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As of September 22, 2023, there has been a notable increase in unusual options activity surrounding MongoDB, indicating the involvement of influential investors targeting a price range between $330.0 and $600.0 over the past three months. The options history for MongoDB reveals that 46% of investors have opened trades with bullish expectations, while 53% have taken a bearish stance. Among the significant options observed is a bullish PUT SWEEP trade set to expire on April 19, 2024, with a strike price of $390.00. This particular trade has a total trade price of $322.8K and an open interest of 1.

Currently, the price of MDB stands at $336.5, experiencing a slight increase of 0.86%. The RSI indicators suggest that the underlying stock may be approaching oversold territory. Looking ahead, the next earnings report for MongoDB is expected to be released in 74 days.

MDB Stock Analysis: Positive Revenue Growth but Concerns Remain Over Profitability

On September 22, 2023, shares of MongoDB Inc. (MDB) showed a positive performance, with the stock opening at $338.09, higher than the previous day’s closing price of $333.64. Throughout the trading day, MDB had a range of $331.32 to $339.38. The stock experienced a volume of 30,050 shares, which was significantly lower than the average volume of 1,682,832 shares over the past three months.

MDB, a technology services company in the packaged software industry, has a market capitalization of $25.8 billion. The company’s financials have shown mixed results, with a negative earnings growth of -5.89% in the previous year. However, the current year has seen a significant improvement in earnings growth, with a positive growth rate of +92.12%. Looking ahead, the company is expected to maintain a steady growth rate of +8.00% over the next five years.

In terms of revenue growth, MDB has performed well, with a growth rate of +46.95% in the last year. This indicates that the company has been successful in increasing its sales and attracting more customers. However, the company’s profitability has been a concern, as it reported a net loss of -$345.4 million in the previous year. The net profit margin stands at -26.90%, indicating that the company’s expenses are higher than its revenues.

When analyzing the stock’s valuation, the price-to-earnings (P/E) ratio is not available (NM), suggesting that the company may not have positive earnings or that it is not profitable. The price-to-sales ratio is 11.45, indicating that investors are willing to pay $11.45 for every dollar of sales generated by the company. The price-to-book ratio is 34.22, which suggests that the stock may be overvalued based on its book value.

On September 22, 2023, MDB’s stock showed a slight increase, with a change of +0.55 and a percentage change of +0.11%. This suggests that the stock had a relatively stable trading day compared to other companies in the sector.

Looking ahead, the next reporting date for MDB is scheduled for December 6, 2023. Analysts forecast an earnings per share (EPS) of $0.27 for the current quarter. In the previous year, the company reported annual revenue of $1.3 billion, which indicates a strong performance in terms of sales. However, the company needs to focus on improving its profitability and reducing its net loss margin.

In conclusion, MDB’s stock performance on September 22, 2023, showed a slight increase, and the company has demonstrated positive revenue growth. However, the company’s profitability remains a concern, as it reported a net loss in the previous year. Investors should closely monitor the company’s financials and future earnings reports to assess its long-term potential.

MongoDB Inc (MDB) Stock Forecast: Analysts Predict a Potential 34.13% Increase on September 22, 2023

On September 22, 2023, MongoDB Inc (MDB) stock is expected to perform well based on the predictions of 23 analysts. According to data from CNN Money, these analysts have provided a 12-month price forecast for MDB with a median target of $450.00. The high estimate is $500.00, while the low estimate is $250.00. This indicates a potential increase of 34.13% from the last recorded price of $335.50.

The consensus among 28 polled investment analysts is to buy stock in MongoDB Inc. This rating has remained steady since September, indicating a consistent positive sentiment towards the company’s stock. Investors seem to have confidence in the future prospects of MDB.

In terms of financial performance, MongoDB Inc reported earnings per share of $0.27 for the current quarter. This indicates the company’s profitability and ability to generate earnings for its shareholders. Additionally, MongoDB Inc reported sales of $389.8 million for the same period, reflecting the company’s strong revenue generation.

Investors can expect more insights into MongoDB Inc’s financial performance when the company reports its next earnings on December 6. This reporting date will provide a more comprehensive understanding of the company’s financial health and its ability to meet market expectations.

Overall, based on the predictions of analysts and the consensus among investment analysts, MDB stock is expected to perform well on September 22, 2023. The positive sentiment towards the company, along with its strong financial performance, indicates a potentially promising future for MongoDB Inc. Investors may want to consider buying stock in the company, keeping in mind the potential for a 34.13% increase in the stock price according to the median target estimate.

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Article originally posted on InfoQ. Visit InfoQ

Security researchers at cloud-security company Wiz discovered a data leak affecting Microsoft’s AI GitHub repository, including a huge amount of private data and a disk backup of two employees’ workstations with sensitive data.

As Wiz researchers found out, Microsoft researchers uploaded instructions to access some AI models for image recognition to Microsoft AI research’s robust-models-transfer repository on GitHub and provided an Azure Storage download link. Unfortunately, the shared URL granted permissions on the entire storage account.

Our scan shows that this account contained 38TB of additional data — including Microsoft employees’ personal computer backups. The backups contained sensitive personal data, including passwords to Microsoft services, secret keys, and over 30,000 internal Microsoft Teams messages from 359 Microsoft employees.

To make things worse, the access URL was misconfigured in a way it enabled “full control” on the account, which would allow potential attackers to modify, overwrite, or delete existing files. Additionally, the pickle formatter Python uses to output model data files is prone to arbitrary code execution by design, meaning that:

An attacker could have injected malicious code into all the AI models in this storage account, and every user who trusts Microsoft’s GitHub repository would’ve been infected by it.

As Wiz researchers noted, however, the storage account was not exposed to the public thanks to the specific mechanism used, SAS tokens, which allows to create a shareable link to access an Azure storage account without the account appearing public. SAS tokens, say Wiz researchers, are often used improperly by granting excessive privileges and/or by setting very long lifetimes and lack management facilities.

Once notified of the incident, the leakage was promptly fixed by revoking the SAS token, which required revoking the entire account key.

While the attack itself did only coincidentally relate to AI, some Hacker News comments pointed at the possibility this kind of incidents could lead to data poisoning attacks tampering with AI models, for example a spoiled GPT which inserted certain vulnerabilities into the code it generates. This is though just a theoretical possibility, given the amount of “poison” one should inject into the training data, remarked another commenter.

Other commenters raised serious concerns about the widespread usage of pickle as a data formatter, which is unfortunately the only available option for many of the major ML packages.

Do not miss the original article if you are interested in the full detail about Wiz’s findings and their recommendations.

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A whale with a lot of money to spend has taken a noticeably bearish stance on MongoDB.

Looking at options history for MongoDB MDB we detected 15 strange trades.

If we consider the specifics of each trade, it is accurate to state that 46% of the investors opened trades with bullish expectations and 53% with bearish.

From the overall spotted trades, 11 are puts, for a total amount of $965,695 and 4, calls, for a total amount of $408,681.

What’s The Price Target?

Taking into account the Volume and Open Interest on these contracts, it appears that whales have been targeting a price range from $330.0 to $600.0 for MongoDB over the last 3 months.

Volume & Open Interest Development

In terms of liquidity and interest, the mean open interest for MongoDB options trades today is 119.0 with a total volume of 347.00.

In the following chart, we are able to follow the development of volume and open interest of call and put options for MongoDB’s big money trades within a strike price range of $330.0 to $600.0 over the last 30 days.

MongoDB Option Volume And Open Interest Over Last 30 Days

Biggest Options Spotted:

Where Is MongoDB Standing Right Now?

• With a volume of 492,675, the price of MDB is up 0.86% at $336.5.
• RSI indicators hint that the underlying stock may be approaching oversold.
• Next earnings are expected to be released in 74 days.

What The Experts Say On MongoDB:

• Barclays has decided to maintain their Overweight rating on MongoDB, which currently sits at a price target of $450.
• Piper Sandler has decided to maintain their Overweight rating on MongoDB, which currently sits at a price target of $425.
• Canaccord Genuity has decided to maintain their Buy rating on MongoDB, which currently sits at a price target of $450.
• Argus Research has decided to maintain their Buy rating on MongoDB, which currently sits at a price target of $484.
• Guggenheim has decided to maintain their Sell rating on MongoDB, which currently sits at a price target of $250.

Options are a riskier asset compared to just trading the stock, but they have higher profit potential. Serious options traders manage this risk by educating themselves daily, scaling in and out of trades, following more than one indicator, and following the markets closely.

If you want to stay updated on the latest options trades for MongoDB, Benzinga Pro gives you real-time options trades alerts.

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

Posted on mongodb google news. Visit mongodb google news

Roth Financial Partners LLC acquired a new stake in MongoDB, Inc. (NASDAQ:MDB – Free Report) in the second quarter, according to the company in its most recent disclosure with the Securities and Exchange Commission (SEC). The fund acquired 500 shares of the company’s stock, valued at approximately $205,000. MongoDB accounts for 0.1% of Roth Financial Partners LLC’s investment portfolio, making the stock its 22nd largest position.

A number of other hedge funds and other institutional investors have also made changes to their positions in the business. 1832 Asset Management L.P. grew its position in MongoDB by 3,283,771.0% during the fourth quarter. 1832 Asset Management L.P. now owns 1,018,000 shares of the company’s stock valued at $200,383,000 after acquiring an additional 1,017,969 shares during the period. Price T Rowe Associates Inc. MD grew its position in MongoDB by 13.4% during the first quarter. Price T Rowe Associates Inc. MD now owns 7,593,996 shares of the company’s stock valued at $1,770,313,000 after acquiring an additional 897,911 shares during the period. Renaissance Technologies LLC grew its position in MongoDB by 493.2% during the fourth quarter. Renaissance Technologies LLC now owns 918,200 shares of the company’s stock valued at $180,738,000 after acquiring an additional 763,400 shares during the period. Norges Bank purchased a new stake in MongoDB during the fourth quarter valued at about $147,735,000. Finally, Champlain Investment Partners LLC purchased a new stake in shares of MongoDB in the first quarter worth about $89,157,000. Hedge funds and other institutional investors own 88.89% of the company’s stock.

Insider Buying and Selling

In other MongoDB news, CAO Thomas Bull sold 516 shares of the stock in a transaction on Monday, July 3rd. The stock was sold at an average price of $406.78, for a total transaction of $209,898.48. Following the completion of the transaction, the chief accounting officer now directly owns 17,190 shares of the company’s stock, valued at approximately $6,992,548.20. The sale was disclosed in a filing with the SEC, which is accessible through the SEC website. In other MongoDB news, Director Hope F. Cochran sold 2,174 shares of the stock in a transaction on Friday, September 15th. The stock was sold at an average price of $361.31, for a total transaction of $785,487.94. Following the completion of the transaction, the director now directly owns 9,722 shares of the company’s stock, valued at approximately $3,512,655.82. The sale was disclosed in a filing with the SEC, which is accessible through the SEC website. Also, CAO Thomas Bull sold 516 shares of the stock in a transaction on Monday, July 3rd. The shares were sold at an average price of $406.78, for a total value of $209,898.48. Following the transaction, the chief accounting officer now directly owns 17,190 shares of the company’s stock, valued at $6,992,548.20. The disclosure for this sale can be found here. Insiders sold 104,694 shares of company stock valued at $41,820,161 in the last three months. 4.80% of the stock is currently owned by corporate insiders.

Wall Street Analysts Forecast Growth

MDB has been the topic of several recent analyst reports. Capital One Financial initiated coverage on MongoDB in a research note on Monday, June 26th. They issued an “equal weight” rating and a $396.00 price target on the stock. Oppenheimer upped their price objective on MongoDB from $430.00 to $480.00 and gave the company an “outperform” rating in a research note on Friday, September 1st. 22nd Century Group restated a “maintains” rating on shares of MongoDB in a research note on Monday, June 26th. Sanford C. Bernstein upped their price objective on MongoDB from $424.00 to $471.00 in a research note on Sunday, September 3rd. Finally, Guggenheim upped their price objective on MongoDB from $220.00 to $250.00 and gave the company a “sell” rating in a research note on Friday, September 1st. One analyst has rated the stock with a sell rating, three have given a hold rating and twenty-one have given a buy rating to the company. According to MarketBeat.com, MongoDB presently has an average rating of “Moderate Buy” and a consensus target price of $418.08.

Check Out Our Latest Report on MongoDB

MongoDB Stock Performance

MDB stock traded up $2.82 on Friday, reaching $336.44. The company’s stock had a trading volume of 498,795 shares, compared to its average volume of 1,660,096. MongoDB, Inc. has a 52 week low of $135.15 and a 52 week high of $439.00. The company has a market capitalization of $24.00 billion, a P/E ratio of -96.42 and a beta of 1.11. The firm has a fifty day moving average of $382.03 and a two-hundred day moving average of $319.28. The company has a debt-to-equity ratio of 1.29, a current ratio of 4.48 and a quick ratio of 4.48.

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

Posted on mongodb google news. Visit mongodb google news

DVF Software Engineer ESSENTIAL SKILLS REQUIREMENTS: Key Skills (or equivalent): Building CLI Tools Building Python Libraries Python Unit Testing Using Public Cloud Services Java Exposure Restful services CI/CD Understanding of Agile ways of working Strong Debugging skills ADVANTAGEOUS SKILLS REQUIREMENTS: MongoDB Exposure AWS services (e.g., SNS, SQS, S3, ECS, Lambda, KMS, Secret Manager, CloudWatch, CDK, […] TypeScript, NodeJS Atlassian APIs Typescript Being able to talk and think at the strategic as well as technical level – considering different decisions and their long term impact, then turning to code details.

Noticing constraints, and opportunities for improvement – and passionately pursuing solutions. Building purpose and ownership – striving for meaning and excellence and delivering solutions that you are proud of.

Sound understanding of computer science. Great code organisation and quality Commitment to infrastructure as code

Read more:
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DVF Software Engineer 0059 – Gauteng PretoriaDVF Software Engineer ESSENTIAL SKILLS REQUIREMENTS: Key Skills (or equivalent): Building CLI Tools Building Python Libraries Python Unit Testing Using Public Cloud Services Java Exposure Restful services CI/CD Understanding of Agile ways of working Strong Debugging skills ADVANTAGEOUS SKILLS REQUIREMENTS: MongoDB Exposure AWS services (e.g., SNS, SQS, S3, ECS, Lambda, KMS, Secret Manager, CloudWatch, CDK, […]

Senior Software Engineer (Android, IOS, Nodejs, Typescript) – Gauteng JohannesburgENVIRONMENT: Our customer operates as a communication platform that links various service providers to swiftly initiate a synchronized reaction. They are currently seeking a Software Engineer, shaping the technological landscape of their company, enabling them to provide cutting-edge solutions and services to their clients in the security industry. You will be responsible for the development, […]

Software Engineer 0198 – Gauteng PretoriaBackend Cloud Developer (Software Engineer)Technical knowledge: Familiarity with Microservices Architecture, Cloud and Container Architecture At least 6 years’ worth of experience using back-end technologies such as Java Javascript / TypeScript / Node.js Python Experience with cloud technologies (Amazon AWS is strongly preferred): Compute: Kubernetes and Severless API Gateway, CloudWatch, DynamoDB, SQS, SNS, Kinesis, S3, etc. […]

Software Engineer (C#.Net) (CPT Hybrid)ENVIRONMENT: A passionate coder with 3 years’ experience in Microsoft C .Net is wanted to fill the role of a Software Engineer joining the team of a fast-paced Asset Management Specialist. You’ll enjoy working in a mature development environment where you will participate in design and planning sessions, do code reviews, monitor continuous integration builds […]

Software Engineer (C#.Net) (CPT Hybrid) – Western Cape BellvilleENVIRONMENT: A passionate coder with 3 years’ experience in Microsoft C .Net is wanted to fill the role of a Software Engineer joining the team of a fast-paced Asset Management Specialist. You’ll enjoy working in a mature development environment where you will participate in design and planning sessions, do code reviews, monitor continuous integration builds […]

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MongoDB (MDB Quick QuoteMDB – Free Report) has introduced a fresh initiative called MongoDB Atlas for Manufacturing and Automotive, which is aimed at supporting organizations in leveraging real-time data for innovation and creating applications that take the advantage of intelligent and interconnected technology.

This offering encompasses experts-led innovation workshops, customized technology partnerships and specialized knowledge accelerators to offer tailored training pathways designed for diverse use cases that developers in these sectors encounter.

Organizations can now tap into the potential of MongoDB Atlas for Manufacturing and Automotive to reshape user experiences and revolutionize manufacturing processes. This includes devising novel strategies to tackle industry-specific challenges through innovation workshops and upskilling teams to facilitate the swift creation of modern applications.

Apart from introducing the MongoDB Atlas for Manufacturing and Automotive initiative, MDB has also become a launch partner for the Amazon’s (AMZN Quick QuoteAMZN – Free Report) division, Amazon Web Services (“AWS”) Automotive Competency.

To achieve this recognition, MongoDB underwent a thorough technical validation process to ensure that MDB and AWS can collaborate effectively to assist automotive companies in working toward a future that is autonomous, customer-focused, safe and sustainable. This development further strengthens the company’s enduring partnership with AWS, which includes making MongoDB Atlas accessible through the AWS Marketplace.

Shares of MDB have gained 69.5% year to date compared with the Zacks Computer and Technology sector’s rise of 34.7% in the same period due to long standing partnerships with giants like AWS.

MongoDB Enters Into a Highly Competitive Automotive Cloud Market

The global automotive cloud market size reached $22.5 billion in 2022. According to an IMARC Group report, the market is expected to reach $60.2 billion by 2028, witnessing a CAGR of 17.30% over the 2023-2028 period. Since the introduction of connected vehicles, the rapid evolution of automotive cloud computing, autonomous driving capabilities and intelligent automotive features has been quite remarkable. Car manufacturers worldwide are increasingly dedicating resources to integrating cloud technology to enhance the driving experience.

The incorporation of cloud technology in automobiles holds the potential to save lives by preventing accidents and enables vehicles to communicate effectively with one another. It’s fair to assert that cloud computing, particularly distributed cloud systems, is becoming an essential technology for contemporary and intelligent automobiles.

This Zacks Rank #3 (Hold) company faces tough competition from cloud providers like Salesforce (CRM Quick QuoteCRM – Free Report) , Oracle (ORCL Quick QuoteORCL – Free Report) and AWS. You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.

Salesforce’s automotive CRM is designed to enable active listening to customers and engagement across diverse touchpoints, encompassing online platforms, mobile devices, social media, showroom interactions and connected vehicle interfaces. It also helps in establishing a lasting relationship with customers.

Oracle’s top-tier cloud solutions provide a platform for handling the specific and distinctive core business needs of the automotive industry. Leveraging advanced digital and analytical technologies, its solutions are designed to enhance operational efficiency across various domains, such as supply chain, enterprise resource planning, engineering, product lifecycle management, production, asset management, customer experience and service management.

AWS offers a comprehensive range of specialized services and solutions tailored for the automotive industry. These encompass software-defined vehicle technology, connected mobility solutions, autonomous mobility systems, digital customer engagement tools, manufacturing support, supply chain management and product engineering solutions for companies of all scales from emerging startups to established global original equipment manufacturers.

MongoDB has already acquired customers like HiveMQ, Share Now and Digitread in the automotive and manufacturing sector. The company’s positive impact in this sector is expected to boost the top line as well as total customers.

The Zacks Consensus Estimate for MDB’s fiscal 2024 revenues is pegged at $1.61 billion, indicating year-over-year growth of 48.34%. The Zacks Consensus Estimate for total customers is pegged at 47,871, indicating a year-over-year increase of 17.3%.

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

Posted on mongodb google news. Visit mongodb google news

DVF Software Engineer ESSENTIAL SKILLS REQUIREMENTS: Key Skills (or equivalent): Building CLI Tools Building Python Libraries Python Unit Testing Using Public Cloud Services Java Exposure Restful services CI/CD Understanding of Agile ways of working Strong Debugging skills ADVANTAGEOUS SKILLS REQUIREMENTS: MongoDB Exposure AWS services (e.g., SNS, SQS, S3, ECS, Lambda, KMS, Secret Manager, CloudWatch, CDK, […] TypeScript, NodeJS Atlassian APIs Typescript Being able to talk and think at the strategic as well as technical level – considering different decisions and their long term impact, then turning to code details.

Noticing constraints, and opportunities for improvement – and passionately pursuing solutions. Building purpose and ownership – striving for meaning and excellence and delivering solutions that you are proud of.

Sound understanding of computer science. Great code organisation and quality Commitment to infrastructure as code

Read more:
ITOnlineSA »

Software Engineer 0198 – IT-OnlineBackend Cloud Developer (Software Engineer)Technical knowledge: Familiarity with Microservices Architecture, Cloud and Container Architecture At least 6 years’ worth of experience using back-end technologies such as Java Javascript / TypeScript / Node.js Python Experience with cloud technologies (Amazon AWS is strongly preferred): Compute: Kubernetes and Severless API Gateway, CloudWatch, DynamoDB, SQS, SNS, Kinesis, S3, etc. […]

Senior Software Engineer (Android, IOS, Nodejs, Typescript) – Gauteng JohannesburgENVIRONMENT: Our customer operates as a communication platform that links various service providers to swiftly initiate a synchronized reaction. They are currently seeking a Software Engineer, shaping the technological landscape of their company, enabling them to provide cutting-edge solutions and services to their clients in the security industry. You will be responsible for the development, […]

Software Engineer 0198 – Gauteng PretoriaBackend Cloud Developer (Software Engineer)Technical knowledge: Familiarity with Microservices Architecture, Cloud and Container Architecture At least 6 years’ worth of experience using back-end technologies such as Java Javascript / TypeScript / Node.js Python Experience with cloud technologies (Amazon AWS is strongly preferred): Compute: Kubernetes and Severless API Gateway, CloudWatch, DynamoDB, SQS, SNS, Kinesis, S3, etc. […]

Software Engineer (C#.Net) (CPT Hybrid)ENVIRONMENT: A passionate coder with 3 years’ experience in Microsoft C .Net is wanted to fill the role of a Software Engineer joining the team of a fast-paced Asset Management Specialist. You’ll enjoy working in a mature development environment where you will participate in design and planning sessions, do code reviews, monitor continuous integration builds […]

Software Engineer (C#.Net) (CPT Hybrid) – Western Cape BellvilleENVIRONMENT: A passionate coder with 3 years’ experience in Microsoft C .Net is wanted to fill the role of a Software Engineer joining the team of a fast-paced Asset Management Specialist. You’ll enjoy working in a mature development environment where you will participate in design and planning sessions, do code reviews, monitor continuous integration builds […]

Kasi soccer skills: Mamelodi Sundowns’ Khuliso Mudau kasi skills Showboating, dribbles and pure kasi soccer skills are an important part of this beautiful game that is loved all over the world.

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Transcript

Wang: We’re going to talk about going from cloud hosted to cloud native. It all starts when you say, I want to put an application on the cloud. It seems really simple. Step one, build the application. Step two, figure out which cloud you want to put it on. Step three, run in production. After this process, you say to yourself, that’s great. I’ve now built a cloud native application. Let’s look at the definition. Cloud native is building and running scalable applications in modern dynamic environments, such as public, private, and hybrid clouds. This is a really great definition from the Cloud Native Computing Foundation. When you think about the scenario that I just outlined, let’s answer the question, is it scalable? Are we really in a modern dynamic environment? The answer is, kind of. The reality is that when you put an application on the cloud, there are a lot of obstacles that come up in the process, and the first being, what operating system should I run it on? Should I even run it on the operating system in the first place? Next, you think about how you should package it. Should it be a function? Should it be a container? What should it be? Next, you think about its configuration? How do I configure it to run on certain infrastructure? How do I make sure it’s routed correctly? The next thing that we think about is, does it even make any sense to secure it? Is it something that is running on a private environment? Is it something that’s running potentially publicly? Are there database passwords and credentials that we should be aware of while it runs?

Then, we come to the CI framework. We need to deploy it to the cloud somehow, and deploying to the cloud is complicated, especially when you have network routing considerations in place, and you have to think about it really carefully. Next, we say, ok, it’s on cloud. We’ve done this process. We’ve built our CI framework so that it can deploy to cloud. We’ve taken all of these steps. We’ve thought carefully about these requirements. It must be cloud native. Then you come back to your cloud bill, and your cloud bill shows to you, it’s pretty expensive to run this application. Then you go back to the drawing board. You rearchitect the application, thinking to yourself, maybe now we consider it cloud native, because it’s after all, taking advantage. All of these service offerings. We’ve done all of our research. We’ve done the engineering work to make sure that we’ve optimized it. The reality is, it’s probably not cloud native. The application that you think about putting on cloud isn’t going to be cloud native. Instead, it’s cloud hosted. You’ve built and run an application in an environment like a public, private, and hybrid cloud. It’s not really scalable. It’s perhaps not really the most modern or dynamic application in the first place.

Here, I am going to answer this question, what does it mean to go from cloud hosted to cloud native? Over the years, I’ve realized that cloud native architecture is very complicated. It has a changing definition. While I think that the CNCF’s definition is really useful, and it’s actually probably more thorough, it’s also not as nuanced as the actual implementation. We’re going to talk about the practices and patterns that you can distill down and identify certain architectures as cloud native, without necessarily going to the specific technology and saying that technology is going to give me a cloud native architecture. When you boil down some of these practices to some foundational pieces, and these you can use to build up a cloud native architecture. I have a couple cloud native considerations that I think about. These are some architectural capabilities that I consider important for a cloud native architecture. First is adaptability. Second is observability. Third is immutability. Fourth is elasticity. Fifth is changeability. We’re going to go through each of these, and I’ll give an example of how they are important to a cloud native architecture.

Adaptability – Adjust Between Environments

The first is adaptability. Adaptability is the ability to adjust between environments. This isn’t just environments as in development, staging, and production. This is also environments, whether it be from a public cloud, private cloud, or hybrid environment, or multi-cloud environment. The idea is that you need to be able to adapt your dependencies between different kinds of environments. Let’s take this example. Imagine that I have a data center and I run Kubernetes in that data center, it could be on OpenShift or something else. I also have a Kubernetes cluster that I’ve run on a managed service. This Kubernetes cluster is managed by a public cloud. They’re Kubernetes, this makes it pretty easy for me. I could take one application, run it in my data center, as long as it’s on Kubernetes, I can just bring it to the public cloud. Both are equivalent, in theory. This principle is adapting by abstraction. The idea is that a cloud native architecture often relies on abstractions to improve adaptability. If you need to adjust or change applications between environments, you’re going to use an abstraction to do that.

There is a bit of a caveat to this. Just because you have Kubernetes in your data center and Kubernetes in a public cloud does not mean that it is an easy path to adapt between environments. This is where I think there are some foundational practices that you need in place in order for the adaptability to exist and thus then, for you to have a cloud native architecture. The first is that when you move an application, or some application manifest from one Kubernetes to another, you have to be concerned about versions. Not all Kubernetes resources are available in every version. Second is image. If you’ve ever worked across multiple clouds with Kubernetes clusters, managed Kubernetes often have different kinds of container registries or image pull registry options compared to other ones.

The other important path or obstacle in the path of moving from one Kubernetes cluster to another often involves customization. Many times, when you’ve worked in a data center environment, and you have your own Kubernetes, you have customized certain workflows so that they are aligned with your organization’s workflows. This could be custom resource definitions or other resources. They don’t map perfectly to a public cloud, so then you have to adapt them as well. Persistence becomes an obstacle. If you expect certain persistent volumes or certain resources to exist in the data center Kubernetes, but they don’t exist in the public cloud Kubernetes, then you have to readjust your application to work with a different persistence layer. There are abstractions in Kubernetes that do help with this. For example, a Kubernetes persistent volume claim will help you attach to a specific persistent volume type that is different across multiple clouds. You have to make the effort to use that abstraction as well as build the underlying different persistent volumes. There are actual significant differences, and it’s not quite that easy moving from one Kubernetes cluster to another.

This is an example, but there are many other scenarios in which this problem exists. What are some foundational practices to keep in mind, if you’re designing, you want to use an abstraction, and you need a way to adjust between them? My big tip is to try for just enough abstraction. Kubernetes is one of those examples of a just enough abstraction. There could be other open source standards that allow you to do just enough abstraction, and take away some of the pain of needing to adjust between environments. Just enough abstractions tend to exist in open source standards, but they also may exist in your organization’s implementation of abstraction. Even if you say, for example, I build one API layer to make sure that I query information for security specs or security metadata. That is just enough abstraction to make sure that you’re not just querying a specific tool.

Here are some foundational practices that help you achieve a cloud native architecture from an adaptability perspective. First, decouple configuration and secrets. I think this is something that you might encounter as part of maybe the twelve-factor app perspective for microservices. Even if you’re not necessarily doing those kinds of software architectures, or application architectures, you need to consider decoupling configuration and secrets in a cloud native architecture. The reason why is that as you adapt across environments, you’re going to have different configurations, whether it be development, staging, or production, or differing kinds of configurations across clouds, you need to be able to decouple configuration as well as the credentials you use to access all of those target clouds. Decoupling them will allow you to scale your application, but also it will minimize the effort that you need to take to adapt it to specific clouds. Decoupling the configuration and secrets away from the application or away from infrastructure is one way that you can ensure that you have some consistency in how you are going to adjust a dependency across clouds.

The next is to use dependency injection. The important thing about dependency injection is to apply the abstraction in a way that you can change the downstream resource as well as the upstream resource. For example, if you’re an application that runs on a server, and that application might need the server IP address for some reason, you don’t want to have the application query the server command line, or query the network interface just for the IP address. The reason being, is that querying the underlying interface could differ. It won’t let you do it in a container. It will be different on a virtual machine. It might be different on something else. You would want a layer of abstraction there so that way the application can query the server IP without necessarily depending on the server’s underlying operating system. The way to think about this is to instead use a metadata service for that machine. You call the endpoint for that machine and you retrieve the IP address from there from an API endpoint. That’s an example of using dependency injection to decouple the dependencies. The reason why this is particularly important, especially from an infrastructure standpoint in cloud native, is that you’re often going to change the upstream resources, basically, the ones that depend on the underlying infrastructure. The upstream resources are going to change much faster than the lower-level resources. It’s not going to be easy to adapt a network across multiple clouds. You can’t use the same network declaration from Azure to AWS to Google Cloud, but what you can do is, generally speaking, describe the server in similar manner across Azure, Google Cloud, and AWS. The idea is to use dependency injection. You can make those changes to those upstream resources, and adapt them across clouds without necessarily putting additional effort into the lower-level resources.

The next thing that, foundationally, you need to implement in order for you to get closer to cloud native from an adaptability standpoint, is to write abstract tests. We don’t like writing tests. Testing is hard to justify the effort. When you have to work across multiple clouds, and also you have the cloud native architecture, meaning one that’s fairly dynamic, the thing that will help you is knowing that functionally everything is working as expected. What I usually do is write an abstract test to test the functionality of my application when it’s on a cloud. I call these end-to-end tests. Why are end-to-end tests an important place to abstract? It’s pretty much going to be the same across any cloud. If I know my application is going to submit a payment, it doesn’t matter which cloud it’s running on, it should just submit the payment. In that case, the test itself should have a level abstraction, meaning it should test the endpoint, and the endpoint should return the correct information. It should not matter what the underlying cloud is, it shouldn’t matter what the underlying technologies are for. Investing in writing abstract tests are very useful.

Finally, this is probably one of the more disruptive practices that I’m going to include on the list and you’re going to encounter. This is one that if you do not have this in place, it’s going to make it incredibly difficult to improve the adaptability of your system. That is to update to stable or stable minus one version. This is because most clouds who will offer some managed service, tend to offer different versions. One cloud may offer up to Kubernetes 1.23, another one might only offer stable 1.17, for some reason or another. If you don’t have stable minus one, or stable versions, what tends to happen is that it makes it difficult to adjust across clouds, and/or even across environments. You can imagine that sometimes dev might be at 1.21, but then production might be at 1.17. The reality is that when you have all of these different versions, it makes it incredibly difficult to adapt upstream dependencies across all of these different environments. Updating to stable or stable minus one is a great way to ensure that you are checking the ability for an upstream dependency to move across all of these different environments comfortably.

If all else fails, and these practices you have in place, you’re finding that it’s still really difficult to adapt an application across all these different platforms and clouds, as a last resort, refactor the abstraction. What I mean is that if it’s not working for you. For example, if you’re finding that it’s still incredibly difficult for you to port this application from one Kubernetes to another. That’s usually an indication that that’s not quite the right abstraction. Maybe the application itself does not lend well to Kubernetes, or maybe it’s just not made to run in a container. In that case, figure out what the abstraction is and identify how to best improve that application in order to suit a better abstraction, and make it a lower effort in order for you to adapt across environments. This is something that is the last resort. This is oftentimes the way we jump toward cloud native. We’ll start with these foundational practices, and then, eventually, we’ll resort to the last one, which tends to be a larger refactor effort.

Observability – Navigate Cloud Cover

Next, we have observability. Observability is the way that you can understand how you’re using your cloud as well as how your applications run on it. This is incredibly important. When we talk about being cloud hosted, we have an understanding of usage as well as performance, but we don’t have a really deep understanding of how it interacts as a larger system. As part of cloud native architecture, you need to understand how everything interacts together. For example, imagine that I have a monitoring system in my data center. That monitoring system in my data center is now responsible for not only retrieving the information from the data center, which has a more traditional monitoring server approach, it also has to retrieve information across various syslogs in different machines. It has to get AWS access logs, Google Cloud access logs, Azure access logs, Azure Active Directory access logs, Kubernetes logging and metrics. It needs to aggregate some of the logs from Spring Cloud, some of the logs from .NET applications. Then, any services that we run on top of the infrastructure, so this could be a service mesh, this could be a secrets manager. All of this information now needs to get aggregated somewhere.

The best approach is to set some standards. Notice I don’t say set one standard, set some standards. With this heterogeneous set of workloads, as well as services, platforms, it’s really difficult to have one standard. You’ll spend way too much time trying to organize everything and format it into one uniform data format with the correct fields and the correct values. While there is some value to that, the amount of effort you spend in doing that does not necessarily give you the additional benefit. What is the alternative? When you set some standards, identify standards that you can adopt from an organizational standpoint, and fit just enough of your workload footprint. For example, if you have Prometheus, Prometheus is a way for you to have open source standard for metrics and formatting. That serves its purpose for a number of metric servers. A lot of metric servers will pull from different endpoints and use Prometheus format and metrics. You can also look at OpenTelemetry to add instrumentation to the application itself. It works across a variety of programming languages and frameworks. There’s also Fluentd. Fluentd will help you extract logs from a machine, and then send it to a target in a more structured format. There are little ways that you can do this, and a number of them are in open source standards. Again, the thought is that abstraction will help you adapt.

However, there is a point to meeting resources where they’re at. Sometimes, it’s just not possible. You can’t add the OpenTelemetry library in, instrumentation is just too difficult. You already have metrics set up for an application, you really don’t want to refactor and add a new library in when you don’t have to. There is a point to meeting resources where they’re at, they’re setting some standards. If there are resources that absolutely cannot be refactored, or the level of effort is just too high, and there’s no real value from that, meet it where it is right now and just take the information. When we talk about taking that information in, there are some foundational practices to taking that information. The first is tagging and adding metadata. If you are using an existing metrics library in an application, make sure you have consistent and standardized metadata. This metadata should be fairly uniform across infrastructure, applications, and other managed service offerings. Identifying and architecting the correct metadata or the proper metadata for identification will help you identify resources and get a better end-to-end picture of what’s going on in your environment. It’s very difficult to justify tagging and adding metadata after the fact, but it is worth the effort especially from a billing and compliance standpoint.

Enable audit and access logs. This seems to be pretty intuitive. Most people don’t do this until after everything has happened. They decide, ok, we’ve built and engineered this, now we can enable audit and access logs. The reality is, audit and access logs are pretty powerful. They not only give you a security view into who’s accessing and looking at what in your environment, but it’s also a very useful way to track transactions and interactions as well. This is important in a very dynamic environment. When we talk about cloud native, it’s often in a very dynamic, ever-changing environment. A container is coming up, a container is coming down. It becomes really difficult from a development perspective to understand what is happening in that system, when a container is only available for perhaps 10, 15 minutes, and then suddenly, it’s been destroyed for some reason. Enabling audit and access logs are incredibly important.

On top of that, in a cloud native architecture, we tend to skew toward continuously delivering the resources. The reason why is, again, we want to take advantage of the dynamic environment. When we think about continuous delivery, it becomes really easy to say, I’m going to just use a CI framework, and let it have access to deploy anything it needs to. That is access. In order for you to understand the access that your CI framework has, and to properly audit its automation, you do need to have logging available for that. You’ll need to do a lot of automation from a cloud native standpoint. It’s critical to understand that any automation you do needs to have access to something. Even if you’re really great at least privilege, meaning minimizing the amount of access that an automation piece does have, you still need a way to log and audit it. It’s not just from a security perspective, if you’re a developer or an engineer who’s working on it, you need it to reconstruct, oftentimes, interactions between a system. Enable those audit and access logs.

The other thing that you’ll need to do is aggregate telemetry. This is a little bit more difficult. Sometimes, it’s not so easy to aggregate telemetry without finding a new tool or technology and installing it in the process. There are ways that you can aggregate the telemetry into a couple different targets and make sure that that information exists. Making sure you aggregate the telemetry will help add, again, a level of abstraction for you to adapt across different environments. If you can aggregate telemetry across other clouds, that allows you the ability to understand how applications are interacting across different clouds versus within a cloud. Standardizing and indexing telemetry comes after you’ve tagged and added the metadata. Standardizing and indexing telemetry does allow you to trace the transactions within an environment. It traces transactions as well as interactions from the application level to infrastructure level. Having some telemetry that you can search on and specific fields that you know will exist will help you identify later on what resources are important, and what resources are not.

Finally, if you’ve done all of these foundational practices, and you find yourself struggling to make changes to your application, it’s still probably mostly a cloud hosted application, it’s not really cloud native. In that case, maybe assess a push versus pull model. More of the traditional monitoring systems use a push model, meaning there’s an agent and it collects the information and pushes it out to the metric server. Or you bundle an agent with the application and it pushes those metrics or telemetry out to a server somewhere. In more recent years, what we consider more of the distributed approach is to pull so you have an agent that’s sitting either in the environment, or on the host level, and it pulls from multiple endpoints, and then sends it out to the server. Assessing a pull-based approach is one way that you can look at getting closer to cloud native, and this will help you scale in the future. It doesn’t mean you have to redo your entire systems. It might just mean that changing one or two configurations on the monitoring agent side to say, we only really need you to exist on one host, and you can scrape all the hosts in this region, for example. Assessing the push or pull model will help scale specifically the observability piece of this. That way you don’t have to rearchitect or rebuild the entire monitoring system just so that you have more visibility across your cloud environment. As a last resort, then rebuild your monitoring system. Sometimes this is not something you can avoid. If you have a lot of older systems in place and you have a lot of bespoke monitoring, sometimes it’s better just to rebuild the monitoring system and standardize with these practices in mind.

Immutability – Keeps Up with Dynamic Environments

Immutability is the next capability that I think about when it comes to cloud native architecture. Immutability helps you keep up with dynamic environments. To describe immutability, I’m just going to go through this example. Imagine that you want to update Java for an application. One way that you could do it is to log into the server that houses the application, and then update the Java package on there. You run into the danger of the application itself potentially breaking. Maybe other dependencies on the machine rely on Java, and now you’ve broken all the other dependencies. Now you have no server running and no application running. That actually affects your systems as well as your customers’. In recent years, we’ve moved more toward the immutability approach, where we deploy a new application binary with a new underlying system with the updated Java. Rather than log in and update Java, what we’re doing is we’re taking a new application, as well as a new instance of the application’s environment, deploying it out, and it has the updated Java. If it does not work, you can always revert to the old version. If it does work, you can simply delete the old version. This helps with the overall reliability of the system.

Immutability helps you roll out new resources for most changes. There’s a caveat to this. Sometimes there are changes that are related to configuration or secrets. Imagine you need to change the database secret, and you don’t really want to do it in the application. You don’t want to just deploy a new application with the new database secret, because maybe it’s been compromised. Now you’re just adding more fuel to the fire. What you’ll do instead is perhaps just change the application and say, the application, please reload, because there’s some configuration change or some password change. Then it will actually reload. This is not immutable, this is mutable. Some changes are mutable. You have to keep that in mind. There are some changes that are mutable because you’ve added a level of abstraction. In this case, because maybe you’ve shifted configuration and secrets into a different management store, the changes can be mutable on the application side.

From a foundational practice standpoint, it’s important to do all of the things you can to be immutable, but know that there are some things that are mutable. Let’s think about that. The first is automating infrastructure as code. Infrastructure as code tends to assume that the infrastructure is immutable. There’s very few places in which you’re updating infrastructure in place when you have infrastructure as code. If you have the automation in place to do that, you get the principle of immutability out of the box, which is nice. Decoupling state and configuration will help you separate the pieces that require immutable changes, away from the ones that are able to be handled immutably. What I mean is that if you have data that your application is writing to or data that your application needs, decouple that from the application that is running. Decoupling the state and configuration becomes an important part of cloud native, mostly because your applications will have to be able to adapt and run anywhere, but your data may not. In which case, you might find yourself needing to further decouple the data as well as the app from the application. Decoupling state and configuration is an important foundational step.

The next is reloading and restarting for changes. Not all changes are done immutably. Some of them are mutable. I just covered some of them. Reloading or restarting the application is important. It helps you make a mutable change without necessarily changing the application itself. One good example of this is that if you change a database password and you’re using Spring Boot, you could use the Actuator API endpoint, and basically tell the endpoint, reload everything, reload the database connection string. What this will do is gracefully make sure that all the other connection strings are shut down before retrieving the new database password and reloading and reconnecting to the database.

Finally, optimize provisioning. I can’t emphasize this enough. When you talk about immutability, it only works if you can create resources quickly. Sometimes, you will have to wait. For example, if you have large clusters that you’re provisioning, it makes more sense not to necessarily do that from within an immutable perspective. You sometimes want to make sure that you need to get these resources really quickly, from a functional standpoint. You also want to make sure that you’re not just affecting the system, because you’re spending a long time waiting for new resources to come up. Make sure you’re optimizing provisioning. It’s important from at least an architectural standpoint, to ensure that anything that you provision, you can do it repeatedly, but also, you can do it fairly quickly. Because if something is broken, you’ll want to take advantage of a new environment, you’ll want to use immutability to create new environments and restore the system. Optimizing provisioning becomes an important piece and practice to that.

Finally, distributing data. Distributing data is a little complicated, but at some point, you’ll realize that when you have data in a cloud native environment, you need to figure out what to do with it. That’s when people start to move toward different kinds of datastores. They move away from the databases, they do some kind of other distributed datastore of some kind, or a distributed database of some kind. What this will do is help treat data immutably without treating the content of data immutably. You still preserve the data itself, but the way that the data is being handled and distributed, is treated in a mutable fashion across your cloud. As the last resort, then you refactor for immutability. This is when you just don’t have any other options. If you don’t have infrastructure as code and you treated your infrastructure immutably before, you have existing infrastructure, and now you need to try to manage it a little bit better. In this case, you may have to undergo a significant refactor so that you have new resources that are managed by your infrastructure as code deployment.

Elasticity – Make the Most of Resources

The fourth capability that is a little bit more complicated to talk about is elasticity. Elasticity is the ability for you to make the most of your cloud resources. I think this is the hallmark of being cloud native. Is your application elastic? What I mean by that is that most of the time when we talk about moving an application to cloud, we think it’s a straightforward, ok, I run it in a data center, it’s been updated to all its latest versions. Now I’m just going to pick it up and move it and run it into a virtual machine in cloud. That works, except then you realize that it’s quite expensive to run that application on a virtual machine, because what you’ve done is you’ve spec’d out the virtual machine to be the same size as the one that’s already in your data center. That doesn’t necessarily improve your cost. Elasticity is actually about the cost of time. In the cloud, you’re getting charged per hour, or per unit, or per run, or how long that run is taking. Elasticity is about taking advantage of the time that you have for that resource. It is all about time.

What do we mean about trying to optimize the cost of time? We traditionally thought about optimizing cost as well as taking advantage of elasticity as the difference between vertical and horizontal scaling. Vertical scaling is a focus on resource efficiency, meaning, if you have an application, you’ll get x number of CPU and x amount of memory associated with it. It works, but we found out quickly that most of the time, we weren’t taking advantage of all the CPU or memory. We thought about maybe going to horizontal scaling. The idea is that we increase the workload density. We have smaller instances scheduled on a lot of different machines, and these smaller instances can do parallel processing so it can better serve requests. It’s not necessarily an either/or, but most cloud native architecture approaches, the general assumption is to do horizontal scaling with increased workload density. Cloud native doesn’t always mean horizontal scaling. It’s actually pretty complicated, because not everything can be horizontally scaled, but also, you’re not going to get the benefit of elasticity just by horizontal scaling.

What do we mean by this? There’s a couple important practices to keep in mind. The first is to evaluate idle versus active resources. It’s not necessarily about horizontal scaling, vertical scaling, smaller instances with many instances versus fewer instances with larger instances. The idea is that what is idle versus active. The reason why horizontal scaling is really appealing from a cloud native architecture standpoint is that it’s taking advantage of active resources. It’s using as many active resources as possible while minimizing idle. There are situations in which you absolutely cannot maximize all of those active resources. In the case of perhaps data processing, it might not make any sense to do horizontal scaling. It might just make sense to do one process, spin up a VM for an hour, and then shut it down. That’s it. Evaluating the idle versus active resources become important. For a more simplistic win, from an elasticity standpoint, when you’re looking at your cloud environment, understand what resources are truly being used. If things are not being used, you can shut them down. A good example of this is that if you have a development environment and you’re only using them in weekdays, maybe shut it down over the weekend. That will save you some money. Evaluating the idle versus active resources actually becomes more important than immediately refactoring your applications to handle horizontal scaling.

The next is optimizing warm-up and runtime. When you have jobs that get processed, or you have resources that get scaled up and down, it might be virtual machine instances. Actually, all of the public clouds now have autoscaling capability, so if you scale up, then you can scale down. Optimizing that warm-up becomes incredibly important, because you are again, charged per hour. If it takes you a very long time to warm up those resources, and then by the time you run them you’re already being charged by the hour, it’s not going to be helpful for you. Instead, you’re going to find that you now have some idle resources, that are available to you. Those are what I would consider idle because they’re not ready for use yet. Optimizing the warm-up and runtime becomes important. You can use immutability to help with that. For example, if you are currently using user data to configure your virtual machine, it might take optimistically 15 minutes to do it. That 15 minutes might include time to install packages. Instead of doing that for 15 minutes, maybe what you do is you use an immutable image, where you build the virtual machine image and you just provision it with all of those packages in place, and all you do is add configuration.

The next is assessing volume versus frequency. The way I best describe this is that in the data space, in the past couple years, I’ve noticed folks using lambdas to process data. That works. That works fairly well. AWS Lambda, using functions to process the data. What they’re doing is they’re saying, I need to create x number of lambdas whenever this happens, and we’re pretty much whenever something comes into queue, and it’s good. It does work. The lambdas do process our functions, and, for the most part, the price point is ok for them. Then there comes to a point when you have a lot of jobs, or a lot of data that you need to be processing. The volume of that data does not justify necessarily using lambda anymore. It becomes more expensive. Especially if the lambda functions need to use private IP addresses in the virtual network. Using a private IP address in the virtual network, it takes time to free up that interface. By that point, while you’re not necessarily getting directly charged for it, you are finding that lambda might be waiting, just so that gets allocated those IP addresses in a private network. By that point, you might as well switch to some other formalized data processing tool, whether it be EMR or something else. The idea is that you want to assess whether or not the volume and the frequency by which you are creating these resources outweigh just having those resources exist, and in being in place. This is the same reason for maybe having a pool of Kubernetes cluster workers, or something like that available. Sometimes you just need that on hand, and it does justify the cost. Assessing volume versus frequency becomes an important part of optimizing for elasticity and making sure you use the most of your cloud resources.

Then, as a last resort, you rebuild to mitigate cost. Refactoring to mitigate cost is a very costly effort, and it takes some time. It also involves oftentimes adapting to new abstractions. If you may be working with a system that was originally not meant to run on cloud, and then all of a sudden now you’re trying to mitigate the cost of it, because you did this lift and shift, you will have to replatform. You will have to ultimately rebuild and rearchitect in order to fully mitigate the cost of the lift and shift. You’ll have to find a technology that does work for your architecture and the functions that you want. That is a last resort.

Changeability – Use the Latest Technologies

Finally, changeability. We all want to use the latest technologies, but it’s not that easy to change an application to use the latest technology, so, how do we do it? Let’s imagine this example. I’ve been hearing a lot about this more recently. You have a CI framework, and you’ve started looking into the cloud native technology of GitOps. I think it’s a really fascinating space. It’s also really helpful for certain use cases. You decide, this is really useful for me, I want to continuously deploy. I want to take advantage of blue-green deployments, in that I want to make sure that when I do a blue-green deployment, it’s completely automated. My canary will increase automatically, and I don’t have to worry about it. That’s great. The only problem with this is that the latest often involves paradigm shifts. Telling your security team, or telling your management team, or telling anybody that they’re going to remove a manual quality gate, or a manual gate to production, and automatically do this wishy-washy, blue-green thing, is really hard to sell. I think it’s really cool, but what I’ve noticed is that it’s not so easy to describe. Most people are wondering, what happens if it fails? The answer is, most of these technologies, especially in the GitOps space, will roll back for you. On the other hand, it’s not so easy to just trust that assumption. The latest often involves paradigm shifts.

Instead, what you might think about doing is changing, not the tool, but changing the paradigm first. Rather than say, I have a CI framework, and I’m doing continuous delivery now. What you might consider instead is, ok, in order to take advantage of all of these cloud native benefits, what I’m going to do is take an intermediate step. I’m going to do some modified continuous deployment on my CI framework. That could be perhaps Spinnaker. You might say, I’m just going to use Spinnaker first, and what I’ll do is I’ll do a manual deployment and manual check. Once I’m comfortable with the canary and the blue-green approach, then maybe I’ll shift to a more continuous deployment approach, or maybe I’ll even shift to the GitOps approach. There are a lot of intermediate options. These intermediate steps exist for change. It’s not to say that you should immediately adopt a tool and that will immediately help you. The reality is that most of the time when you’re looking to go from cloud hosted to cloud native, you’ll need to take the intermediate step, especially if you plan on such a drastic change, that changes the underlying assumption of how your application behaves and how it’s being deployed.

Of course, the first thing you’re going to do is assess the benefit, but in a more nuanced perspective, you do want to assess the benefit of changing the paradigm, of changing the assumption itself. The other thing you’ll want to do is review all the previous patterns I talked about. All the foundational practices for immutability, adaptability, all of those. Review that you have those foundations in place. If you don’t, it will be incredibly difficult to change the tool after that. Then, choose an intermediate step. In the case of the CI framework, maybe we modify our CI framework to do a manual blue-green. Once we’re comfortable, then we can move toward an automated blue-green deployment, or an automated canary deployment.

Finally, refactor your application or infrastructure in order to accommodate that intermediate step. In the case of the CI framework that I was talking about, and also moving towards some continuous deployment approach, you will need to refactor the application to expose metrics. If your application does not have metrics, then it will not work in the continuous deployment approach. The reason why is that an automated deployment needs somewhere to retrieve the application metrics. You need either an error rate or a composite metric for you to understand how the application is behaving, and whether or not is failing or succeeding. Without that metric, you cannot do an automated deployment. For the most part, it’s also hard for you to determine whether or not you should increase traffic to that new application. You do have to refactor the application or infrastructure to accommodate for that metric. In which case, it will stop you from adopting the next latest and greatest approach.

After that, if you find that you’re having a really difficult time with that intermediate step. You’re finding that the intermediate step is not sufficient for you to graduate to the latest and greatest technology, build a greenfield environment. It’s not easy. It is going to be a very high level of effort. You do have to assess if it’s a high value as well. Most of the time, if you’ve done these basic steps, you’ve done an intermediate, and it’s still not quite working, and you’re still finding that you need to go to this new latest technology, because it has some significant business benefit to you, then you build a greenfield environment.

Summary

Returning back to the definition of cloud native architecture. I’ve gone through a lot of these foundational practices that move you from cloud hosted to cloud native. The focus of these practices is not to necessarily say that you will be cloud native. The focus is that you’re going to have a more scalable application and you’re going to take advantage of the dynamic environment in place. As these technologies change, year over year, month over month, even, you have the ability to change with them. You can adapt with them. In summary, here are the considerations. Again, remember that if you want to get to a cloud native architecture, consider the adaptability, observability, immutability, elasticity, and changeability. All of these will help contribute to a more cloud native approach and your application to better adapt to changes in your architecture.

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• MongoDB

The Global SQL 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 SQL Market is divided based on application, end user, and region, with a specific focus on manufacturers situated in various geographic areas. The study offers a comprehensive analysis of diverse factors that contribute to the industry’s growth. It also outlines potential future impacts on the industry through various segments and applications. The report includes a detailed pricing analysis for different types, manufacturers, regional considerations, and pricing trends.

The SQL Share report delivers an overview of the market’s value structure, cost determinants, and key driving factors. It assesses the industry landscape and subsequently examines the global landscape encompassing industry size, demand, applications, revenue, products, regions, and segments. Moreover, SQL Market report presents the competitive scenario in the market among distributors and manufacturers, encompassing market value assessment and a breakdown of the cost chain structure.

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Global SQL Market Report: Key Insights

• REGIONAL SHARE: The SQL Market report provides market size data for various regions, including North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa. In 2022, North America dominated the SQL market, followed by Europe, while Asia Pacific held a significant share.
• SEGMENT OVERVIEW: The market is segmented based on Type and Application. In 2022, Down accounted for a notable percentage of the SQL market, with Feather also contributing significantly. Comforters held a significant share as well, and Apparel played a role in the market.
• COMPETITIVE LANDSCAPE: The report offers a comprehensive analysis of prominent players who have substantial market shares. It includes information on the concentration ratio and provides detailed insights into the market performance of each player. This allows readers to gain a holistic understanding of the competitive landscape and better knowledge of their competitors.
• KEY FACTORS CONSIDERED: With the global impact of COVID-19, the report tracks market changes during and after the pandemic. It examines the effects on upstream and downstream market participants, changes in consumer behavior, demand fluctuations, transportation challenges, trade flow adjustments, and other relevant factors.
• REGIONAL CONFLICTS: The report also addresses the influence of regional conflicts, such as the Russia-Ukraine war, on the market. It discusses how these conflicts have negatively affected the market and provides insights into the expected evolution of the market in the coming years.
• CHALLENGES AND OPPORTUNITIES: The report highlights factors that could create opportunities and enhance profitability for market players. It also identifies challenges that may hinder or pose a threat to player development. These insights can assist in making strategic decisions and their effective implementation.

SQL Market Report Overview:

The global SQL market size was valued at USD Million in 2022 and will reach USD Million in 2028, with a CAGR during 2022-2028.

SQL is a specific purpose programming language for managing relational database management systems or for stream processing in relational flow data management systems. SQL is based on relational algebra and tuple relationship calculus, including a data definition language and data manipulation language.

The SQL market report covers sufficient and comprehensive data on market introduction, segmentations, status and trends, opportunities and challenges, industry chain, competitive analysis, company profiles, and trade statistics, etc. It provides in-depth and all-scale analysis of each segment of types, applications, players, 5 major regions and sub-division of major countries, and sometimes end user, channel, technology, as well as other information individually tailored before order confirmation.

Meticulous research and analysis were conducted during the preparation process of the report. The qualitative and quantitative data were gained and verified through primary and secondary sources, which include but not limited to Magazines, Press Releases, Paid Databases, Maia Data Center, National Customs, Annual Reports, Public Databases, Expert interviews, etc. Besides, 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-clients.

In this report, the historical period starts from 2018 to 2022, and the forecast period ranges from 2023 to 2028. The facts and data are demonstrated by tables, graphs, pie charts, and other pictorial representations, which enhances the effective visual representation and decision-making capabilities for business strategy.

The report provides a forecast of the SQL Market across regions, types, and applications, projecting sales and revenue from 2021 to 2030. It emphasizes SQL Market Share, distribution channels, key suppliers, evolving price trends, and the raw material supply chain. The SQL Market Size report furnishes essential insights into the current industry valuation and presents market segmentation, highlighting growth prospects within this sector.

This report centers on SQL Market manufacturers, analyzing their sales, value, market share, and future development plans. It defines, describes, and predicts SQL Market Growth based on type, application, and region. The goal is to examine global and key regional market potential, advantages, opportunities, challenges, as well as restraints and risks. The report identifies significant trends and factors that drive or hinder SQL Market growth, benefiting stakeholders by pinpointing high-growth segments. Furthermore, the report strategically assesses each submarket’s individual growth trend and its contribution to the overall SQL Market.

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What are the types of SQL available in the Market?

• Text
• Number
• Date

What are the factors driving applications of the SQL Market?

• Retail
• Online Game Development
• IT
• Social Network Development
• Web Applications Management
• Others

The Global SQL Market Trends, development and marketing channels are analysed. Finally, the feasibility of new investment projects is assessed and overall research conclusions offered.The global SQL Market Growth is anticipated to rise at a considerable rate during the forecast period, between 2021 and 2028. In 2021, the market was 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.

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SQL Market Trend for Development and marketing channels are analysed. Finally, the feasibility of new investment projects is assessed and overall research conclusions offered. SQL Market Report also mentions market share accrued by each product in the SQL market, along with the production growth.

Which regions are leading the SQL Market?

North America (Covered in Chapter 6 and 13)

Europe (Covered in Chapter 7 and 13)

Asia-Pacific (Covered in Chapter 8 and 13)

Middle East and Africa (Covered in Chapter 9 and 13)

South America (Covered in Chapter 10 and 13)

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Reasons to Purchase SQL Market Report?

• SQL Market Report provides qualitative and quantitative analysis of the market based on segmentation involving both economic as well as non-economic factors.
• SQL Market report gives outline of market value (USD) data for each segment and sub-segment.
• This report indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market.
• SQL Market Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region.
• Competitive landscape which incorporates the market ranking of the major players, along with new service/product launches, partnerships, business expansions and acquisitions in the past five years of companies profiled.
• Extensive company profiles comprising of company overview, company insights, product benchmarking and SWOT analysis for the major market players.
• The current as well as the future market outlook of the industry with respect to recent developments (which involve growth opportunities and drivers as well as challenges and restraints of both emerging as well as developed regions.
• SQL Market Includes an in-depth analysis of the market of various perspectives through Porter’s five forces analysis also Provides insight into the market through Value Chain.

Detailed TOC of Global SQL Market Research Report, 2023-2030

1 SQL Market Overview
1.1 Market Definition and Product Scope
1.2 Global SQL Market Size and Growth Rate 2018-2028
1.2.1 Global SQL Market Growth or Decline Analysis
1.3 Market Key Segments Introduction
1.3.1 Types of SQL
1.3.2 Applications of SQL
1.4 Market Dynamics
1.4.1 Drivers and Opportunities
1.4.2 Limits and Challenges
1.4.3 Impacts of Global Inflation on SQL Industry

2 Industry Chain Analysis
2.1 SQL Raw Materials Analysis
2.2 SQL Cost Structure Analysis
2.3 Global SQL Average Price Estimate and Forecast (2018-2028)
2.4 Factors Affecting the Price of SQL
2.5 Market Channel Analysis
2.6 Major Downstream Customers Analysis

3 Industry Competitive Analysis
3.1 Market Concentration Ratio and Market Maturity Analysis
3.2 New Entrants Feasibility Analysis
3.3 Substitutes Status and Threats Analysis

4 Company Profiles
4.1 MariaDB
4.1.1 MariaDB Basic Information
4.1.2 Product or Service Characteristics and Specifications
4.1.3 MariaDB SQL Sales, Price, Value, Gross Margin 2018-2023
4.2 Sybase
4.2.1 Sybase Basic Information
4.2.2 Product or Service Characteristics and Specifications
4.2.3 Sybase SQL Sales, Price, Value, Gross Margin 2018-2023
4.3 MySQL
4.3.1 MySQL Basic Information
4.3.2 Product or Service Characteristics and Specifications
4.3.3 MySQL SQL Sales, Price, Value, Gross Margin 2018-2023
4.4 Microsoft
4.4.1 Microsoft Basic Information
4.4.2 Product or Service Characteristics and Specifications
4.4.3 Microsoft SQL Sales, Price, Value, Gross Margin 2018-2023
4.5 PostgreSQL
4.5.1 PostgreSQL Basic Information
4.5.2 Product or Service Characteristics and Specifications
4.5.3 PostgreSQL SQL Sales, Price, Value, Gross Margin 2018-2023
4.6 Oracle Database
4.6.1 Oracle Database Basic Information
4.6.2 Product or Service Characteristics and Specifications
4.6.3 Oracle Database SQL Sales, Price, Value, Gross Margin 2018-2023
4.7 Basho Technologies
4.7.1 Basho Technologies Basic Information
4.7.2 Product or Service Characteristics and Specifications
4.7.3 Basho Technologies SQL Sales, Price, Value, Gross Margin 2018-2023
4.8 MarkLogic Corporation
4.8.1 MarkLogic Corporation Basic Information
4.8.2 Product or Service Characteristics and Specifications
4.8.3 MarkLogic Corporation SQL Sales, Price, Value, Gross Margin 2018-2023
4.9 MongoDB
4.9.1 MongoDB Basic Information
4.9.2 Product or Service Characteristics and Specifications
4.9.3 MongoDB SQL Sales, Price, Value, Gross Margin 2018-2023

5 SQL Market – By Trade Statistics
5.1 Global SQL Export and Import
5.2 United States SQL Export and Import Volume (2018-2023)
5.3 United Kingdom SQL Export and Import Volume (2018-2023)
5.4 China SQL Export and Import Volume (2018-2023)
5.5 Japan SQL Export and Import Volume (2018-2023)
5.6 India SQL Export and Import Volume (2018-2023)

6 North America SQL Market Overview Analysis
6.1 North America SQL Market Development Status (2018-2023)
6.2 United States SQL Market Development Status (2018-2023)
6.3 Canada SQL Market Development Status (2018-2023)
6.4 Mexico SQL Market Development Status (2018-2023)

7 Europe SQL Market Overview Analysis
7.1 Europe SQL Market Development Status (2018-2023)
7.2 Germany SQL Market Development Status (2018-2023)
7.3 United Kingdom SQL Market Development Status (2018-2023)
7.4 France SQL Market Development Status (2018-2023)
7.5 Italy SQL Market Development Status (2018-2023)
7.6 Spain SQL Market Development Status (2018-2023)

8 Asia Pacific SQL Market Overview Analysis
8.1 Asia Pacific SQL Market Development Status (2018-2023)
8.2 China SQL Market Development Status (2018-2023)
8.3 Japan SQL Market Development Status (2018-2023)
8.4 South Korea SQL Market Development Status (2018-2023)
8.5 Southeast Asia SQL Market Development Status (2018-2023)
8.6 India SQL Market Development Status (2018-2023)

9 Middle East and Africa SQL Market Overview Analysis
9.1 Middle East and Africa SQL Market Development Status (2018-2023)
9.2 Saudi Arabia SQL Market Development Status (2018-2023)
9.3 UAE SQL Market Development Status (2018-2023)
9.4 South Africa SQL Market Development Status (2018-2023)

10 South America SQL Market Overview Analysis
10.1 South America SQL Market Development Status (2018-2023)
10.2 Brazil SQL Market Development Status (2018-2023)
10.3 Argentina SQL Market Development Status (2018-2023)

11 SQL Market – By Regions
11.1 Global SQL Sales by Regions (2018-2023)
11.2 Global SQL Value by Regions (2018-2023)
11.3 SQL Value and Growth Rate (2018-2023) by Regions
11.3.1 North America SQL Value and Growth Rate (2018-2023)
11.3.2 Europe SQL Value and Growth Rate (2018-2023)
11.3.3 Asia Pacific SQL Value and Growth Rate (2018-2023)
11.3.4 Middle East and Africa SQL Value and Growth Rate (2018-2023)
11.3.5 South America SQL Value and Growth Rate (2018-2023)

12 SQL Market – By Types
12.1 Global SQL Sales by Types
12.1.1 Global SQL Sales by Types (2018-2023)
12.1.2 Global SQL Sales Market Share by Types (2018-2023)
12.2 Global SQL Value by Types
12.2.1 Global SQL Value by Types (2018-2023)
12.2.2 Global SQL Value Market Share by Types (2018-2023)
12.3 Global SQL Price Trends by Types (2018-2023)
12.4 Text Sales and Price (2018-2023)
12.5 Number Sales and Price (2018-2023)
12.6 Date Sales and Price (2018-2023)

13 SQL Market – By Applications
13.1 Global SQL Sales by Applications
13.1.1 Global SQL Sales by Applications (2018-2023)
13.1.2 Global SQL Sales Market Share by Applications (2018-2023)
13.2 Global SQL Value by Applications
13.2.1 Global SQL Value by Applications (2018-2023)
13.2.2 Global SQL Value Market Share by Applications (2018-2023)
13.3 Retail Sales, Revenue and Growth Rate (2018-2023)
13.4 Online Game Development Sales, Revenue and Growth Rate (2018-2023)
13.5 IT Sales, Revenue and Growth Rate (2018-2023)
13.6 Social Network Development Sales, Revenue and Growth Rate (2018-2023)
13.7 Web Applications Management Sales, Revenue and Growth Rate (2018-2023)
13.8 Others Sales, Revenue and Growth Rate (2018-2023)

14 SQL Market Forecast – By Types and Applications
14.1 Global SQL Market Forecast by Types
14.1.1 Global SQL Sales by Types (2023-2028)
14.1.2 Global SQL Value by Types (2023-2028)
14.1.3 Global SQL Value and Growth Rate by Type (2023-2028)
14.1.4 Global SQL Price Trends by Types (2023-2028)
14.2 Global SQL Market Forecast by Applications
14.2.1 Global SQL Sales by Applications (2023-2028)
14.2.2 Global SQL Value by Applications (2023-2028)
14.2.3 Global SQL Value and Growth Rate by Application (2023-2028)

15 SQL Market Forecast – By Regions and Major Countries
15.1 Global SQL Sales by Regions (2023-2028)
15.2 Global SQL Value by Regions (2023-2028)
15.3 North America SQL Value by Countries (2023-2028)
15.4 Europe SQL Value by Countries (2023-2028)
15.5 Asia Pacific SQL Value by Countries (2023-2028)
15.6 Middle East and Africa SQL Value by Countries (2023-2028)
15.7 South America SQL Value by Countries (2023-2028)

16 Research Methodology and Data Source
16.1 Research Methodology
16.2 Research Data Source
16.2.1 Secondary Data
16.2.2 Primary Data
16.2.3 Legal Disclaimer

Continued

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