources are being used while compiling the report.

Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users.

Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.

Please find a more complete list of data sources in Chapters 11.2.1 and 11.2.2.

4. Can I modify the scope of the report and customize it to suit my requirements?

Yes. Customized requirements of multi-dimensional, deep-level and high-quality can help our customers precisely grasp market opportunities, effortlessly confront market challenges, properly formulate market strategies and act promptly, thus to win them sufficient time and space for market competition.

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Chapter including in NoSQL market report:

• Chapter 1 mainly defines the market scope and introduces the macro overview of the industry, with an executive summary of different market segments ((by type, application, region, etc.), including the definition, market size, and trend of each market segment.
• Chapter 2 provides a qualitative analysis of the current status and future trends of the market. Industry Entry Barriers, market drivers, market challenges, emerging markets, consumer preference analysis, together with the impact of the COVID-19 outbreak will all be thoroughly explained.
• Chapter 3 analyzes the current competitive situation of the market by providing data regarding the players, including their sales volume and revenue with corresponding market shares, price and gross margin. In addition, information about market concentration ratio, mergers, acquisitions, and expansion plans will also be covered.
• Chapter 4 focuses on the regional market, presenting detailed data (i.e., sales volume, revenue, price, gross margin) of the most representative regions and countries in the world.
• Chapter 5 provides the analysis of various market segments according to product types, covering sales volume, revenue along with market share and growth rate, plus the price analysis of each type.
• Chapter 6 shows the breakdown data of different applications, including the consumption and revenue with market share and growth rate, with the aim of helping the readers to take a close-up look at the downstream market.
• Chapter 7 provides a combination of quantitative and qualitative analyses of the market size and development trends in the next five years. The forecast information of the whole, as well as the breakdown market, offers the readers a chance to look into the future of the industry.
• Chapter 8 is the analysis of the whole market industrial chain, covering key raw materials suppliers and price analysis, manufacturing cost structure analysis, alternative product analysis, also providing information on major distributors, downstream buyers, and the impact of COVID-19 pandemic.
• Chapter 9 shares a list of the key players in the market, together with their basic information, product profiles, market performance (i.e., sales volume, price, revenue, gross margin), recent development, SWOT analysis, etc.
• Chapter 10 is the conclusion of the report which helps the readers to sum up the main findings and points.
• Chapter 11 introduces the market research methods and data sources.

Some of the key questions answered in this report:

• What will the NoSQL market growth rate during the forecast period?
• Which are the key factors driving the NoSQL market?
• What was the size of the NoSQL market by 2029?
• Which region is expected to hold the highest market share in the NoSQL market?
• What trends, challenges and barriers will impact the development and sizing of the Global NoSQL market?
• What are the NoSQL market opportunities in the global NoSQL Industry?

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Detailed TOC of Global NoSQL Industry Research Report

1 NoSQL Market Overview

1.1 Product Overview and Scope of NoSQL Market

1.2 NoSQL Market Segment by Type

1.2.1 Global NoSQL Market Sales Volume and CAGR (%) Comparison by Type

1.3 Global NoSQL Market Segment by Application

1.3.1 NoSQL Market Consumption (Sales Volume) Comparison by Application

1.4 Global NoSQL Market, Region Wise

1.4.1 Global NoSQL Market Size (Revenue) and CAGR (%) Comparison by Region

1.4.2 United States NoSQL Market Status and Prospect

1.4.3 Europe NoSQL Market Status and Prospect

1.4.4 China NoSQL Market Status and Prospect

1.4.5 Japan NoSQL Market Status and Prospect

1.4.6 India NoSQL Market Status and Prospect

1.4.7 Southeast Asia NoSQL Market Status and Prospect

1.4.8 Latin America NoSQL Market Status and Prospect

1.4.9 Middle East and Africa NoSQL Market Status and Prospect

1.5 Global Market Size of NoSQL

1.5.1 Global NoSQL Market Revenue Status and Outlook

1.5.2 Global NoSQL Market Sales Volume Status and Outlook

1.6 Global Macroeconomic Analysis

1.7 The impact of the Russia-Ukraine war on the NoSQL Market

2 Industry Outlook

2.1 NoSQL Industry Technology Status and Trends

2.2 Industry Entry Barriers

2.2.1 Analysis of Financial Barriers

2.2.2 Analysis of Technical Barriers

2.2.3 Analysis of Talent Barriers

2.2.4 Analysis of Brand Barrier

2.3 NoSQL Market Drivers Analysis

2.4 NoSQL Market Challenges Analysis

2.5 Emerging Market Trends

2.6 Consumer Preference Analysis

2.7 NoSQL Industry Development Trends under COVID-19 Outbreak

2.7.1 Global COVID-19 Status Overview

2.7.2 Influence of COVID-19 Outbreak on NoSQL Industry Development

3 Global NoSQL Market Landscape by Player

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

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

3.3 Global NoSQL Average Price by Player (2018-2023)

3.4 Global NoSQL Gross Margin by Player (2018-2023)

3.5 NoSQL Market Competitive Situation and Trends

3.5.1 NoSQL Market Concentration Rate

3.5.2 NoSQL Market Share of Top 3 and Top 6 Players

3.5.3 Mergers and Acquisitions, Expansion

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

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

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

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

4.4 United States NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.4.1 United States NoSQL Market Under COVID-19

4.5 Europe NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.5.1 Europe NoSQL Market Under COVID-19

4.6 China NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.6.1 China NoSQL Market Under COVID-19

4.7 Japan NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.7.1 Japan NoSQL Market Under COVID-19

4.8 India NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.8.1 India NoSQL Market Under COVID-19

4.9 Southeast Asia NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.9.1 Southeast Asia NoSQL Market Under COVID-19

4.10 Latin America NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.10.1 Latin America NoSQL Market Under COVID-19

4.11 Middle East and Africa NoSQL Sales Volume, Revenue, Price and Gross Margin (2018-2023)

4.11.1 Middle East and Africa NoSQL Market Under COVID-19

5 Global NoSQL Sales Volume, Revenue, Price Trend by Type

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

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

5.3 Global NoSQL Price by Type (2018-2023)

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

6 Global NoSQL Market Analysis by Application

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

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

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

7 Global NoSQL Market Forecast (2023-2028)

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

7.1.1 Global NoSQL Sales Volume and Growth Rate Forecast (2023-2028)

7.1.2 Global NoSQL Revenue and Growth Rate Forecast (2023-2028)

7.1.3 Global NoSQL Price and Trend Forecast (2023-2028)

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

7.2.1 United States NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.2 Europe NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.3 China NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.4 Japan NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.5 India NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.6 Southeast Asia NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.7 Latin America NoSQL Sales Volume and Revenue Forecast (2023-2028)

7.2.8 Middle East and Africa NoSQL Sales Volume and Revenue Forecast (2023-2028)

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

7.3.1 Global NoSQL Revenue and Growth Rate of Photo (2023-2028)

7.3.2 Global NoSQL Revenue and Growth Rate of Video (2023-2028)

7.4 Global NoSQL Consumption Forecast by Application (2023-2028)

7.4.1 Global NoSQL Consumption Value and Growth Rate of Commercial (2023-2028)

7.4.2 Global NoSQL Consumption Value and Growth Rate of Editorial

7.5 NoSQL Market Forecast Under COVID-19

8 NoSQL Market Upstream and Downstream Analysis

8.1 NoSQL Industrial Chain Analysis

8.2 Key Raw Materials Suppliers and Price Analysis

8.3 Manufacturing Cost Structure Analysis

8.3.1 Labor Cost Analysis

8.3.2 Energy Costs Analysis

8.3.3 RandD Costs Analysis

8.4 Alternative Product Analysis

8.5 Major Distributors of NoSQL Analysis

8.6 Major Downstream Buyers of NoSQL Analysis

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

9 Players Profiles

9.1 Manufacture 1

9.1.1 Manufacture 1 Basic Information, Manufacturing Base, Sales Region and Competitors

9.1.2 NoSQL Product Profiles, Application and Specification

9.1.3 Manufacture 1 Market Performance (2018-2023)

9.1.4 Recent Development

9.1.5 SWOT Analysis

10 Research Findings and Conclusion

11 Appendix

11.1 Methodology

11.2 Research Data Source

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The worldwide “Distributed Database Management Systems Market” 2023 Research Report presents a professional and complete analysis of the Global Distributed Database Management Systems Market in the current situation. This report includes development plans and policies along with Distributed Database Management Systems manufacturing processes and price structures. the reports 2023 research report offers an analytical view of the industry by studying different factors like Distributed Database Management Systems Market growth, consumption volume, Market Size, Revenue, Market Share, Market Trends, and Distributed Database Management Systems industry cost structures during the forecast period from 2023 to 2030. It encloses in-depth Research of the Distributed Database Management Systems Market state and the competitive landscape globally. This report analyzes the potential of the Distributed Database Management Systems Market in the present and future prospects from various angles in detail.

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➳ North America (United States, Canada, Mexico)
➳ Europe (Germany, UK, France, Italy, Spain, Others)
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Global Distributed Database Management Systems Market Development Strategy Pre and Post COVID-19, by Corporate Strategy Analysis, Landscape, Type, Application, and Leading 20 Countries covers and analyzes the potential of the global Distributed Database Management Systems industry, providing statistical information about market dynamics, growth factors, major challenges, PEST analysis, and market entry strategy Analysis, opportunities and forecasts.

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Keybank National Association OH grew its holdings in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 20.9% in the 1st quarter, according to the company in its most recent filing with the SEC. The firm owned 1,915 shares of the company’s stock after buying an additional 331 shares during the quarter. Keybank National Association OH’s holdings in MongoDB were worth $446,000 at the end of the most recent quarter.

Several other institutional investors have also recently made changes to their positions in MDB. Raymond James & Associates boosted its position in shares of MongoDB by 32.0% during the first quarter. Raymond James & Associates now owns 4,922 shares of the company’s stock valued at $2,183,000 after purchasing an additional 1,192 shares in the last quarter. PNC Financial Services Group Inc. raised its stake in MongoDB by 19.1% during the first quarter. PNC Financial Services Group Inc. now owns 1,282 shares of the company’s stock valued at $569,000 after buying an additional 206 shares during the last quarter. MetLife Investment Management LLC purchased a new position in MongoDB during the first quarter valued at $1,823,000. Panagora Asset Management Inc. raised its stake in MongoDB by 9.8% during the first quarter. Panagora Asset Management Inc. now owns 1,977 shares of the company’s stock valued at $877,000 after buying an additional 176 shares during the last quarter. Finally, Vontobel Holding Ltd. raised its stake in MongoDB by 100.3% during the first quarter. Vontobel Holding Ltd. now owns 2,873 shares of the company’s stock valued at $1,236,000 after buying an additional 1,439 shares during the last quarter. Institutional investors own 89.22% of the company’s stock.

MongoDB Stock Up 1.0 %

Shares of MDB stock opened at $362.04 on Wednesday. MongoDB, Inc. has a 1 year low of $135.15 and a 1 year high of $439.00. The company has a debt-to-equity ratio of 1.44, a quick ratio of 4.19 and a current ratio of 4.19. The stock’s 50 day simple moving average is $391.67 and its two-hundred day simple moving average is $296.73.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings data on Thursday, June 1st. The company reported $0.56 earnings per share (EPS) for the quarter, beating analysts’ consensus estimates of $0.18 by $0.38. MongoDB had a negative return on equity of 43.25% and a negative net margin of 23.58%. The business had revenue of $368.28 million for the quarter, compared to the consensus estimate of $347.77 million. During the same period in the prior year, the business posted ($1.15) earnings per share. The company’s quarterly revenue was up 29.0% compared to the same quarter last year. Equities research analysts anticipate that MongoDB, Inc. will post -2.8 earnings per share for the current year.

Analyst Ratings Changes

A number of research analysts have issued reports on MDB shares. Royal Bank of Canada lifted their price objective on MongoDB from $400.00 to $445.00 in a research note on Friday, June 23rd. 58.com restated a “maintains” rating on shares of MongoDB in a research note on Monday, June 26th. KeyCorp boosted their price target on MongoDB from $372.00 to $462.00 and gave the stock an “overweight” rating in a research note on Friday, July 21st. Guggenheim cut MongoDB from a “neutral” rating to a “sell” rating and boosted their price target for the stock from $205.00 to $210.00 in a research note on Thursday, May 25th. They noted that the move was a valuation call. Finally, The Goldman Sachs Group boosted their price target on MongoDB from $420.00 to $440.00 in a research note on Friday, June 23rd. One research analyst has rated the stock with a sell rating, three have assigned a hold rating and twenty have assigned a buy rating to the company. Based on data from MarketBeat, the company has a consensus rating of “Moderate Buy” and an average target price of $378.09.

View Our Latest Research Report on MongoDB

Insider Transactions at MongoDB

In other news, CRO Cedric Pech sold 360 shares of the stock in a transaction that occurred on Monday, July 3rd. The stock was sold at an average price of $406.79, for a total value of $146,444.40. Following the sale, the executive now directly owns 37,156 shares in the company, valued at $15,114,689.24. The sale was disclosed in a document filed with the SEC, which is available at this link. In related news, CRO Cedric Pech sold 360 shares of the stock in a transaction that occurred on Monday, July 3rd. The stock was sold at an average price of $406.79, for a total value of $146,444.40. Following the transaction, the executive now owns 37,156 shares of the company’s stock, valued at $15,114,689.24. The sale was disclosed in a legal filing with the SEC, which can be accessed through the SEC website. Also, CAO Thomas Bull sold 516 shares of the stock in a transaction that occurred on Monday, July 3rd. The stock was sold at an average price of $406.78, for a total value of $209,898.48. Following the completion of the transaction, the chief accounting officer now directly owns 17,190 shares in the company, valued at approximately $6,992,548.20. The disclosure for this sale can be found here. Over the last three months, insiders have sold 102,220 shares of company stock worth $38,763,571. 4.80% of the stock is currently owned by company insiders.

MongoDB Profile

(Free Report)

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

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Transcript

Cockcroft: I’m Adrian Cockcroft. I’m going to talk to you about cloud provider sustainability, the current status and future directions. I also call this DevSusOps, so adding sustainability concerns to development and operations. Although, I’m focusing very much on what the developer concerns are in this space. You can find me at OrionX.net, which is a consulting firm that I work for. Also, on mastodon.social and DevSusOps@hachyderm.io. I just want to talk about, why does sustainability matter? Why is this hard? Getting the terminology and mental models right is actually much more difficult than you might expect. I’m going to talk about cloud provider similarities and differences. What are the measurements? Then, the interesting part at the end, things developers need to build and what we need to get so that we can build them.

Why Does Sustainability Matter?

Let’s start with, why does sustainability matter? Fundamentally, we want to leave the world habitable for future generations. There’s also regulatory compliance coming down through board level from governmental regulations. There’s market transition risks, as the markets move to electric cars from gas powered cars. There’s physical risks to business assets, like flooding, and high temperatures. Green market positioning for your products. There’s employee enthusiasm. People may not want to work for you if you don’t have a green perception in the market. There’s potentially reduced cost now or in the future. It turns out, renewable energy is now the cheapest form of energy. That’s driving quite a lot of the work here. Then, some companies, it’s your social license to operate. There’s things we can do now. There’s development. You can optimize your code, just make things more efficient so you’re using less resources to get your job done. Operations, you want to deploy this code with high utilization, more automation. Just be careful about cleaning everything up as well.

Measuring Carbon

I’m going to focus mostly on measuring carbon, and why is this hard. Because, fundamentally, we’re just multiplying two numbers together. We need to figure out how much energy some code uses, and we have to figure out the carbon content of the machine it’s running on. That’s pretty much it. It turns out that there’s a lot more to this. What I just mentioned is called scope 2, which is the energy use. What actually matters also is scope 3, which is the energy used, and the carbon emissions of everything it took to make that computer, and deliver it, and put it in a building, and the cost of creating the building, and all of that stuff. That’s the supply chain. Then there’s also if you recycle it at the end, then, what’s the energy used to recycle things? This is from a Microsoft document that gives a good summary of what’s going on, https://learn.microsoft.com/en-us/industry/sustainability/api-calculation-method. You can see from Microsoft’s point of view, the scope 3 is their upstream supply chain. The operations of their services is scope 1 and 2. Scope 3 is also the downstream disposition or recycling.

Then, if you’re a cloud customer, though, what is reported to you by the cloud provider is your scope 3 is reported by you as your scope 3. As the carbon content of energy tends to zero, as we get more renewables, the scope 3 is dominating, because the shipping and manufacturing is not coming down as fast as the carbon content of energy. It’s taking much longer to decarbonize silicon production, and shipping, for example. It will get there in the end, but probably more like 2040, 2050, rather than 2025 to 2030, which is what we’re looking at for carbon. However, in Europe and the U.S., current renewable energy for the cloud providers is almost completely clean. They’re very high percentage of renewable energy. It’s really only in Asia, which is where the silicon is manufactured that the electricity there is particularly high carbon. If you’re trying to build APIs with your developers for supply chain automation and pulling in all the data from your suppliers, this Pathfinder Framework is a good place to look.

Cloud Provider Scope 3 Differences, and Methodology

There are some differences across the different cloud providers for scope 3. AWS doesn’t provide any scope 3 information yet. I wrote a blog. There was Computer Weekly story. There was a LinkedIn page with a response to that story. Fundamentally, they are working on it but they’re taking their time. If you really need scope 3 from AWS, and you’re a big enough customer, you can escalate, and they are providing estimates to people under NDA. That’s true of pretty much anything you want custom from AWS. If you’re big enough and you ask the right people, they will usually give you things under NDA, information that isn’t publicly available. What they’re working on is the APIs and all the infrastructure needed to provide more information to everybody, down to the individual. However, Microsoft has had this for a few years, they have a detailed paper and API. It’s a good paper. They include recycling, but they don’t include transport and buildings yet. Numbers are going to go up a bit when those are added. Google also has detailed scope 3, which I think includes transport and buildings, but doesn’t seem to include end of life recycling. They claim that that’s estimated to be immaterial. There’s going to be differences from provider to provider, but hopefully not too much. Most of the time, what we really care about is the silicon and the steel in the machine itself. Then, probably a bit of the building it’s in.

Then we look at the methodology. This is another diagram from Microsoft. The question is, how do you allocate the building and all of these other scope 3 things to the workload? What they do, as far as I can tell, is they take the proportion of the energy used by the different workloads or customers in the building, and then they apportion all the scope 3 proportionately to the energy use. Ideally, you’d want to say this machine uses this much rack, and it’s using this much space in the building. That’s a little too complicated. They just do a proportional allocation. You tend to find these kinds of simplifying assumptions quite a lot when you’re looking at carbon calculations. This isn’t bad, it’s just you have to dig in and find out, because it may be that another cloud provider is using a different methodology to allocate scope 3 to workloads. That’s one of the problems I’m going to highlight. It’s just there are differences here.

There’s some interesting information here from one of the AWS sustainability leads, Cyril. This is across Europe. What you can see here, if you just look at the columns for scope 1, 2, and 3 here. Scope 1 he’s saying is the same. They have the same data center in different countries around Europe. This isn’t cloud, this is just a data center. Scope 3 he’s estimating is the same. Scope 1’s the same, which is mostly the fuel used for backup generators. Scope 2 is all over the place. Like Poland has obviously a lot of fossil fuel in its energy, and it really dominates. If you’re in Sweden or Norway, then your energy is dominated by scope 3. This is just looking at it from the basis of grid mix. What the cloud providers do is they buy additional renewable energy to offset that. We’re developers, and scope 3 is about hardware, it’s a Ops problem. Let’s just concentrate on the energy, on the carbon content, because that’s something that we can control by using our machines more efficiently. It’s a number that we might want to manipulate.

Energy

Let’s look at energy. How much energy does a specific line of code use? There’s a few things out there. There’s JoularJX, which uses the JVM instrumentation to try and come up with some estimates for energy monitoring, by the line of code almost. There’s also, how much energy does a transaction use? There was a talk about the Green Software Foundation and SCI, which is, if you can figure out the energy you’re using and the transactions you’re doing with it in a workload, now you can start normalizing energy per customer, per transaction, per those kinds of things. That’s good, but you still need to know the underlying energy in order to calculate SCI. What I’m focusing on is the underlying information you would need to go and do SCI at all, from a cloud provider. How much energy does a workload use, would actually be interesting. Maybe you need to get down to an individual container. What does that use? How much energy does a specific cloud instance use? This one I’m running on right now, how much is that currently using? Unfortunately, cloud providers, none of them tell you any of those things in enough detail for you to answer that question. It’s a bit of a problem.

How could you measure it directly? If you have a physical access to a machine, you can put a monitor on it. This is a little box that you can get for 20 bucks that you can run your laptop or your desktop machine on and you can go and try and measure energy usage when you run different workloads and see if you can figure out how much energy they’re using. You’ll find, it’s very unrepeatable and unreproducible. If you’re trying to do this data for real and use the data, then I think you should understand how to do statistical analysis to make sure you’re not reporting noise. That’s what Gage R&R does. It basically extracts how much repeatability, reproducibility you really have in your data. Energy is a pain, we’re just going to have an average hourly rate, which we’ll get from somewhere.

Carbon Content

Let’s try and figure out now where the carbon content comes from. That depends. What’s the grid mix for energy generation in that location? Where did you get the measures of the mix? When did you get them? It turns out, the grid mix isn’t really available accurately until a month later when you get your bill from the energy supplier, so maybe a month or two. Can you get hourly? GCP has an hourly 24 by 7 measure that they use for generating their estimates, so they’re calculating based on an hourly rate, but they don’t tell you what the actual hourly rate is, as far as I can tell. Unfortunately, these numbers also change after you use them for up to a year, for reasons that I’ll get into later. The grid mix isn’t just a thing you can depend on. Then, what really matters for the cloud is how much private provider energy was used. They don’t actually tell you that. There’s something called a bundled REC, you want to know how much that was used. You have to know how much unbundled REC was used. These are all things that matter. The private energy is the entity they contract with directly. Then the RECs are things that are traded.

Grid mix, you get it from the bill, a month or so in arrears, along with power purchase agreements. This is the Google wind farm, the Amazon solar farm, the battery farms that go with them, those kinds of things that are basically built under contract by the cloud providers. There’s a lot of that happening. Then renewable energy credits are more tradable things. We also care about the power usage efficiency. It depends where it’s measured, but that’s the energy coming into the building. How much of it actually gets all the way to the computer and is running that CPU? It’s typically somewhere between 10% to 100%, maybe twice as much energy is coming into the building as is actually being used for your computer, in some parts of the world. It’s typically, for the cloud providers, 10% to 80% range of overhead. What we have then is to get the carbon we need to take the power mix. We need to know the power usage efficiency. We need to know how much capacity of something we’re using, and we need to know how much emissions factor is per that capacity.

There’s two completely different ways of reporting this, location and market based. Location based uses this utility grid mix, 24 by 7, this is what Google is using. They’re saying every hour how much energy is coming into the building, and what was the grid mix at that time. They’re not taking into account the fact that Google has a bunch of wind farms and solar farms out there. Market based accounts for these dedicated power purchases and the renewable energy credits that you bought in that same market. Market based means that the electricity is connected. You don’t account for a power station in another country that’s not connected to you. You can’t use that. You have to use the ones that are connected in the grid to the same company so the electricity could flow. Not all countries are fully connected. In the U.S., there’s multiple grids and there are interconnects. The interconnects don’t have enough capacity to power everything across them.

Why It Matters

Why does it matter? AWS and Azure use regional market-based data. Google, originally, they have this claim that since 2017, they’ve been 100% renewable. That’s global market. Effectively, generation in Europe and U.S. was being counted against Singapore, which is a bit. It was a good idea in 2017. It’s not such a great idea now. The current data that Google has is location based. You can’t really compare the data between location and regionally based, but it’s probably better for tuning work to use the location-based data if you can get it. Really, what we want is every cloud provider to give us both. Here’s that formula, the power mix, what are the problems? The utility bill that tells you your power mix is going to be a month or more depending on where in the world you are. It takes longer the further out into the developing world you go. Power purchase agreements, these are contracts to build and consume power generation. Amazon has over 20 gigawatts of power purchase agreements, I think solar, wind, and increasingly battery power so that they can use power during the day from solar, and run it overnight when the wind isn’t blowing, and things like that.

The cloud providers are now amongst the largest energy producers in the world. They have energy on a similar scale to a lot of utility providers that you would normally buy energy from. In Europe, renewable energy credits are called guarantees of origin, but they’re slightly different. Renewable energy credits are purchases of renewable capacity on the open market from existing generators. You can only claim that energy once, but you can claim it later on. You don’t have to claim it exactly at the time it was generated, which causes other problems I’ll get to later. RECs can top up on top of PPAs. What you typically do as a cloud provider, you buy lots of PPAs, and then a little bit extra here and there, so you buy some RECs. Maybe you’ve contracted for a wind farm, but it hasn’t finished being built yet, so you might buy some RECs to cover for that. You’ve got the commitment. You know you’re going to get it, but you’re waiting for them to finish building it, those kinds of things. Or just to get your percentage to where you want it to be. What’s good is that you buy as many PPAs as possible, and then you buy some RECs to top that up.

Guarantee of origin, renewable energy credit, fairly similar, a little bit different in how they’re regulated and traded. A local market REC in the same grid, that’s good. When you go cross-border or non-local market, that’s energy that can’t flow to you. You’re assuming that the carbon you’re saving in one part of the world is offsetting carbon being emitted somewhere else. You’re connecting the two places over the carbon cycle, rather than over the electricity grid. This is also often used as a cheap carbon offset and greenwashing. This has got RECs a bad name. It’s really the cross-border and non-local market RECs that people are buying them too cheaply that’s got a bad name. Local market RECs actually make much more sense and are actually reasonably valuable. There’s some links down below if you want to dig into this a bit more.

You get grid mix. It changes every month. There’s hourly data for 24/7 starting to appear. Google and Microsoft are publicly working towards 24/7. AWS hasn’t said anything about this. The cloudcarbonfootprint.org open source tool doesn’t include these PPAs or RECs. It just works off the grid mix, which is ok in some places, but it’s increasingly becoming inaccurate. Then there’s an interesting startup called FlexiDAO working in the energy trading space.

Power Mix Problems and Misconceptions

Here’s some problems and misconceptions. I try to illustrate this a bit. The problem is, let’s look at the bottom two things: you’ve got fossil fuel energy and renewable energy. That is the power mix reported as the grid mix used by the Cloud Carbon Footprint. Then on top of that, you’ve got some renewable energy purchase agreements by dedicated capacity and a few credits. The power mix used by a cloud provider in a more emerging market space is some mixture of these things. This is where the energy from the PPAs is never more than the cloud provider needs. Here, you’ve got a wind farm, you’re going to use every bit that that wind farm can possibly make all the time, there’s none left over. That’s what I mean by an emerging market. As we develop this, one of the problems here is that the RECs on top, the blue area, this is where it gets complicated. The more RECs you have, it makes the grid mix worse for everyone else. RECs take away from the grid mix. The other problem is RECs are traded for up to a year. You generate energy 1 month for up to 12 months. Twelve months later you could say, no, that was not green energy going into the grid mix, that is green energy I’m going to sell to somebody because they wanted to top up what they were doing a year ago. It may sound a bit bogus, but this is the rules. This is how it works. What that fundamentally means is the grid mix is not a stable metric for 12 months, and at various points in time it can decrease and look worse over time, just to get that out there. There’s explanation in this URL, https://www.ecocostsvalue.com/lca/gos-and-recs-in-lca/. It basically goes into this. That’s in emerging markets. What I mean by that is you’re still at a deficit, you’re still depending very heavily on the grid.

Then, if we get into a more mature market where the cloud provider is generating so much renewable energy that they have excess left over, what are they going to do with that excess energy that they can’t actually use because they’re generating more during the day than they can consume? They sell it as credits. Some of the renewable energy credits are now being put back into the market, and sold from cloud providers to anyone else that wants them. Then, any credits that aren’t sold, that excess renewable energy goes into the grid mix and it makes the grid mix look better than it was before. Now it’s not clear what’s going on, these boundaries are getting very blurry. It gets pretty complicated to understand. This is why this is difficult.

Then there’s this thing, if you follow this URL, the top one and that’s shown below, we get what I’m calling the Norway problem. In Norway, it is 2% fossil electricity, it’s 98% green. Great, let’s move our data center to Norway and get green energy. Unfortunately, Norway sells 86% of the RECs for its green energy to other countries in Europe. The effect of that is if you actually set up in Norway and run your data center or your company, for that 86% that’s sold to Europe, you actually get the residual mix from Europe back into that to compensate, and that ends up at about 50%. The net of this is even though almost all of the energy in Norway is hydroelectric, if you operate in Norway, you’ll have to report about 50% renewable and 50% non-renewable unless you go out and buy those RECs from the Norwegian energy companies that basically say, yes, you are really buying renewable energy. This may sound stupid, whatever, but this is the rule. This is fundamentally because selling these across borders is allowed. This is considered a bad practice nowadays, but this is what happens. This is complicated stuff.

Let’s look at power usage efficiency. It’s not that well standardized, but you can compare across similar data center designs. AWS doesn’t publish PUE. People tend to quickly point at this blog by James Hamilton that explains why it’s complicated. Azure and Google do publish PUE. They have numbers for it. Azure’s numbers are about 1.2, Google’s are about 1.1. I wouldn’t compare them across that much, because the methodology is all different. At least you can see that they are a lot better than most public data centers, which are 1.5 to 2. We’re trying to figure out the capacity that we’re using. We’ve got dedicated, capacity is relatively easy to account for, but then you’ve got the shared instances, network equipment, it’s pretty difficult. This is hard for the cloud providers to report to you. If you are a multi-tenant SaaS provider, you have the same problem, you’ve got to figure out the capacity you use in your business. How do you provide common information to the people that consume your product? Everyone’s got this problem. This allocation problem is a big, complicated area. There’s a lot of interesting algorithms needed here. This is a developer problem. Some of you maybe end up working on it. Then the other thing we need to know is how much power an instance type, storage class, or service uses. This depends on utilization overheads, and all of this data isn’t available.

Takeaways

What can you do today? Fundamentally, if you use less stuff, it makes the biggest difference. It reduces your energy use, but also it reduces your footprint in terms of scope 3. The best thing you can do is just use fewer computers, and use them for less time, just be efficient about it. High utilization and more efficient systems is just going to be the best thing you can do. If you’re looking at cloud providers, all the cloud providers are really pretty similar. The numbers, different people might argue that one is better than another. In fact, they are all pretty much in the same place, there isn’t that much difference. They’re all buying enough renewables to cover most of their energy usage in U.S. and Europe. They all have the same problems in Asia. Scope 3 is dominated by the same chip suppliers for everybody. They’re buying the same SSDs, the same Intel chips. It doesn’t really make much difference in terms of the carbon footprint of a machine in terms of scope 3. There are probably some differences in terms of efficiency of shipping and buildings and things. The big dominant things that go up to make a machine, it’s not that different.

Use any cloud provider. There will be detailed differences, but it’s not going to be that different. Try to minimize use of Asia regions for the next few years. Most of them have a roughly two-to-three-year timeframe to try and get Asia to decarbonize and to get more renewable energy there. In two, three-year’s time, we should be pretty renewable everywhere in the world. Fundamentally, they are all much better than a typical enterprise data center. There are papers from all the cloud providers, sort of 80% to 90% better reduced carbon compared to a typical enterprise data center. This illustrates that. It’s a Google figure, it shows you in the U.S. and Europe, there are some pretty good green all-day numbers. If you look at Singapore, it’s 4% renewable, and Taiwan is 17%. That’s what I’m talking about Asia being a problem. That is the fundamental problem. There just isn’t renewable energy, and there isn’t the ability to put renewable energy in Singapore. Everyone’s got that problem. This isn’t a Google problem. This is an everyone problem, and everyone’s working on it in different ways.

Measuring – Compare APIs and Schemas Across AWS, Azure, and GCP

Let’s look at the APIs and schemas, I said, developer talk. Let’s go and look at what data you can actually get across AWS, Azure, and GCP. This is the AWS data. It was released about a year ago. Customer Carbon Footprint tool is part of the billing console in an AWS account, and you get a monthly summary. The location, you get it for the three different continents, you don’t get it down to the region. You get accounts, EC2 per account. Account by account, you can get how much was EC2, how much was S3, and everything else is in others. It’s pretty low specificity. Then, you get down to 0.1 of a metric ton of CO2e for resolution. A lot of you, when you run these reports in Europe will just get zero. That’s actually expected, pretty much. You’ve got less than a 10th of a metric ton of CO2e per month, because you’re just not big enough to make it move off of that. This is scope 1 and market-based scope 2. This doesn’t include the scope 3, and there’s some criticism around that, but they’re working on it. It’ll get there eventually.

Azure actually have an OData API. There’s a tool. Let’s look at it through the API. There’s a bunch of queries there, but, fundamentally, what you get out of this query is, again, a monthly summary. The day is always going to be one, when you get a date. It’s going to tell you which month it is. This time, they have regions as well as countries. You have services as well as accounts. Every individual service, you can say, what is this service, and it will tell you the carbon for that service. The resolution is a kilogram instead of 100 kilograms. It’s 100 times better resolution than AWS from looking at the summaries of the data and figuring this out. They also include scope 3. It’s a market-based scope 2. Reasonable API. OData is a fun thing, you can write interesting queries with.

The way Google works is you export your data into BigQuery, and then use the BigQuery API on it. This is the export schema. Again, monthly summary. This time, they go down below regions to each zone. You get data per zone. You have projects on Google. If you wrap a workload into a project, you can get project specific data, at least at the monthly level. That’s a nice feature. I think AWS never had the concept of a project, and just very recently came out with something that is a bit like a project. I’m hoping that more AWS support starts to do projects. Resolution, a 10th of a kilogram. That’s like 10 times smaller than the Microsoft data that I’ve seen. They do scope 1, location-based scope 2 with placeholders in here for marketplace. They’re stating they’re going to have both, and scope 3. This is in some decent schema, and then some good examples of what the numbers might look like.

Workload Carbon Footprint Standard (Proposal)

Those are the measurements we can get, but what do we actually want? What I want is real-time carbon metrics for optimization. I want it to be just another metric like CPU utilization. I want it to be reported by the same tools we already use. I want whatever tool you’re using to monitor your machines to report carbon as well. This is what I’d like to see. This is me proposing something I’ve made up. This is a proposal. It’s not really been vetted by anybody. I’m launching that here. What I want to see is the same data for all cloud providers, but also data center automation tools, VMware, whatever, things like that. I’d like to see resolution, not monthly data, I want minutes, seconds, maybe. I want it to come out in CloudWatch, once a minute. I want to have a carbon number in CloudWatch as well as the CPU utilization. Country, region, and zone, I think that makes sense. I want to go down to containers, file systems, basically anything CloudWatch measures. Anything I can get a metric from that makes sense, I want to be able to get a carbon number for it. Actually, I don’t really just want carbon, what I actually want is the energy usage, pretty high-resolution energy usage. I think millijoules or milliwatt seconds is probably good, because some of these things we might want to measure might be battery powered devices at the edge, which use very little energy. If you have millions of them around the world, they add up, so you want to understand that.

I want to have energy and carbon, because the carbon is going to be an estimate. I want location and market-based scope. What I mean by this is energy is final. We could figure out the energy, we’re not going to revise that. Carbon would have to be an initial estimate, because it’s going to be a guess of what the next hour or what that hour looks like. The data is there to do an estimate, but the numbers are going to move. What I’d want is to have an initial estimate, just so the numbers are there, that’s roughly in the right ballpark. Perfectly good enough for tools and optimization algorithms, things like that. Then the audit report quality that the cloud providers currently give us takes a couple of months to produce. A couple of months is fine. Then, as I said, the RECs settle for a grid, so you really want to have another update after 12 months to say, this is the final number that we’re never going to change again, potentially, that might be useful for some cases. If we compare across all these, this is what I’d really like to have to develop some cool tools on this in the first column, and then AWS, Azure, and GCP in the other columns, just to get it all onto one table.

What Tools Could We Build?

What could you build with this? I think the first thing is there’s a lot of cost optimization tooling out there, which looks at the utilization of systems, it looks at the billing data, and it combines them together. Cloud Carbon Footprint is probably the most common one that’s out there. This should be in all the tooling. Any tool that does cost optimization or performance optimization should be able to optimize for carbon as a metric, as well as latency or utilization, or dollars, or euros, or whatever. Then, SaaS providers, we need to attribute and allocate tools. This is a big, complicated problem. We’re going to be building lots of tools here. There should be tools off-the-shelf that can plug in onto any provider and say, here’s how you do allocation. Then I think it’d be interesting to have architecture planning tools. Like, what’s the carbon footprint difference if you’re trying to decide, should you run your own Kafka on EC2? Should you run AWS MSK, which is a Kafka as a service, or should you go to Confluent, or Redpanda, which is a Kafka compatible clone? Or AWS Kinesis, which isn’t the same as Kafka, but it might be a lower carbon? It’s got a shared control plane, so it possibly is, but it depends. The question is, could we get all of these different things to either publish or have a way to calculate what that comparison would look like? I really open this up to ideas. I’m really looking for input. What would you want to build if you could get this fine-grained data and do something interesting with it?

Measuring Climate Impact and Risk

Finally, there’s climate impact and risk. The OS-Climate.org, I was involved in helping get them started while I was at AWS. A lot of open source software here. If you’re trying to do transition analysis, there’s economic modeling for business transitions. Then the other thing they look at is physical risk and resilience, things like trying to understand where your customers are. Like if your customers can’t get to you, if you’re a retail chain, and there’s floods, what happens? People can’t get to you, you’ve shut down. What’s the impact to your business of increasing probability of bad weather effects? This is an area. It’s all open source. If you want to go contribute to something, go take a look at it. Lots of interesting data science, climate science, and the transition analysis models are pretty sophisticated. Some cool software here.

Things Devs Need to Build

These are the things I think developers need to build, energy usage instrumentation for applications, attribution allocation algorithms, data lakes to collect energy and carbon measurements, instrumentation at the edge, IoT, mobile, whatever. Energy usage dashboards and reports. Supply chain, carbon interchange protocols, energy to carbon models, and climate impact and risk models.

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Market Growth Potential

According to Gartner’s report published on August 2nd 2023, the global database market is projected to experience a compound annual growth rate of 16.8% from 2022 to 2027, reaching a total value of $203.6 billion by 2027. Currently, the NoSQL database sector holds a relatively small market share, with legacy relational databases dominating the market.

As MongoDB’s management indicated, migrating from a traditional database to a new structured database is a time-consuming process, as it necessitates rewriting application code and then migrating the database. Consequently, the majority of NoSQL database growth is currently driven by new workloads and applications. MongoDB has recently introduced its Relational Migrator tool, which enables enterprises to migrate data from legacy vendors to MongoDB. Additionally, the tool can assist in generating sample code for data queries. This tool is expected to expedite the migration of legacy systems for enterprises. Once the migration is complete and enterprises transition to a new structured database, they are likely to experience cost savings and modernize their data structures.

If MongoDB can increasingly replace traditional databases, it stands to benefit from both the growth in new workloads and the substitution of existing workloads.

Low-Code Speeds up Application Creation

The growth of MongoDB is closely linked to the number of new applications developed, as each application necessitates a database. Therefore, as more applications are created, there is a higher demand for MongoDB’s databases. The emergence of generative AI technology is making low-code development a reality, as AI can automatically generate substantial portions of code for engineers.

According to Microsoft’s forecast, the next five years are expected to witness the creation of 500 million more applications, surpassing the total number built in the past 40 years. Moreover, modern applications increasingly require new structured databases like MongoDB, particularly as these applications often incorporate AI functionalities and process unstructured datasets. MongoDB’s document-based databases empower engineers to write code in a manner that aligns with how data is stored in the database. Consequently, I believe MongoDB stands to gain significantly from the surge in application development anticipated over the next five years.

Growth Driven by AI and Big Data

I firmly believe that the most critical factor for AI and machine learning is the availability of high-quality datasets, especially when it comes to large algorithmic machine learning models that require vast volumes of data. Additionally, the foundation of data analytics and big data lies in the utilization of unique, unstructured data. Consequently, I am highly optimistic about MongoDB’s growth potential, driven by the ongoing trends in AI and big data.

Furthermore, I see AI and Big Data adoption as catalysts for accelerating cloud migration and application modernization. To engage in AI and machine learning, enterprises are compelled to shift their workloads to the cloud. Simultaneously, they must redesign their existing applications to harness the capabilities of AI technologies. These types of application modernization efforts present MongoDB with substantial growth opportunities.

Recent Financial Results and Outlook

On June 1, 2023, MongoDB announced its Q1 FY24 earnings, showcasing robust growth figures. Their revenue surged by 29% year-over-year, while adjusted gross profits saw an impressive increase of 30.6%. Furthermore, the total number of their customers grew by 22.4% compared to the previous year.

Looking forward, MongoDB has provided guidance for FY24, projecting a revenue growth rate of 18.5% to 20% and anticipating non-GAAP income from operations within the range of $110 million to $125 million.

In my assessment, their revenue guidance for FY24 appears rather conservative, taking into account the potential deceleration in database consumption resulting from macroeconomic factors.

Risk Assessment

Competitions: I believe MongoDB faces competition from two primary categories: legacy vendors and emerging NoSQL databases. Legacy vendors may have difficulty introducing disruptive technologies that could jeopardize their established revenue streams, so they pose less concern. In contrast, the landscape includes numerous new NoSQL databases like Couchbase (BASE) and Astra DB. MongoDB enjoys a first-mover advantage and maintains a robust growth trajectory. For instance, in Q1 FY24, MongoDB witnessed a remarkable 28% year-over-year growth in its six-figure customer count, underscoring its strong market presence.

Macro Uncertainties: I believe this is a common risk shared by cloud-based software companies. During periods of macroeconomic uncertainty, enterprises often consider reducing or postponing their cloud investments and projects, which could, in turn, impact application usage. MongoDB’s revenue model is consumption-based, and any deceleration in the consumption of existing customers could potentially dampen their revenue growth.

However, it’s worth noting that MongoDB’s full-year guidance may already factor in the potential slowdown in consumption.

Negative Profitability: MongoDB is currently in the early investment stage and has not yet generated profits. Many enterprises are likely to begin adopting MongoDB’s products for non-mission-critical workloads. As they become more familiar with MongoDB’s technologies and experience their benefits, I believe these enterprises will gradually increase their usage of existing applications or extend MongoDB’s products to a broader range of applications.

As previously mentioned, the entire NoSQL database sector is still in its infancy, with relational databases continuing to dominate the market. This suggests that MongoDB and similar NoSQL databases have ample room for growth and market expansion.

Valuations

I am using a DCF model to estimate the fair value of MongoDB. My assumptions include a 21% revenue growth rate in FY24, and I anticipate that MongoDB could become a company with revenue exceeding $13 billion by FY33. On the margin side, I expect gradual margin expansion, aiming for a 30% margin by FY33, which is a typical figure for most software companies.

With these assumptions, the model forecasts a free cash flow margin of 45.2% in FY33. The working capital as a percentage of revenue is maintained at a stable 24% according to my estimates.

To calculate the discount rate, I am using the following assumptions:

Beta: 1.12. Data Source: Yahoo Finance 5-year monthly data.

Risk-Free Rate of Return: 4%. I am using 10-year US government bond yield.

Expected Market Return Premium: 7%. I am using the same assumption across my models.

Cost of debt: 10%. I am using the same assumption across my models.

Equity/(Equity + Gross Debt): 42.4% using my projected figure in FY24.

As a results, the discount rate is estimated to be 9.6% in my model, and applying the WACC, the fair value of equity value is estimated to be $33 billion for MongoDB. As such, the fair value is $460 per share as per my estimates.

Final Thoughts

I believe MongoDB’s document-based database technology has the potential to revolutionize the entire database industry, and their growth is driven by the increasing adoption of AI and big data technologies. In my view, MongoDB is positioned as a high-growth company for the next decade. Taking valuation into consideration, I would assign a ‘Strong Buy’ rating to MongoDB.

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Redis Inc recently announced the unified release of Redis 7.2, which includes several new features like auto-tiering, native triggers, and a preview of an enhanced, scalable search capability that provides increased performance for query and search scenarios, including vector similarity search (VSS).

Redis is the company’s flagship product, an open-source in-memory data store that functions as a database, cache, streaming engine, and message broker, facilitating communication between data services and applications that use different messaging protocols. With the launch of Redis 7.2, the company brings enhancements in performance, new commands, and improvements in manageability across all products and services, including open source, source-available, commercial cloud (e.g., Azure Cache for Redis, Redis Enterprise Cloud for AWS), software, and Kubernetes distributions.

The enhancement in performance applies to the auto-tiering feature, formerly known as Redis on Flash, enabling developers and operators to efficiently handle growing large datasets by maintaining frequently accessed data in memory and migrating less used data to SSD. According to the company, this results in over twofold improved throughput compared to the prior version and up to 70 percent reduced infrastructure expenses for managing extensive DRAM.

Another enhancement in response to growing challenges for generative AI applications is scalable search preview in Redis 7.2, which enables a higher query throughput, including VSS and full-text search. By blending sharding for seamless data expansion with efficient vertical scaling, the company claims it ensures optimal distributed processing across the cluster and improves query throughput by up to 16x compared to what was previously possible.

Furthermore, Redis 7.2 include triggers and functions, which makes it possible for developers to build and maintain real-time applications by moving business logic closer to the data while remaining inside Redis, providing lower latency. In a Redis blog post on triggers and functions, the authors explain:

This capability lets developers define events (called triggers) to execute functions closer to the data. That is, developers define business logic that executes in response to database events or commands. That speeds up the code and related interactions, because there is no wait to bring code from clients into the database.

Full JavaScript, as an example, looks like this:

#!js name=lib api_version=1.0

function answer(client, data) {

    return client.call(‘ping’);

}

redis.registerFunction(‘playPingPong’, answer);

The company also adds Redis Data Integration (RDI), which is a feature in preview that transforms any dataset into real-time accessibility by incrementally bringing data from multiple sources to Redis. The feature allows developers to integrate with data sources such as Oracle Database, Postgres, MySQL, and Cassandra.

Finally, for Redis Enterprise Software users, a new cluster management user interface (UI) has been introduced, modeled after the company’s cloud UI, ensuring a consistent experience that enables operators to access database information easier. In addition, by collaborating with the maintainers of five libraries (Jedis, node-redis, redis-py, NRedisStack, and Go-Redis), the company has enhanced consistency in user experience, documentation, governance, performance, and security.

Stephen O’Grady, Principal Analyst with RedMonk, said in a Redis press release:

One of the most important ways databases can improve their developer experience is by becoming more versatile by way of the addition of new capabilities and tools. By leveraging its broad developer community, Redis is looking to deliver a more versatile database and thus, a better developer experience.

However, a respondent on a Reddit thread commented:

Redis stopped development years ago. This is all closed-source overpriced enterprise software. But, like, crazily overpriced. I’m sorry I ever designed my startup with Redis as a database (and not just a basic cache). I would have never made such a deep reliance by design on an overpriced enterprise software. The OSS part of Redis is just a facade at this point.

Lastly, more information on Redis is available on the documentation pages.

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Keybank National Association OH grew its holdings in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 20.9% in the 1st quarter, according to the company in its most recent filing with the SEC. The firm owned 1,915 shares of the company’s stock after buying an additional 331 shares during the quarter. Keybank National Association OH’s holdings in MongoDB were worth $446,000 at the end of the most recent quarter.

Several other institutional investors have also recently made changes to their positions in MDB. Raymond James & Associates boosted its position in shares of MongoDB by 32.0% during the first quarter. Raymond James & Associates now owns 4,922 shares of the company’s stock valued at $2,183,000 after purchasing an additional 1,192 shares in the last quarter. PNC Financial Services Group Inc. raised its stake in MongoDB by 19.1% during the first quarter. PNC Financial Services Group Inc. now owns 1,282 shares of the company’s stock valued at $569,000 after buying an additional 206 shares during the last quarter. MetLife Investment Management LLC purchased a new position in MongoDB during the first quarter valued at $1,823,000. Panagora Asset Management Inc. raised its stake in MongoDB by 9.8% during the first quarter. Panagora Asset Management Inc. now owns 1,977 shares of the company’s stock valued at $877,000 after buying an additional 176 shares during the last quarter. Finally, Vontobel Holding Ltd. raised its stake in MongoDB by 100.3% during the first quarter. Vontobel Holding Ltd. now owns 2,873 shares of the company’s stock valued at $1,236,000 after buying an additional 1,439 shares during the last quarter. Institutional investors own 89.22% of the company’s stock.

MongoDB Stock Up 1.0 %

Shares of MDB stock opened at $362.04 on Wednesday. MongoDB, Inc. has a 1 year low of $135.15 and a 1 year high of $439.00. The company has a debt-to-equity ratio of 1.44, a quick ratio of 4.19 and a current ratio of 4.19. The stock’s 50 day simple moving average is $391.67 and its two-hundred day simple moving average is $296.73.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings data on Thursday, June 1st. The company reported $0.56 earnings per share (EPS) for the quarter, beating analysts’ consensus estimates of $0.18 by $0.38. MongoDB had a negative return on equity of 43.25% and a negative net margin of 23.58%. The business had revenue of $368.28 million for the quarter, compared to the consensus estimate of $347.77 million. During the same period in the prior year, the business posted ($1.15) earnings per share. The company’s quarterly revenue was up 29.0% compared to the same quarter last year. Equities research analysts anticipate that MongoDB, Inc. will post -2.8 earnings per share for the current year.

Analyst Ratings Changes

A number of research analysts have issued reports on MDB shares. Royal Bank of Canada lifted their price objective on MongoDB from $400.00 to $445.00 in a research note on Friday, June 23rd. 58.com restated a “maintains” rating on shares of MongoDB in a research note on Monday, June 26th. KeyCorp boosted their price target on MongoDB from $372.00 to $462.00 and gave the stock an “overweight” rating in a research note on Friday, July 21st. Guggenheim cut MongoDB from a “neutral” rating to a “sell” rating and boosted their price target for the stock from $205.00 to $210.00 in a research note on Thursday, May 25th. They noted that the move was a valuation call. Finally, The Goldman Sachs Group boosted their price target on MongoDB from $420.00 to $440.00 in a research note on Friday, June 23rd. One research analyst has rated the stock with a sell rating, three have assigned a hold rating and twenty have assigned a buy rating to the company. Based on data from MarketBeat, the company has a consensus rating of “Moderate Buy” and an average target price of $378.09.

View Our Latest Research Report on MongoDB

Insider Transactions at MongoDB

In other news, CRO Cedric Pech sold 360 shares of the stock in a transaction that occurred on Monday, July 3rd. The stock was sold at an average price of $406.79, for a total value of $146,444.40. Following the sale, the executive now directly owns 37,156 shares in the company, valued at $15,114,689.24. The sale was disclosed in a document filed with the SEC, which is available at this link. In related news, CRO Cedric Pech sold 360 shares of the stock in a transaction that occurred on Monday, July 3rd. The stock was sold at an average price of $406.79, for a total value of $146,444.40. Following the transaction, the executive now owns 37,156 shares of the company’s stock, valued at $15,114,689.24. The sale was disclosed in a legal filing with the SEC, which can be accessed through the SEC website. Also, CAO Thomas Bull sold 516 shares of the stock in a transaction that occurred on Monday, July 3rd. The stock was sold at an average price of $406.78, for a total value of $209,898.48. Following the completion of the transaction, the chief accounting officer now directly owns 17,190 shares in the company, valued at approximately $6,992,548.20. The disclosure for this sale can be found here. Over the last three months, insiders have sold 102,220 shares of company stock worth $38,763,571. 4.80% of the stock is currently owned by company insiders.

MongoDB Profile

(Free Report)

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

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This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

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Introduction:

“Relational Database Management System Market” New Research Insight Report 2023 | Spread Across 106 Pages Report which provides an in-depth analysis Based on Regions, Applications (BFSI, IT and Telecom, Retail and e-commerce, Manufacturing, Healthcare, Others), and Types (On-premise, Cloud Based). The report presents the research and analysis provided within the Relational Database Management System Market Research is meant to benefit stakeholders, vendors, and other participants in the industry. The Relational Database Management System market is expected to grow annually by magnificent (CAGR 2023 – 2030).

Who is the largest manufacturers of Relational Database Management System Market worldwide?

• TmaxSoft
• MongoDB
• SingleStore
• Microsoft Corporation
• Informix Corporation
• Sequel Pro
• PostgreSQL
• Connx Solutions
• Teradata Corporation
• Oracle Corporation
• Amazon.com Inc
• SAP SE
• Aerospike
• MariaDB
• Couchbase
• IBM

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Short Description About Relational Database Management System Market:

The Global Relational Database Management System market is anticipated to rise at a considerable rate during the forecast period, between 2023 and 2030. In 2022, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

The global Relational Database Management System market size was valued at USD 62000.0 million in 2021 and is expected to expand at a CAGR of 11.38% during the forecast period, reaching USD 118400.0 million by 2027.

The report combines extensive quantitative analysis and exhaustive qualitative analysis, ranges from a macro overview of the total market size, industry chain, and market dynamics to micro details of segment markets by type, application and region, and, as a result, provides a holistic view of, as well as a deep insight into the Relational Database Management System market covering all its essential aspects.

For the competitive landscape, the report also introduces players in the industry from the perspective of the market share, concentration ratio, etc., and describes the leading companies in detail, with which the readers can get a better idea of their competitors and acquire an in-depth understanding of the competitive situation. Further, mergers and acquisitions, emerging market trends, the impact of COVID-19, and regional conflicts will all be considered.

In a nutshell, this report is a must-read for industry players, investors, researchers, consultants, business strategists, and all those who have any kind of stake or are planning to foray into the market in any manner.

Get a Sample Copy of the Relational Database Management System Report 2023

What are the factors driving the growth of the Relational Database Management System Market?

Growing demand for below applications around the world has had a direct impact on the growth of the Relational Database Management System

• BFSI
• IT and Telecom
• Retail and e-commerce
• Manufacturing
• Healthcare
• Others

What are the types of Relational Database Management System available in the Market?

Based on Product Types the Market is categorized into Below types that held the largest Relational Database Management System market share In 2023.

• On-premise
• Cloud Based

Which regions are leading the Relational Database Management System Market?

• North America (United States, Canada and Mexico)
• Europe (Germany, UK, France, Italy, Russia and Turkey etc.)
• Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)
• South America (Brazil, Argentina, Columbia etc.)
• Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

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This Relational Database Management System Market Research/Analysis Report Contains Answers to your following Questions

• What are the global trends in the Relational Database Management System market? Would the market witness an increase or decline in the demand in the coming years?
• What is the estimated demand for different types of products in Relational Database Management System? What are the upcoming industry applications and trends for Relational Database Management System market?
• What Are Projections of Global Relational Database Management System Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export?
• Where will the strategic developments take the industry in the mid to long-term?
• What are the factors contributing to the final price of Relational Database Management System? What are the raw materials used for Relational Database Management System manufacturing?
• How big is the opportunity for the Relational Database Management System market? How will the increasing adoption of Relational Database Management System for mining impact the growth rate of the overall market?
• How much is the global Relational Database Management System market worth? What was the value of the market In 2020?
• Who are the major players operating in the Relational Database Management System market? Which companies are the front runners?
• Which are the recent industry trends that can be implemented to generate additional revenue streams?
• What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Relational Database Management System Industry?

Relational Database Management System Market – Covid-19 Impact and Recovery Analysis:

We were monitoring the direct impact of covid-19 in this market, further to the indirect impact from different industries. This document analyzes the effect of the pandemic on the Relational Database Management System market from a international and nearby angle. The document outlines the marketplace size, marketplace traits, and market increase for Relational Database Management System industry, categorised with the aid of using kind, utility, and patron sector. Further, it provides a complete evaluation of additives concerned in marketplace improvement in advance than and after the covid-19 pandemic. Report moreover done a pestel evaluation within the business enterprise to study key influencers and boundaries to entry.

Our studies analysts will assist you to get custom designed info to your report, which may be changed in phrases of a particular region, utility or any statistical info. In addition, we’re constantly inclined to conform with the study, which triangulated together along with your very own statistics to make the marketplace studies extra complete for your perspective.

Final Report will add the analysis of the impact of Russia-Ukraine War and COVID-19 on this Relational Database Management System Industry.

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Detailed TOC of Global Relational Database Management System Market Research Report, 2023-2030

1 Market Overview
1.1 Product Overview and Scope of Relational Database Management System
1.2 Classification of Relational Database Management System by Type
1.2.1 Overview: Global Relational Database Management System Market Size by Type: 2017 Versus 2021 Versus 2030
1.2.2 Global Relational Database Management System Revenue Market Share by Type in 2021
1.3 Global Relational Database Management System Market by Application
1.3.1 Overview: Global Relational Database Management System Market Size by Application: 2017 Versus 2021 Versus 2030
1.4 Global Relational Database Management System Market Size and Forecast
1.5 Global Relational Database Management System Market Size and Forecast by Region
1.6 Market Drivers, Restraints and Trends
1.6.1 Relational Database Management System Market Drivers
1.6.2 Relational Database Management System Market Restraints
1.6.3 Relational Database Management System Trends Analysis

2 Company Profiles
2.1 Company
2.1.1 Company Details
2.1.2 Company Major Business
2.1.3 Company Relational Database Management System Product and Solutions
2.1.4 Company Relational Database Management System Revenue, Gross Margin and Market Share (2019, 2020, 2021 and 2023)
2.1.5 Company Recent Developments and Future Plans

3 Market Competition, by Players
3.1 Global Relational Database Management System Revenue and Share by Players (2019,2020,2021, and 2023)
3.2 Market Concentration Rate
3.2.1 Top3 Relational Database Management System Players Market Share in 2021
3.2.2 Top 10 Relational Database Management System Players Market Share in 2021
3.2.3 Market Competition Trend
3.3 Relational Database Management System Players Head Office, Products and Services Provided
3.4 Relational Database Management System Mergers and Acquisitions
3.5 Relational Database Management System New Entrants and Expansion Plans

4 Market Size Segment by Type
4.1 Global Relational Database Management System Revenue and Market Share by Type (2017-2023)
4.2 Global Relational Database Management System Market Forecast by Type (2023-2030)

5 Market Size Segment by Application
5.1 Global Relational Database Management System Revenue Market Share by Application (2017-2023)
5.2 Global Relational Database Management System Market Forecast by Application (2023-2030)

6 Regions by Country, by Type, and by Application
6.1 Relational Database Management System Revenue by Type (2017-2030)
6.2 Relational Database Management System Revenue by Application (2017-2030)
6.3 Relational Database Management System Market Size by Country
6.3.1 Relational Database Management System Revenue by Country (2017-2030)
6.3.2 United States Relational Database Management System Market Size and Forecast (2017-2030)
6.3.3 Canada Relational Database Management System Market Size and Forecast (2017-2030)
6.3.4 Mexico Relational Database Management System Market Size and Forecast (2017-2030)

7 Research Findings and Conclusion

8 Appendix
8.1 Methodology
8.2 Research Process and Data Source
8.3 Disclaimer

9 Research Methodology

10 Conclusion

Continued….

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.NET MAUI is now available in .NET 8 Preview 7. This version introduces keyboard accelerators and more bug fixes and enhancements. This is the final familiarisation release of .NET 8. With this announcement came further community concerns about the framework.

The keyboard accelerators allow you to assign keyboard shortcuts to menus, even hidden ones, and link them to any part of the user interface. This is demonstrated by the ability to add an accelerator using the MenuItem.Accelerator property to a window-level menu on a page. 

Keyboard accelerators (Source: Microsoft blog)

To express the accelerator in C# code instead of using the included property, it can be formulated as follows:

MenuItem.SetAccelerator(AddProductMenu, Accelerator.FromString("ctrl+a"));

The primary focus of this release centres around addressing bugs and enhancing overall quality. For a comprehensive compilation of changes, it is advisable to refer to the release notes for version 8.0.0-preview.7.8842.

In the new release memory leaks within various UI controls, including Border, Editor, and Entry, on diverse platforms (iOS, Android, Windows) have been meticulously addressed, resulting in improved memory management and application stability. User interface controls such as Border, WebView and Entry have been updated in terms of behaviour, performance and customisation options across iOS, Android and Windows platforms. Together, these enhancements enrich user interaction, providing a more user-friendly and feature-rich environment.

Moreover, Platform-specific enhancements have been implemented across the major platforms (iOS, Android, Windows). These tailored enhancements, from resolving memory leaks to optimising performance, ensure that applications run smoothly and efficiently in various environments.

In addition, data entry and user interaction have been improved with enhancements including cursor behaviour in text fields, menu key accelerator integration and permutations in InputTransparent behaviour. Together, these strategic updates enhance user engagement and improve the overall usability of the application.

As previously mentioned, this announcement prompted discussions. The same thing happened with the .NET MAUI in .NET 8 Preview 5. Developers indicate that the framework is not production-ready. Among others, Stephen Winstanley shared some thoughts there:

After months of fighting with Maui to get on Windows and Android, I spend half my time ‘fixing’ what was working before. I wonder if all the hard work has been for nothing sometimes with Maui and wish I had never heard of it and could have just kept the original fully working Xamarin applications.

The community appreciates the team’s commitment and understands this tool’s complexity. However, they think Microsoft should be open about it, that the framework is not yet ready for complex applications, at least not for anything more complicated than a ToDo application.

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The intersection of large language models and graph databases is one that’s rich with possibilities. The folks at property graph database maker Neo4j today took a first step in realizing those possibilities for its customers by announcing the capability to store vector embeddings, enabling it to function as long-term memory for an LLM such as OpenAI’s GPT.

While graph databases and large language models (LLMs) live at separate ends of the data spectrum, they bear some similarity to each other in terms of how humans interact with them and use them as knowledge bases.

A property graph database, such as Neo4j’s, is an extreme example of a structured data store. The node-and-edge graph structure excels at helping users to explore knowledge about entities (defined as nodes) and their relationships (defined as edges) to other entities. At runtime, a property graph can find answers to questions by quickly traversing pre-defined connections to other nodes, which is more efficient than, say, running a SQL join in a relational database.

An LLM, on the other hand, is an extreme example of unstructured data store. At the core of an LLM is a neural network that’s been trained primarily on a massive amount of human-generated text. At runtime, an LLM answers questions by generating sentences one word at a time in a way that best matches the words it encountered during training.

Whereas the knowledge in the graph database is contained in the connections between labeled nodes, the knowledge in the LLM is contained in the human-generated text. So while graphs and LLMs may be called upon to answer similar knowledge-related questions, they work in entirely different ways.

The folks at Neo recognized the potential benefits from attacking these types of knowledge challenges from both sides of the structured data spectrum. “We see value in combining the implicit relationships uncovered by vectors with the explicit and factual relationships and patterns illuminated by graph,” Emil Eifrem, co-founder and CEO of Neo4j, said in a press release today.

Neo4j Chief Scientist Jim Webber sees three patterns for how customers can integrate graph databases and LLMs.

The first is using the LLM as a handy interface to interact with your graph database. The second is creating a graph database from the LLM. The third is training the LLM directly from the graph database. “At the moment, those three cases seem very prevalent,” Webber says.

How can these integrations work in the real world? For the first type, Webber used an example of the query “Show me a movie from my favorite actor.” Instead of prompting the LLM with a load of text explaining who your favorite actor is, the LLM would generate a query for the graph database, where the answer “Michael Douglas” can be easily deduced from the structure of the graph, thereby streamlining the interaction.

For the second use case, Weber shared some of the work currently being done by BioCypher. The organization is using LLMs to build a model of drug interactions based on large corpuses of data. It’s then using the probabilistic connections in the LLM to build a graph database that can be query in a more deterministic manner.

BioCypher is using LLMs because it “does the natural language hard stuff,” Webber says. “But what they can’t do is then query that large language model for insight or answers, because it’s opaque and it might hallucinate, and they don’t like that. Because in the regulatory environment saying ‘Because this box of randomness told us so’ is not good enough.”

Webber shared an example of the last use case–training a LLM based on curated data in the knowledge graph. Weber says he recently met with the owner of an Indonesian company that is building custom chatbots based on data in the Neo4j knowledge graph.

“You can ask it question about the latest Premiere League football season, and it would have no idea what  you’re talking about,” Webber says the owner told him. “But if you ask a question about my products, it answers really precisely, and my customer satisfaction is going through the roof.

In a blog post today, Neo4j Chief Product Officer Sudhir Hasbe says the integration of LLMs and graph will help customers in enhancing fraud detection, providing better and more personalized recommendations, and for discovering new answers. “…[V]ector search provides a simple approach for quickly finding contextually related information and, in turn, helps teams uncover hidden relationships,” he writes. “Grounding LLMs with a Neo4j knowledge graph improves accuracy, context, and explainability by bringing factual responses (explicit) and contextually relevant (implicit) responses to the LLM.”

There’s a “yin and yang” to knowledge graphs and LLMs, Webber says. In some situations, the LLM are the right tool for the job. But in other cases–such as where more transparency and determinism is needed–then moving up the structured data stack a full-blown knowledge graph is going to be a better solution.

“And at the moment those three cases seem very prevalent,” he says. “But if we have another conversation in one year…  honestly don’t know where this is going, which is odd for me, because I’ve been around a bit in IT and I usually have a good sense for where things are going, but the future feels very unwritten here with the intersection of knowledge graphs and LLMs.”

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