Month: November 2024

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Today, AWS announced support for a new Apache Flink connector for Amazon DynamoDB. The new connector, contributed by AWS for the Apache Flink open source project, adds Amazon DynamoDB Streams as a new source for Apache Flink. You can now process DynamoDB streams events with Apache Flink, a popular framework and engine for processing and analyzing streaming data.

Amazon DynamoDB is a serverless, NoSQL database service that enables you to develop modern applications at any scale. DynamoDB Streams provides a time-ordered sequence of item-level changes (insert, update, and delete) in a DynamoDB table. With Amazon Managed Service for Apache Flink, you can transform and analyze DynamoDB streams data in real time using Apache Flink and integrate applications with other AWS services such as Amazon S3, Amazon OpenSearch, Amazon Managed Streaming for Apache Kafka, and more. Apache Flink connectors are software components that move data into and out of an Amazon Managed Service for Apache Flink application. You can use the new connector to read data from a DynamoDB stream starting with Apache Flink version 1.19. With Amazon Managed Service for Apache Flink there are no servers and clusters to manage, and there is no compute and storage infrastructure to set up.

The Apache Flink repo for AWS connectors can be found here. For detailed documentation and setup instructions, visit our Documentation Page.

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𝐔𝐒𝐀, 𝐍𝐞𝐰 𝐉𝐞𝐫𝐬𝐞𝐲- According to the Market Research Intellect, the global NoSQL Databases Software market is projected to grow at a robust compound annual growth rate (CAGR) of 15.49% from 2024 to 2031. Starting with a valuation of 7.12 Billion in 2024, the market is expected to reach approximately 16.89 Billion by 2031, driven by factors such as NoSQL Databases Software and NoSQL Databases Software. This significant growth underscores the expanding demand for NoSQL Databases Software across various sectors.

The market for NoSQL database software is expanding significantly due to the growing need for adaptable and scalable data management solutions. Organizations are turning to NoSQL databases in order to efficiently handle unstructured and semi-structured data as a result of the growth of big data, the Internet of Things, and real-time applications. The market is expanding because of industries where handling high-velocity data is crucial, such as social media, e-commerce, healthcare, and finance. Document, key-value, and graph databases are just a few of the models that NoSQL databases provide, allowing companies to choose the one that best suits their requirements. Furthermore, NoSQL’s growth is aided by the growing use of cloud computing and microservices design, which effortlessly integrate with these frameworks to provide enhanced performance, agility, and cost-effectiveness. As a result, there is a good chance that the market will continue to expand during the upcoming years.

The market for NoSQL database software is influenced by a number of factors, such as expanding competition, the desire for more flexible data, and technological breakthroughs. NoSQL solutions, which manage high volumes, diversity, and complexity more effectively, are becoming more popular as a result of the difficulties traditional relational databases encounter when firms handle enormous amounts of diverse data. Because NoSQL databases expand horizontally and are ideal for cloud environments, cloud computing is also becoming more and more popular. However, hybrid databases-which include NoSQL and SQL features-are a competitor in the market, attracting businesses looking for flexibility without totally giving up on relational models. Market dynamics are also being impacted by regulatory constraints pertaining to data storage and privacy, which are forcing NoSQL providers to improve security and compliance features. This could hasten the adoption of NoSQL in highly regulated industries.

𝐑𝐞𝐪𝐮𝐞𝐬𝐭 𝐏𝐃𝐅 𝐒𝐚𝐦𝐩𝐥𝐞 𝐂𝐨𝐩𝐲 𝐨𝐟 𝐑𝐞𝐩𝐨𝐫𝐭: (𝐈𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐅𝐮𝐥𝐥 𝐓𝐎𝐂, 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐓𝐚𝐛𝐥𝐞𝐬 & 𝐅𝐢𝐠𝐮𝐫𝐞𝐬, 𝐂𝐡𝐚𝐫𝐭) @ https://www.marketresearchintellect.com/download-sample/?rid=2527410&utm_source=OpenPr&utm_medium=027

𝐊𝐞𝐲 𝐃𝐫𝐢𝐯𝐞𝐫𝐬:

The growth of the NoSQL Databases Software market is driven by several key factors. Technological advancements in NoSQL Databases Software have enabled greater efficiency and enhanced capabilities, spurring adoption across industries. Additionally, the rising demand for sustainable and eco-friendly solutions is pushing companies to innovate and adopt greener practices. Expanding applications in sectors like NoSQL Databases Software and NoSQL Databases Software are further contributing to market demand, as these industries seek advanced solutions to streamline operations and enhance product quality. Favorable government policies and incentives in regions such as North America, Europe, and Asia-Pacific support investment and growth. Moreover, an increasing focus on NoSQL Databases Software for improving operational efficiency and cost-effectiveness is encouraging businesses to embrace new technologies, fostering sustained market expansion.

𝐌𝐞𝐫𝐠𝐞𝐫𝐬 𝐚𝐧𝐝 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧𝐬

Mergers and acquisitions (M&A) play a pivotal role in the NoSQL Databases Software market, as companies look to expand their capabilities, access new technologies, and strengthen market presence. Leading players engage in strategic acquisitions to consolidate their position and gain a competitive edge. These transactions often facilitate the integration of advanced NoSQL Databases Software solutions, helping firms broaden their product portfolios and meet growing customer demands. Additionally, M&A activities support companies in achieving economies of scale and penetrating new regional markets, particularly in high-growth areas like Asia-Pacific. Through such strategic alliances, businesses aim to accelerate innovation, enhance operational efficiency, and address evolving market challenges, ultimately driving the overall growth of the NoSQL Databases Software market.

𝐆𝐞𝐭 𝐚 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭 𝐎𝐧 𝐓𝐡𝐞 𝐏𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐎𝐟 𝐓𝐡𝐢𝐬 𝐑𝐞𝐩𝐨𝐫𝐭 @ https://www.marketresearchintellect.com/ask-for-discount/?rid=2527410&utm_source=OpenPr&utm_medium=027

𝐓𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐊𝐞𝐲 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐬 𝐀𝐫𝐞 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧 𝐎𝐮𝐫 𝐑𝐞𝐩𝐨𝐫𝐭

𝐁𝐲 𝐓𝐲𝐩𝐞
Cloud Based
Web Based

𝐁𝐲 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Large Enterprises
SMEs

𝐌𝐚𝐣𝐨𝐫 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 in NoSQL Databases Software Market are:
MongoDB, Amazon, ArangoDB, Azure Cosmos DB, Couchbase, MarkLogic, RethinkDB, CouchDB, SQL-RD, OrientDB, RavenDB, Redis

Global NoSQL Databases Software Market -𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

𝐍𝐨𝐫𝐭𝐡 𝐀𝐦𝐞𝐫𝐢𝐜𝐚:
North America is expected to hold a significant share of the NoSQL Databases Software market due to advanced technological infrastructure and the presence of major market players. High demand across sectors like NoSQL Databases Software and NoSQL Databases Software is driving growth, with the U.S. being a key contributor. Additionally, ongoing investments in R&D and innovation reinforce the region’s strong market position.

𝐄𝐮𝐫𝐨𝐩𝐞:
Europe is projected to experience steady growth, driven by stringent regulatory standards and a rising focus on sustainability in NoSQL Databases Software practices. Countries like Germany, France, and the UK are leading due to their advanced industrial base and supportive government policies. The demand for eco-friendly and efficient NoSQL Databases Software solutions is expected to continue fostering market expansion.

𝐀𝐬𝐢𝐚-𝐏𝐚𝐜𝐢𝐟𝐢𝐜:
Asia-Pacific is anticipated to be the fastest-growing region, fueled by rapid industrialization and urbanization. Countries such as China, India, and Japan are driving demand due to expanding consumer bases and increasing investments in infrastructure. The region’s robust manufacturing sector and favorable economic policies further enhance growth opportunities in the NoSQL Databases Software market.

𝐋𝐚𝐭𝐢𝐧 𝐀𝐦𝐞𝐫𝐢𝐜𝐚:
Latin America and the Middle East & Africa are expected to show moderate growth in the NoSQL Databases Software market. In Latin America, growth is supported by rising industrial activities in countries like Brazil and Mexico. Meanwhile, in the Middle East & Africa, infrastructure development and an increasing focus on innovation in sectors like NoSQL Databases Software are key drivers of market expansion.

𝐌𝐢𝐝𝐝𝐥𝐞 𝐄𝐚𝐬𝐭 𝐚𝐧𝐝 𝐀𝐟𝐫𝐢𝐜𝐚:
The Middle East and Africa represent emerging markets in the global NoSQL Databases Software market, with countries like UAE, Saudi Arabia, South Africa, and Nigeria showing promising growth potential. Economic diversification efforts, urbanization, and a young population are driving demand for NoSQL Databases Software products and services in the region.

𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐭𝐥𝐲 𝐀𝐬𝐤𝐞𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐅𝐀𝐐)
1. What is the current size of the NoSQL Databases Software market?

Answer: The NoSQL Databases Software market was valued at approximately 7.12 Billion in 2024, with projections suggesting it will reach 16.89 Billion by 2031, growing at a CAGR of 15.49%.

2. What factors are driving the growth of the NoSQL Databases Software market?

Answer: The market’s expansion is attributed to several factors, including increased demand for NoSQL Databases Software, advancements in NoSQL Databases Software technology, and the adoption of NoSQL Databases Software across various sectors.

3. Which regions are expected to dominate the NoSQL Databases Software market?

Answer: Regions such as North America, Europe, and Asia-Pacific are anticipated to lead due to the presence of major industry players and growing investments in NoSQL Databases Software.

4. Who are the key players in the NoSQL Databases Software market?

Answer: Prominent companies in the NoSQL Databases Software market include NoSQL Databases Software, NoSQL Databases Software, and NoSQL Databases Software, each contributing to market growth through innovations and strategic partnerships.

5. What challenges does the NoSQL Databases Software market face?

Answer: The market faces challenges such as NoSQL Databases Software, regulatory compliance, and competition from alternative solutions. However, ongoing advancements aim to address these issues.

6. What are the future trends in the NoSQL Databases Software market?

Emerging trends include the integration of NoSQL Databases Software technology, sustainability practices, and digital transformation in processes, all expected to shape the market’s future.

7. How can businesses benefit from the NoSQL Databases Software market?

Answer: Businesses can leverage growth opportunities in the NoSQL Databases Software market by adopting new solutions, enhancing operational efficiency, and expanding their offerings to meet evolving consumer demands.

8. Why invest in a NoSQL Databases Software market report from MRI?

Answer: MRI’s report provides in-depth analysis, future projections, and key insights to support strategic decision-making, enabling businesses to stay competitive and capitalize on growth trends in the NoSQL Databases Software market.

𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐨𝐫 𝐐𝐮𝐞𝐫𝐲, 𝐕𝐢𝐬𝐢𝐭 @ https://www.marketresearchintellect.com/product/global-nosql-databases-software-market-size-and-forecast/?utm_source=OpenPr&utm_medium=027

𝐌𝐨𝐫𝐞 𝐀𝐫𝐭𝐢𝐜𝐥𝐞𝐬 𝐅𝐫𝐨𝐦 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐈𝐧𝐭𝐞𝐥𝐥𝐞𝐜𝐭

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𝐀𝐛𝐨𝐮𝐭 𝐔𝐬: 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐈𝐧𝐭𝐞𝐥𝐥𝐞𝐜𝐭

Market Research Intellect is a leading Global Research and Consulting firm servicing over 5000+ global clients. We provide advanced analytical research solutions while offering information-enriched research studies. We also offer insights into strategic and growth analyses and data necessary to achieve corporate goals and critical revenue decisions.

Our 250 Analysts and SMEs offer a high level of expertise in data collection and governance using industrial techniques to collect and analyze data on more than 25,000 high-impact and niche markets. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise, and years of collective experience to produce informative and accurate research.

Our research spans a multitude of industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverages, etc. Having serviced many Fortune 2000 organizations, we bring a rich and reliable experience that covers all kinds of research needs.

𝐅𝐨𝐫 𝐢𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬, 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐔𝐬 𝐚𝐭:

Mr. Edwyne Fernandes

Market Research Intellect

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This release was published on openPR.

Article originally posted on InfoQ. Visit InfoQ

Transcript

Gray: Has anyone ever been to a powerboat race in their life before? I went to a powerboat race when I was a little kid, and I vividly remember this by being in awe of the speed and the agility of these boats. The way they could turn the corners. The way they could accelerate. This is actually a world winning powerboat race, the boat. This won the 2022 World Championships. It’s got top speeds of up to 164 miles an hour. It’s a pretty impressive piece of kit. This, however, is a cargo ship. This is the world’s largest cargo ship. It’s called the MSC LORETO. It’s 400 meters long. It can carry 224,000 container crates, but it can only go at 19.5 miles an hour. You might be thinking, why am I telling you about different types of boats? It’s because at ClearBank, as most startups, you start up as a powerboat.

You’ve got the ability to accelerate quickly, go quickly, and make change happen. If we went on a mission from the UK to the U.S., which is approximately 3300 miles, the powerboat would get you there in 20 hours. The cargo ship would get you there a lot slower. It’d get you there in about a week. In some cases, that’s good. There’s a metaphor here, the container ship is the big banks, the powerboat is ClearBank. What we wanted to make sure is we didn’t become a container ship with rigorous processes. If you think about how you get a container ship loaded up, you’ve got to fuel a massive thing. You’ve got to have a really rigorous process for getting containers on and off the ship. Whereas a powerboat, you fuel up with a small team and just go. How ClearBank wanted to scale is not to become a massive container ship, but to have a fleet of powerboats that can change direction really quickly, deliver value quickly when needed. I’m Mike.

About ClearBank

Let’s start off with a little bit about ClearBank. This is ClearBank’s mission statement. At ClearBank, our purpose is to provide great technology that unlocks our partners’ potential, ensuring everyone has the freedom to choose the financial services they need. What does that mean? We’re playing quite a critical role in the revolutionization of the financial markets, previously were dominated by the Big Four, those were the only places you could get products from. Since then, we’ve had regulation change. We’ve had open banking, to name one, which is all about expanding the competitiveness of the market so that people like you can all get market lead and more products to choose from. These are some of our customers. I don’t know if any of you have got a Chip savings account or a contraction potentially banking with Tide? All of those companies sit on top of us. How we do this is we provide the financial fabric, which is like payments and accounts layer.

The other companies sit on top of us, they use our banking license. TrueLayer provide the open banking payments. All of you will have used TrueLayer, no doubt, when you’ve been checking out customers. Cazoo use TrueLayer. While it’s an advantage for Cazoo, when people are buying cards, you don’t have to pay the huge card fees. I also met somebody who was a referee, who built an application essentially to make sure referees were getting paid on time, who ended up using TrueLayer. We actually moved the money for TrueLayer. We’re everywhere, but you probably never see our name. ClearBank started in 2014, took a couple years for the bank to get a banking license with funding. Since then, we’ve grown from strength to strength. As you can see across there, we’ve got multiple awards. The interesting parts here are our headcount. Our headcount was 100, 2019, 250, now we’re at 720. We’re at 720 people now with 30 products and technology teams processing over a million payments daily. We have over 220 of those customers that I described in the previous slide. We’re growing, and continue to grow, which is really great in the current climate.

ClearBank’s Journey

Let’s talk about how we got to where we are. ClearBank, in these early days, was successful really because of its culture. It had shared mission and purpose. Had open collaboration and communication, continuous learning and growth, trust, empowerment, and autonomy. Fast decision making. All of these are conditions for high-performing teams, and that’s what gave ClearBank the competitive advantage. Everything that we’ve done since then is about maintaining these principles to make sure that we still have the competitive advantage in the market. That’s as we add enough powerboats. Powerboats, as we go through this, represent development teams, essentially. ClearBank started a little bit differently to most startups, because we had to get a banking license, which means we have to show good controls, risk management, all of these things. One of the big things regulators do care about is segregation of duties.

Interestingly, ClearBank was a lot more structured than most startups. We had development teams. We had testing teams. We had security teams and operations teams. It was your classic big bank setup. Why? To please the regulator. From day one, it was about, we need that banking license. If we can’t get the banking license, we can’t do business. That introduces challenges. We just talked about all of these different powerboats being able to make autonomous decisions, move independently. It created a lot of bottlenecks in our system. Because of the segregation of duties, we had the development teams writing code, passing them off to QA. QA then maybe there’s a bug, the development team has to fix it again. We had a change of advisory board, which, really, if anyone’s worked in a bank, is approval board.

A bunch of people approving something that they don’t necessarily have the context to approve for, and making decisions about whether this software can make its way through to production. This also creates bottlenecks in the system. As we add more development teams, again, security, operations, QA, they become bigger bottlenecks. It doesn’t really scale, unless you add loads more people. ClearBank, we made the decision to go from gatekeeping to enabling. That was quite a transition. Our goal is this, deliver constant flow of value as quickly as possible to our customers, so we need to remove the constraints.

Testing as a Gatekeeper

This is how we were. I just briefly described it. Development team writes code. Test engineer writes code, finds bugs. The development team fixes it. The testing team tests the performance of the code. Engineering teams then have to fix the performance of the code. Not very efficient handoffs. People aren’t too happy working that way. We’re a new organization, we probably shouldn’t be working that way, so we made a transition to move into testing to be an enablement function. The testing team’s role fundamentally changed. They were there to help the teams get good at testing. Because we’re regulated, we had to put some controls in place around this to prove that we still had segregation of concerns. For us, that’s our PR and release process. All our development teams have to have at least one QA champion. That means that when they’re approving the PRs into the main branch, we’ve got that end-to-end audit trail.

Development teams are now responsible for the quality of their software and performance of their software. Testing champions, the testing team also run a community of practice, which is all about continuously sharing new ways of working, that kind of thing, so that everybody can be upskilled. What we found that that did for us is it increased flow efficiency. We’ve removed the handoff, which was great. The quality actually increased. The development teams know which bugs are the bugs that need to be fixed. We can make sensible tradeoff decisions about that. They felt a sense of ownership. We kept the segregation of duty, importantly for the regulator, and actually the control was operating more effectively from our point of view.

Security as a Gatekeeper

You might see a pattern with this. Security, same thing. Software is created. Some cases it was a design that security would then review. Have to go to the change approval board or advisory board. Penetration testing would be after the fact. Security would say no a lot, to a lot of things. Because, when you don’t have the context, saying no is the easiest thing to do. It’s the safest thing to do, especially from a security perspective. That’s really quite frustrating for the engineering teams. Again, we made this transition to migrate security to an enabling function. Now the teams at ClearBank, they’re responsible for creating threat models for the software. Responsible for the security of the software.

Again, you’ll see a pattern, there’s a security champion within the team. Security teams move to providing training and coaching. Consulting with the development teams, we still in some cases need the advice. That’s what they’re there for, their expertise. They run the community of practice, so that anyone can attend, and security champions must attend. In the PR process, now when a change goes to production, we need a security champion. Satisfy the regulator. What we found with this, more considered conversations around security and risk: less, no, more, ok. We get that risk, we found the teams were better at explaining to security the risks and the potential problems, because they were more educated about security and the potential risks that different vulnerabilities might introduce. We’ve also removed another handoff and another bottleneck.

Operations as a Gatekeeper

The final one, operations as a gatekeeper. This one was a little bit different. Our operations team don’t become an enabling function, but we’ll get to that. It was like this, we write software. We wanted to blow it. Operations team, we don’t have capacity. We want to use new technology. Operations teams say, in six months, maybe we’ll have the ability to support it. Operations team is supposed to be running things in production. Guess who gets called in the middle of the night? It’s the development teams. They’re still getting called. It wasn’t really working. We moved to operations, to DevOps: you build it, you run it. Our operations team migrate to infrastructure team.

A lot of ClearBank’s in the cloud. Some of it’s on-premise, because we have to have direct connectivity so that we can move money between banks, because that’s a lot of what we do. They’ve owned a lot of that, running the infrastructure, patching, security updates, on-premise connectivity, that kind of thing. We moved everything left to the team. The teams are now building and running the software. They’re on-call 24/7. They understand the software. They’re also allowed to explore new technologies for their specific use case without that bottleneck from the operations team saying, no. Actually, what we found with this was it reduced the number of incidents we had, as point one. Two, it reduced the severity of incidents, increased flow, increased the number of deployments that were going to production. Teams were very happy, because you could start to use the power of Azure, and actually use some of the technologies that are offered there without the bottleneck of the operations team.

Mindset Shift, Influence Through Secondments

One thing to talk about that I think is pretty cool, but pretty open, secondments is something we actively promote. Because if you go and work in a specific area for a period of time, you’re going to gain the knowledge, you’re going to become a broader individual. When we went through this kind of transition with all of these teams, it was really encouraged that development engineers should go and join the testing teams, should go and join the operations teams, should go and join the security teams.

Why? First of all, was because it was going to upskill them. Two, they understand how the development teams work and what they need. It also gave them the context, in security and testing teams, to enable development teams in the best way possible. In summary, for that, every time we allow for end-to-end ownership, which was our goal, we want these small, autonomous powerboats, we saw improvements off the board. We stopped chucking stuff over the fence and, yes, hurried and growing. This is where we were before, and where we’ve ended up. Gandalf saying, you shall not pass. Now we’ve got our enablement teams getting change to production as quickly as possible.

Boundaries and Interactions

That’s all like a really good story, but that’s not all complete reality. There are a lot of other constraints that we’ve had. ClearBank, as with a lot of startups, started off with a monolith, a small group of teams working together. It wasn’t huge constraints on it. One of the challenge, really, was, it wasn’t that modular. What happened was that, as teams grew, we tried to split this monolith into microservices, but we still had some really awkward interactions. Our boundaries weren’t super clear. That’s also a challenge, because when one powerboat starts depending on another to get somewhere, it’s going to take you longer. We focused on boundaries to enable autonomy. This is where we were when I joined. I joined in 2021. We’d done some work. We’d got a version in our APIs to tow between applications. We’ve got what we called our domain events. One of the ways that ClearBank wanted to split stuff was event driven, which if used, it still introduced coupling, which we’ll talk about.

Challenges with this is, yes, we’ve introduced these APIs with versions, but they’re never ever retired. All of these teams have still had these dependencies. We had powerboats and teams that just needed to maintain old stuff for forever because one team in the organization was using it. The domain events, there was no concept of internal or external, which is tough, which meant people started subscribing for really what should have been this size and was bounded context, were being exposed to the outside world. Then people start depending on them, and we’re introducing coupling. What we’ve moved to more recently is talking about boundaries of change. We’ve got three. We’ve got public, internal public, and internal. Public changes continuously.

We’ve introduced stuff so that our customers, if we make a breaking change, they have six months to move, which still allows us to move. We have internal public which are typically around a domain or a bounded a context. They’re internally versioned, and we’ve got strict policies now which mean people have to move off versions within six months when they need to. We introduced a new concept called integration events. I know it can be a bit contentious, everything’s a domain event. We’ve called it integration events, same concept. It’s speaking the external language of the domain, so that people can couple themselves to that, rather than the internal language.

We’ve made some more boundaries, but what we still got is quite awkward interactions. This is the next thing that we needed to tackle. This is an example of our billing engine, and this worked for a period of time, and this is where I say events still create coupling. For us a lot of our products are payment rails. Payment has been completed. Billing engine listens for that and says, I need to charge the customer for this thing that’s happened. Then we had another product. Now the billing team have still to know about that event, and they have to, then, therefore, in the billing engine, figure how to charge for it. Then we had another product capability, and again, they have to subscribe for another event.

These two red arrows signify awkward interactions, and those are the things that we want to mitigate and make sure we got on top of. We moved to this model instead, where we had the product, product billing module. We introduced a concept of a product code. Product code, the products understands what it is. The product code gets charged a fee. Products can create product codes, so they just call an API. We change this billing engine to be a completely self-serve capability, and we removed those awkward interactions. That’s just an example of something that we continuously do all the time. We’re looking for these awkward interactions, and we’re trying to mitigate them.

Speed and Velocity

We’ve removed a load of bottlenecks. We’re moving really quickly. However, we’ve got powerboats going really quickly, maybe not in the direction we wanted to get to. We wanted to go to New York, but we’ve got a couple going around the world. They’re going really fast. This is the difference between speed and velocity. This is where we ended up, not super chaotic, but chaotic enough for it to be tricky. Speed is the rate at which an object is moving. Velocity includes direction. We were missing the direction. We started off as a tech company, but continued as a tech company. It was time for us to add something new. This is how we wanted to be moving.

At ClearBank, this is where we introduced product. The mission at the top at this point became way too abstract for the teams to understand what that meant. There were too many development teams now for the mission to be translated. This is where we introduce product, so we now have a product function that worked really closely with the rest of the business and tech. They’re the translation layer. They provide us with the product strategy, and therefore, help us understand what the product offerings are. That’s how we organize our teams around them. That’s how we started to gain direction. That’s how we’ve translated our mission through to what the actual products and engineering teams have to build.

The Shift Towards Platform Services

Now we’re all aligned. All rosy. Not quite. The transition to DevOps costs money, a different way. It costs us a lot in our cloud bill. Teams now have the freedom to choose technology. Who uses Azure? Who’s had the pleasure of using App Service Environments? Expensive. We’re a bank, so security is pretty important. App Service Environments give you an isolated server rack in Microsoft’s Azure data center. When you’re a bank and it’s top priority, and you’re moving people’s money around, that’s quite important. However, they’re really expensive. We’ve got serverless, teams start to use serverless functions, but they have to sit inside in an App Service Environment. The technology is great, but the use case and the cost is not forefront of mind. Four grand a month to run a cron job once a month, it’s not a good use of money. Those are some of the challenges that they introduced for us, and our costs increased. It’s something that we’re getting on top of now. I’m sure that’s not an unheard-of story for the rest of you.

The other challenge, the security, data, testing, the teams grew, and suddenly they have their own roadmaps. They’ve got their own mission now. The mission is to make the bank more secure. Here’s a roadmap to do it. Save the data. We need to improve the quality of our data, here’s a roadmap to do it. Testing. Our testing could be better, let’s do all of this. Problem with that is all of them are asks of our product teams. It’s not long before the product teams are feeling like this, “I’ve got all of these things that I really have to care about, but I’m here to deliver value to our customers, so we need to do something about that.” This is where we shift our mindset a bit to platform services. I think we’ve done it in a slightly different way. I’ll talk you through that, and hopefully it’s interesting. This is a picture of some of the platform services we offer at the moment for ClearBank.

The whole point of platform, as I’m sure a lot of you know, is to reduce the cognitive load of the teams. To reduce the cognitive load so that we don’t get that brain picture. What we started to do is create this loop. The testing team now also starts to have a testing platform. What’s great about this model is the testing team have always been collaborating with the development teams. We’ve had secondments of the development teams into the testing teams. They’ve really started to understand what the problems are, not just of one team, but of multiple development teams. This allows them to start creating a testing platform at ClearBank. We’ve got things in there, like how we do our SLO reporting, performance testing toolkit, chaos testing toolkit. Those are all things that teams can then consume without having to re-interact and consult with the testing team, to make sure the quality of the software is great. There’s another pattern here.

Similar story with the security team. The security team are constantly collaborating with the engineering teams, the development teams, as they start to build their own platform, which is all about reducing the cognitive load of the team. We’ve got things in here. Where are we? Built-in pipeline, making sure our dependencies are secure. From here you got, we’re a Microsoft house, if you didn’t know. Threat modeling toolkit, so we introduced IriusRisk, which is a toolkit which helps teams model all the threats that could be potentially there in their software. We also introduced the security application score, which is something that teams can see every day. I think we use something called Phoenix, and this not only plugs in with Snyk that we use to scan for dependencies, but also plugs in to the infrastructure. It gives you that end-to-end picture. Also, gives you information on how you can mitigate them, which is, again, something that empowers the teams to take more ownership of this stuff.

The infrastructure team transitioned to our internal developer platform. This is a lot of what people talk about when they talk about platforms. It’s fairly standard what we do here. We’ve got our API platform, which takes care of webhooks, public APIs, authentication, messaging standards, how we do observability, so that we can see our entire system at a glance: compute, storage, all of these things. This has started to bring our cost back under control. Teams at ClearBank now can’t just use what technology they want. We have a bit more of a rigorous process in place.

Anyone can start to use any technology they like and propose to, however, they do have to then hopefully at some point get adopted into platform. That really helps with cost, if you want to start getting on top of that. Back to this, this is also a platform. We’re funding our platforms continuously, it’s not just about internal developer platforms. It’s not just about testing platforms. This is a platform. Billing has become a platform. For us, our accounts is also a platform, whereas before, we considered it a product in its own right. It’s an enabler for other products. This is a great way to get efficiencies in your system, get rid of those awkward interactions finding your platforms.

Dealing with Decisions at ClearBank

Hope now what we’ve done here is, hopefully, we’ve reduced some of that cognitive load from the teams. We’ve pushed it down into the platform services, rather than shifting it left. We saved some money because now the teams aren’t using App Service Environments for serverless stuff that cost 4 grand a month. The teams are a lot happier, and we’re delivering stuff to the customers nice and quickly. Onto decisions. This decision was bad. This makes no sense. Why have you done that? Or maybe somebody actually says, I would like to understand why they made that decision, because they know there’s missing context. There are no right or wrong decisions, there are only tradeoffs. With the context that people had at the point in time, when somebody had made that decision, we don’t know what context they had. If you were in that situation, quite possibly you would have made the same decision. I’ll talk about how we do deal with decisions at ClearBank.

One of the consequences for us at ClearBank was we introduced really localized decisions. We wanted these powerboats to be going really quickly, make their own decisions, have agility, all the rest of this. That’s the context. By design, each product team is now an autonomous unit. They make their own localized decisions with the context that they have. I just briefly talked about some of the challenges that we have there. We want localized decisions, because this is how we scale. We made the decision to embrace localized decisions. Some of the consequences of that are, decisions will be made by people with the most context. Great. We’re pushing it down to the teams who know the most about the products.

However, some decisions that get made may impact others that don’t have the wider context. Then we’re talking about the global system, and maybe we’ve made local optimizations that don’t benefit the global system. Then system drift could become a problem. These teams are slowly making decisions that over time mean that the teams almost become incompatible, because they’ve maybe chosen different technologies or whatever, that aren’t as interruptible anymore. We needed a way to manage this.

Have any of you read the article by Andrew Harmel-Law that was on Thoughtworks, about scaling architecture conversationally? I would recommend reading it. I’ve been fortunate enough to be asked to review some of these chapters in his book as well, which is going to be a great read. It’s one of those books where you’re reading and nodding. They’re always going to be good. We took what he defined as the Architecture Advice Process on that Thoughtworks article and gave it a little bit of a twist to make it work for ClearBank. Best part, we introduced those engineering principles. Whole point of the engineering principles is to aid in day-to-day decision making. Every decision that we make should be looking back up one of our principles. Does this fit with our principles? That’s going to help us to manage a little bit of that system drift in the localized decisions, because we’ve got some principles for people to look about.

We’ve put that in our developer portal, front and center, so it’s the first thing everybody sees when you log on in the morning. A huge amount of our process is actually around architecture decision records. I think they’re great. How many use architecture decision records actively, because it’s pretty popular now? You should explore and see what they can do for you. Not only are they a great way to store a decision, it’s a great way to think, because it forces you to think about the context that you’re working within, the decision that you’re going to make, and the tradeoffs that you’re going to have to make with the decision. All decisions have consequences: some are good, some are bad. Always there’s pros and cons to all of them. This is at the center of how we communicate and make decisions at ClearBank. Everything is written in an architecture decision record.

The piece that’s different to the Thoughtworks article that’s unique to us, I believe, is decision scopes. We had challenges. We had some teams that were mavericks. They would make load of really quick decisions continuously. We had other teams that weren’t so confident with making decisions. They were always looking to someone more senior to make decisions for them. Both of those aren’t great. We needed to find some balance, so that’s why we introduced decision scopes. Decision scopes are quite abstract, purposefully, and it’s all about impact. It’s all about, if I make this decision, who do I think it’s going to impact? If it’s just my team, you make that decision. If it’s my domain or the teams that are working within my domain more closely, have a conversation with them, and make that decision together. If it’s enterprise, we actually have a forum for that, more wide impacting decisions, which we’ll talk about.

This is our process for making decisions. You’ll see, all of them start with writing an ADR, before we’ve even had a discussion to frame the conversation. Is it a team decision? Write an ADR. Have a conversation with the team. Is it agreed? Yes, or no? If no, still store it. Rejected decisions are also super important. Why haven’t I made a decision at this point in time, given this context? That’s also really useful information to make sure you have. For the enterprise ones, you’ll notice it says, bring to AAF. AAF stands for Architecture Advisory Forum. We run this once a week. We’ll talk about that. Architecture Advisory Forum. Previously at ClearBank, we had an architectural control, it’s called architecture review. It was a bunch of senior people taking minutes. It wasn’t that productive. The whole point was we could show to the regulator that we were in control of the direction of our architecture. We’ve changed it now. It’s the same control that we have in place, it’s Architecture Advisory Forum.

This is where we make all the wide-sweeping decisions. It’s all about this impact, the enterprise decisions, they all come here. They come here for discussion. They bring in architecture decision records to the forum. We have a discussion. We have quorum from all areas. We have data, security, infrastructure, senior leadership. We discuss it. We have a conversation, really fruitful. Anyone can come. It’s not just restricted to senior leadership anymore. Why? People making decisions really benefit from maybe more experienced people asking questions about decisions other people are going to make. Then they learn to make better decisions as a consequence of that. It’s also an advice forum. Sometimes you don’t know who or what you need to speak to, which is one of the prerequisites, really, for the forum. Find out who you think it impacts. Sometimes it’s, I would like advice on this topic. Is there a group of people here that could help me out with it? This forum, I think, has been really fruitful, and I think it’s changed the way we think about and do architecture. I think it’s changed the way we made decisions. I think it’s really been a positive for us, for good.

What have we done with this decision-making process? We’ve managed system drift through conversation. We’ve got a history of all the decisions we’ve made. I think 300 or 400 now, maybe in our central repository of enterprise and domain decisions. It’s a learning forum, not just a technical decision-making forum, so people can upskill and learn how to make better decisions. More importantly, I think it’s a catalyst for change. Senior managers bring process changes to this forum now. Why? Because this is how it’s going to impact you. This is what we’re doing. Teams put their hand up, ask questions. This is how it’s going to impact me. They get to make little tweaks to the processes that we’re going to be introducing. That makes them feel really included rather than being dictated to. That’s been really effective, and it really helps make change in the organization a lot easier, if people feel like they’ve had an input into it. Lastly, but most importantly, for our banking license is the regulator’s happy because we’ve got another effective, working control.

Summary

Just to round off, just to show you where we’re at and see how this is working in practice. These are the DORA metrics. This is where we sit at the moment. Deployment frequency, we’re on demand. Change lead time, between one week and one day. That’s because of some of the controls that we have in place that hopefully we’ll be able to optimize in the future. Change failure rate, we’re below 2% on that. We’re pretty good at recovering as we shifted everything towards the teams that build and run teams. Biggest thing that we’ve managed to achieve in the last couple of years is we managed to build a new bank in nine months, from first commit to technology was ready. We’re just waiting for our European banking license now from the Dutch National Bank. Hopefully, we’ll get there really quickly. All of this, and this is really important, was done over a long period of time. All of this was continuous improvement.

None of this was transformation. I’ve seen a lot of people try to do too much at any one point in time. There’s only so much change an organization can absorb at any point in time. It’s really important to have that continuous improvement mindset so that, yes, you get better every day. We are where we are now at ClearBank. We still got that mindset. We’re going to be continuously making some more changes to try and improve as an organization. ClearBank is successful because of its culture. I think that’s true today. We’ve got a shared mission and purpose. The product strategy helps us out with that. We’ve got collaboration, open communication. We talked about a few of the mechanisms that we’ve got for that, with the Architecture Advisory Forum. Continuous learning and growth, which I haven’t talked about all of: take it easy as an example. We’ve got trust, empowerment, autonomy. We’ve got really quick decision making at the right levels.

I think it’s important for the decision making to be made at the right level. We’ve got a culture that breeds high-performing teams, that gives ClearBank the competitive advantage in the market. We’ve managed to avoid being one of the incumbent banks with really heavy processes. We’ve scaled through conversation and people and culture. We’ve managed to avoid just running around like headless chicken all over the globe. This is where I think we are today. It’s a group of powerboats sailing from the UK to New York, hopefully nicely aligned and traveling at quite some speed.

Questions and Answers

Participant 1: The architectural forum, is there an ultimate decision maker? Can you give an example of a really contentious issue?

Gray: Is there an ultimate decision maker? This is one of the challenges we’ve had with it. When there’s been quite a contentious decision, we have come into trouble, where we’ve come to like a stalemate in some cases. That’s true. Most of the time, that can be resolved through more conversation that gets taken offline. Sometimes, maybe myself or one of the more senior people in the call, will have to make a call. Ok, we understand your objections. However, most people agree with this, so we need to move forward, because it moves ClearBank forward. Yes, we have, and they’re always tough to navigate. Most of the time not. Most of the time the conversation is pretty good, and people have valid reasons for having concerns that they have that which people then address. Sometimes, yes.

This is one that’s yet to be resolved, that needs to be resolved. I’ll give you that example. We tried to introduce a concept called deployment units at ClearBank. We’ve gone from monolith to microservices. We’ve gone way this side on the right-hand side, introduced too much complexity into the system. We’ve got 700, 800 deployables, it’s a bit much to manage with our team. We’re trying to bring this stuff back. We put something out with a proposal to deployment units, which is all about finding the right size for these things, and really deploying capabilities, rather than just little things that do one job. A lot of pushback from that, from teams. That’s not just a ClearBank culture, that’s a tech-wide culture that I think really needs a bit of work on. It is happening now. People are feeling the pain. That’s ongoing. We haven’t closed that one out. That’s the challenge. We’ve had process changes where people weren’t very happy, but it had to happen for regulatory reasons or whatever. Those things are a bit easier, but we try to not be too dictatorial to the engineering teams. We want to make change by influence and respect. Sometimes it’s tricky to walk that line.

Participant 2: You talked about the API platform, which is the upside of it. What is it specifically that you build? Is it a custom API, a set of documentation, tools, an IDP. Any feedback on that?

Gray: All of that, really. We’ve got documentation on best practices, how to use the platform. An example of one of the platform service is webhook delivery. We’ve got one mechanism to deliver webhooks to our customers, where people just call an API and they end up to our customers to understand something’s happened. All of our pipelines are templated. They have to be, because we’re a bank, and we have to show that segregation of concerns, when it’s flowing through, approvers have approved change through to production, and that kind of thing. They’re templated. They’re part of our platform. How we get software to production, observability standards, some of that’s documentation, some of that’s packages that teams can then consume and use.

Participant 2: It’s like a toolkit, or?

Gray: Some of it’s toolkits, some of it’s like APIs, some of it’s documentation.

Participant 3: You briefly mentioned cloud costs and the regulation about that, and you already have a platform mindset in your company. Have you ever thought about getting back on-prem or getting on-prem?

Gray: Going back on-prem? No. We haven’t. There’s been articles recently about, which company was it? They said they’ve massively reduced their cloud costs by going back on-prem and buying a load of Dell service. It’s quite contentious. No, not for us. There’s always concern in finance from the regulators about critical partners, and if they fail, then, does your bank fail? Yes, sometimes there’s pressure to explore multiple cloud providers. We do also have an agreement with AWS. We don’t actively build and deploy to both clouds at the moment, but maybe in future we will need to, to give the regulators that peace of mind. At the moment, no, no plan to go back to on-prem. It suits us the way it runs at the moment.

Participant 4: Coming from a finance background, we also have one other group of people doing the 24/7 monitoring, like mission control. How is that integrated in your DevOps teams? Is this integrated? I know that the teams are running but they are running the operations, the DevOps teams, but we have mission control. They are 24/7. There’s someone sitting in a room and monitoring everything. Do you have something like this? Is this integrated? Because we always have these problems that the people in mission control, they have the control over all the services, which is quite a lot, and no one can understand whatever happens there. It’s very hard to integrate these people into our normal workflows.

Gray: The teams have their own dashboards. We use PagerDuty as well. We have our own dashboards and monitoring set up for each of the individual teams that look after their own products. That’s how it’s monitored. They don’t sit there and watch it every day. It’s been quite a journey to get our monitoring and observability to where it is today. We introduced it. We had a load of noise. Then you got to get rid of your false positives. Now we’re at a fairly decent spot where we’ve balanced that out. We get alerts when there’s something going wrong with the system, rather than somebody watching it 24/7.

Participant 4: I understand that you just react to events, you don’t create the events yourself, on which you react?

Gray: No. We’ve got rules in place which will then trigger something and notify us, and wake you up in the middle of the night.

Participant 5: What is the composition of these Architectural Advisory Forum. What is the role of the principals and staff engineers, if you have those?

Gray: We facilitate.

Participant 5: Maybe you have principals being part of that group, or maybe senior ICs.

Gray: Everyone’s welcome. When we rolled it out, we invited principal engineers, staff engineers, senior engineering management, and all of the team leads, with the instruction, delegate it to other people who care about it. Since then, the invite list has grown to a lot of people. We’ve not taken that access away from them because we think it’s valuable to them. Because they learn how to make better decisions by listening to us discussing decisions. We still see value in that. It’s not got a strict composition. The only bit that is strict, we have to make sure we have quorum for regulatory reasons, so it is audit, it is security, data infrastructure.

Participant 6: How long do your secondments last, and how do they get initiated or triggered?

Gray: Some are permanent. Some of those secondments, they become leaders of their areas, with respect to areas. Security is a great example. Security engineering, we had someone called Seb, who then left the infrastructure space and ended up being our security leader. The other answer is, it depends. Typically, it’s like a quarter, three months, and then you go back to your team. For us, it’s always been open for negotiation. We’ve got security engineers who were software engineers who work in the security team now building up platform out. It depends, but typically three months.

Participant 6: How do you guys think about staffing your platform teams? Were they fixed, or were they loosely defined groups, like project teams, for example? Could you share how many engineers per team do you put into these platform teams or project teams?

Gray: Five or six, typically, in each platform team. Between platform and infrastructure, which also operate on-prem, I think we’ve got seven teams working in that area at the moment.

Participant 6: How did you guys think about staffing your platform teams. Are they fixed, or are they loosely defined? Because API platform, for example, you had a really huge box, and then the rest were smaller.

Gray: We have teams that own compute and messaging. We have other teams that own APIs, which include webhooks. They actually also own authentication and our public API interface, sort of security stuff. We organize them around capabilities and the services that they offer.

How do we staff them? We’ve had some challenges in this area, where we staff the platform teams with infrastructure engineers. One of the challenges we found with that is that they didn’t have much empathy for the development teams, because they’re there to enable them reduce the cognitive load so they provide services to do exactly that, whereas we found that they were interested in the new technology, which actually in some cases, would increase the cognitive load on the development teams. Because, we should be doing it like this, and these are all these complicated ways we could be better. The goal is for you to reduce the cognitive load, so that’s just adding more at the moment. Is this something that we need to do? We’ve started to change that a bit by, again, bringing development engineers into the platform space, and then they have that empathy with the development team, so that they can make sure they’ve got that kind of development voice while they’re building the platform services as well. We’ve also invested in product owners in that area as well, as before. It’s very technology led.

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Anthropic released two new models: Claude 3.5 Haiku and an improved version of Claude 3.5 Sonnet. They also released a new feature for Claude 3.5 Sonnet that allows the model to interact with a computer’s GUI the same way a human user does.

Claude 3.5 Haiku is the company’s fastest model; the new version outperforms larger models such as GPT-4o and the previous generation of Claude 3.5 Sonnet on the SWE-bench Verified coding benchmark. The upgraded Claude 3.5 Sonnet performs even better on that benchmark, “higher than all publicly available models” according to Anthropic. The model also supports a new feature, computer use, which allows it to interact with a computer by interpreting the images on the screen, moving the mouse pointer, clicking buttons, and entering text via a virtual keyboard. This allows the model to interact with virtually any program, not just ones that support an API. According to Anthropic, 

Computer use is a completely different approach to AI development. Up until now, LLM developers have made tools fit the model, producing custom environments where AIs use specially-designed tools to complete various tasks. Now, we can make the model fit the tools—Claude can fit into the computer environments we all use every day. Our goal is for Claude to take pre-existing pieces of computer software and simply use them as a person would.

The computer use feature relies on Claude’s ability to interpret images. Anthropic describes it as “taking screenshots and piecing them together.” One key advancement was training the model to accurately count pixels; many LLMs struggle with similar tasks such as counting the number of letters in a word. Without this skill, the model would be unable to move the computer mouse to the proper place.

Claude currently has the top spot on the OSWorld benchmark leaderboard, which tracks the ability of AI agents to interact with computers. While humans typically score higher than 70% on this benchmark, Claude’s best score is 14.9%. However, GPT-4, “the next-best AI model in the same category” according to Anthropic, scores only 7.7%.

Users on Hacker News discussed the computer use feature, pointing out its potential for automating a wide range of common business processes

This is actually a huge deal. As someone building AI SaaS products, I used to have the position that directly integrating with APIs is going to get us most of the way there in terms of complete AI automation…I started to realize that pretty much most of the real world runs on software that directly interfaces with people, without clearly defined public APIs you can integrate into…I am glad they did this, since it is a powerful connector to these types of real-world business use cases that are super-hairy, and hence very worthwhile in automating.

Anthropic notes that the feature still “remains slow and often error-prone.” Alex Albert, the company’s Head of Claude Relations, posted on X that:

It’s not perfect yet. The model struggles at times with basic computer actions which can lead to some amusing moments. While filming demos, Claude accidentally stopped a long-running screen recording, causing all footage to be lost. Later, Claude took a break from the coding demo and began to browse photos of Yellowstone National Park.

The computer use feature is currently in public beta. Anthropic also released example code on GitHub demonstrating how to use the feature.

Article originally posted on InfoQ. Visit InfoQ

This week’s Java roundup for November 4th, 2024 features news highlighting: the first candidate release of Spring Cloud 2024; an update on Project Loom; the release of Open Liberty 24.0.0.11; point and milestone releases for Apache Groovy; and an update on Jakarta EE 11.

OpenJDK

For the third week in a row, it was a busy week in the OpenJDK ecosystem during the week of November 4th, 2024, highlighting: five JEPs, having successfully completed their respective reviews, are now Targeted for JDK 24; five new JEPs have been Proposed to Target for JDK 24 and will be under review during the week of November 11th, 2024; and two JEP drafts that have been promoted to Candidate status. More details may be found in this InfoQ news story.

JDK 24

Build 23 of the JDK 24 early-access builds was made available this past week featuring updates from Build 22 that include fixes for various issues. Further details on this release may be found in the release notes.

For JDK 24, developers are encouraged to report bugs via the Java Bug Database.

Jakarta EE

In his weekly Hashtag Jakarta EE blog, Ivar Grimstad, Jakarta EE Developer Advocate at the Eclipse Foundation, provided an update on Jakarta EE 11, writing:

Jakarta EE 11 Core Profile is just about ready for release review. Everything is ready and all artefacts are staged or published according to the Jakarta EE Specification Process (JESP).

The Jakarta EE TCK Project is working heroically to finalize the TCK so we will be able to have the release reviews for Jakarta EE 11 Platform and Jakarta EE 11 Web Profile underway in the beginning of December. The goal is to have them completed, or at least ongoing when JakartaOne Livestream is happening on December 3rd, 2024.

The road to Jakarta EE 11 included four milestone releases with the potential for release candidates as necessary before the GA release in 4Q2024.

Project Loom

Build 24-loom+10-110 of the Project Loom early-access builds was made available to the Java community this past week and is based on Build 22 of the JDK 24 early-access builds. This build improves the implementation of Java monitors (synchronized methods) for enhanced interoperability with virtual threads.

Spring Framework

The first release candidate of Spring Cloud 2024.0.0, codenamed Moorgate, features bug fixes and notable updates to sub-projects such as: Spring Cloud Kubernetes 3.2.0-RC1; Spring Cloud Function 4.2.0-RC1; Spring Cloud OpenFeign 4.2.0-RC1; Spring Cloud Stream 4.2.0-RC1; and Spring Cloud Gateway 4.2.0-RC1. This release is based on Spring Boot 3.4.0-RC1. More details on this release may be found in the release notes.

Open Liberty

IBM has released version 24.0.0.11 of Open Liberty featuring: new messaging and authorization support for InstantOn, the Open Liberty implementation of Checkpoint/Restore in Userspace (CRIU), for improved application startup; and a new webModuleClassPathLoader configuration attribute for improved enterprise application class loader control that is referenced by a web module Class-Path attribute.

IBM has recently submitted a Compatibility Certification Request (CCR) for the Jakarta EE 11 Core Profile using Open Liberty 24.0.0.11-beta as certification.

Quarkus

Quarkus 3.16.2, the first maintenance release (version 3.16.0 was skipped), featuring notable changes such as: open the OidcRequestContextProperties class for modification so that request filters may pass in their own properties; and a removal of throwing a NullPointerException from the interceptAfterAllMethod() method, defined in the QuarkusTestExtension class, to resolve an intermittent issue in the CI. More details on this release may be found in the changelog.

Apache Software Foundation

The release of Apache Kafka 3.9.0 delivers bug fixes and new features/improvements such as: the ability to re-enable Tiered Storage if it has previously been disabled; and the addition of a --remote-log-metadata-decoder flag to the kafka-dump-log.sh tool that may be used to decode the payload of the __remote_log_metadata records produced by the default instance of the RemoteLogMetadataManager interface. More details on this release may be found in the release notes.

Versions 5.0.0-alpha-11, 4.0.24 and 3.0.23 of Apache Groovy provide bug fixes, dependency upgrades and new features such as: improved static type checking error handling when a method reference is being provided for a type that is not a functional interface; and declare the GroovyClassLoader class to be parallel capable to eliminate the use of reflection. More details on these releases may be found in the release notes for version 5.0.0-alpha-11, version 4.0.24 and version 3.0.23.

Gradle

The third release candidate of Gradle 8.11.0 delivers continuous updates on new features such as: improved performance in the configuration cache with an opt-in parallel loading and storing of cache entries; the C++ and Swift plugins now compatible with the configuration cache; and improved error and warning reporting in which Java compilation errors are now displayed at the end of the build output. More details on this release may be found in the release notes.

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Assetmark Inc. cut its holdings in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 11.2% in the 3rd quarter, according to the company in its most recent disclosure with the Securities and Exchange Commission (SEC). The institutional investor owned 25,559 shares of the company’s stock after selling 3,214 shares during the period. Assetmark Inc.’s holdings in MongoDB were worth $6,910,000 at the end of the most recent reporting period.

A number of other institutional investors and hedge funds also recently made changes to their positions in MDB. Vanguard Group Inc. boosted its position in MongoDB by 1.0% in the first quarter. Vanguard Group Inc. now owns 6,910,761 shares of the company’s stock worth $2,478,475,000 after purchasing an additional 68,348 shares during the last quarter. Swedbank AB grew its position in MongoDB by 156.3% during the second quarter. Swedbank AB now owns 656,993 shares of the company’s stock valued at $164,222,000 after buying an additional 400,705 shares during the period. Champlain Investment Partners LLC increased its holdings in MongoDB by 22.4% during the first quarter. Champlain Investment Partners LLC now owns 550,684 shares of the company’s stock worth $197,497,000 after buying an additional 100,725 shares during the last quarter. Clearbridge Investments LLC raised its position in MongoDB by 109.0% in the first quarter. Clearbridge Investments LLC now owns 445,084 shares of the company’s stock worth $159,625,000 after acquiring an additional 232,101 shares during the period. Finally, Thrivent Financial for Lutherans boosted its stake in MongoDB by 1,098.1% in the second quarter. Thrivent Financial for Lutherans now owns 424,402 shares of the company’s stock valued at $106,084,000 after acquiring an additional 388,979 shares during the last quarter. Institutional investors and hedge funds own 89.29% of the company’s stock.

MongoDB Trading Up 0.5 %

Shares of MDB opened at $291.57 on Tuesday. MongoDB, Inc. has a fifty-two week low of $212.74 and a fifty-two week high of $509.62. The firm has a market cap of $21.54 billion, a price-to-earnings ratio of -96.55 and a beta of 1.15. The firm has a 50 day moving average price of $277.72 and a 200-day moving average price of $276.45. The company has a debt-to-equity ratio of 0.84, a quick ratio of 5.03 and a current ratio of 5.03.

MongoDB (NASDAQ:MDB – Get Free Report) last posted its quarterly earnings results on Thursday, August 29th. The company reported $0.70 earnings per share (EPS) for the quarter, beating the consensus estimate of $0.49 by $0.21. The business had revenue of $478.11 million during the quarter, compared to analyst estimates of $465.03 million. MongoDB had a negative net margin of 12.08% and a negative return on equity of 15.06%. The company’s revenue was up 12.8% on a year-over-year basis. During the same period in the prior year, the business posted ($0.63) earnings per share. As a group, equities research analysts forecast that MongoDB, Inc. will post -2.39 earnings per share for the current year.

Analysts Set New Price Targets

Several equities research analysts have recently issued reports on MDB shares. Wedbush upgraded shares of MongoDB to a “strong-buy” rating in a research note on Thursday, October 17th. Bank of America raised their target price on MongoDB from $300.00 to $350.00 and gave the stock a “buy” rating in a research report on Friday, August 30th. Wells Fargo & Company upped their price target on MongoDB from $300.00 to $350.00 and gave the company an “overweight” rating in a research report on Friday, August 30th. Truist Financial lifted their price objective on MongoDB from $300.00 to $320.00 and gave the stock a “buy” rating in a report on Friday, August 30th. Finally, DA Davidson upped their price objective on shares of MongoDB from $330.00 to $340.00 and gave the company a “buy” rating in a report on Friday, October 11th. One equities research analyst has rated the stock with a sell rating, five have given a hold rating, nineteen have issued a buy rating and one has assigned a strong buy rating to the company’s stock. Based on data from MarketBeat, MongoDB has an average rating of “Moderate Buy” and an average price target of $334.25.

View Our Latest Stock Analysis on MDB

Insider Activity

In related news, CFO Michael Lawrence Gordon sold 5,000 shares of the company’s stock in a transaction on Monday, October 14th. The shares were sold at an average price of $290.31, for a total transaction of $1,451,550.00. Following the completion of the sale, the chief financial officer now directly owns 80,307 shares in the company, valued at $23,313,925.17. This represents a 0.00 % decrease in their position. The transaction was disclosed in a filing with the SEC, which is available through this link. In related news, CFO Michael Lawrence Gordon sold 5,000 shares of the business’s stock in a transaction dated Monday, October 14th. The stock was sold at an average price of $290.31, for a total transaction of $1,451,550.00. Following the transaction, the chief financial officer now owns 80,307 shares in the company, valued at $23,313,925.17. The trade was a 0.00 % decrease in their position. The sale was disclosed in a document filed with the Securities & Exchange Commission, which is available at this link. Also, CEO Dev Ittycheria sold 3,556 shares of the company’s stock in a transaction that occurred on Wednesday, October 2nd. The shares were sold at an average price of $256.25, for a total value of $911,225.00. Following the transaction, the chief executive officer now directly owns 219,875 shares of the company’s stock, valued at $56,342,968.75. The trade was a 0.00 % decrease in their position. The disclosure for this sale can be found here. Over the last 90 days, insiders sold 24,281 shares of company stock valued at $6,657,121. 3.60% of the stock is currently owned by company insiders.

About MongoDB

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides 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-premises, 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.

Read More

Before you consider MongoDB, you’ll want to hear this.

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While MongoDB currently has a “Moderate Buy” rating among analysts, top-rated analysts believe these five stocks are better buys.

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Apache Cassandra is an established open source, NoSQL database designed for handling workloads across commodity servers. So what applications is it now supporting?

The annual Cassandra Community survey has landed, revealing Cassandra’s evolving usage. Among respondents, 41 percent said Cassandra was their organization’s primary database, with more than 50 percent of enterprise data going through it. Over a third (34 percent) said 10 to 50 percent of their enterprise data was handled by Cassandra.

“Scalability” was cited by 78 percent of respondents as a reason for using the database, while 73 percent claimed it was down to “performance.”

Among multiple use cases at organizations, 47 percent use the database for time series data, and 34 percent use it for event logging. In addition, 31 percent use the platform for data aggregation.

Other significant uses include online retail/e-commerce, user activity tracking, user profile management, fraud detection, and backup and archiving.

In the future, 43 percent vowed to use Cassandra for AI workloads, and 38.5 percent planned to use it for machine learning workloads. Currently, 36 percent of users said they were already “experimenting” with the database to run at least one generative AI app.

In terms of data volumes, 30 percent currently run over 100 TB on Cassandra, and 27 percent handle 10 to 100 TB on it. Just under a quarter (23 percent) put 1 to 10 TB through it.

The survey found that 35 percent of Cassandra workloads were already in the cloud, and 25 percent of organizations pledged to put 10 to 50 percent of their workloads into the cloud over the next 12 months. Eight percent said they would be moving at least half of their workloads into the cloud in the next year.

Some 37 percent of Cassandra users had been using the platform for five to ten years, and nearly a fifth (18 percent) had used the database for upwards of ten years.

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Presented by MongoDB

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In 2020, the pandemic was in full swing, and many office workers were working remotely for the first time. Paylocity, a provider of cloud-based payroll and human capital management (HCM) software, found their proprietary Community application embraced by customers who were looking to nurture stronger connections and engagement within remote teams. However, the resulting upswing in traffic  showed Paylocity that the platform’s SQL-based architecture was no longer meeting their required performance metrics.

For a database solution that could meet all their needs, Paylocity tech leaders turned to MongoDB — and found a solution that cost five times less than their previous vendor’s solution. Today, Paylocity runs over twenty applications on MongoDB, and its developers can create an application within minutes — something that used to take weeks.

VentureBeat sat down with Stephen Dick, VP of engineering at Paylocity, and Sahir Azam, chief product officer at MongoDB, to talk about that relationship — from the opportunities and challenges that Paylocity and MongoDB have experienced as they’ve grown, to the ways Paylocity’s partnership with MongoDB has helped drive their success along the way. 

VB: What technical challenges kicked off Paylocity’s quest for a new database solution, and what made you ultimately choose MongoDB?

Along the way, we evaluated many options but ultimately chose MongoDB as our database partner. There were technical determinants to the decision for sure, like the flexibility of MongoDB’s schema-less architecture, performance benchmarks and the scalability of the architecture. But important drivers were also how proactive the support and services team were.

And of note, the MongoDB development community is very rich and the company places a premium on making developers’ lives easier. It’s a commitment we share. I have an entire team dedicated to improving the developer experience within Paylocity, so there was a shared sense of purpose. 

VB: How have these developer tools and support from MongoDB impacted your development team and your bottom line?

SD: It used to take a lot of time to create the infrastructure, integrate our standard frameworks and tools, adopt our commonly held libraries and so on. To move faster, we adopted modern developer experience (DevEx) frameworks, including SPACE, which emphasizes productivity, satisfaction, collaboration and flow to achieve a faster time-to-market. This led to investments in cloud infrastructure, starter packages, common platforms and innovative documentation. We’re rolling out new AI code assist tools, including Tabnine, which will further enhance the developer experience. 

Building strategic relationships with key vendors is a critical part of our productivity strategy. For example, MongoDB’s support has been proactive, engaging with us early in the process to avoid common pitfalls and offering solutions before challenges arise, rather than reacting to issues after the fact. This level of partnership is incredibly invaluable. It helps us maximize the effectiveness of our tools. 

Overall, we’ve freed up so much developer time to focus on higher-value work. This has led to faster iteration cycles and fewer code errors, contributing to both cost savings and a smoother development process. 

VB: MongoDB prides itself on serving developers. Sahir, can you tell me a bit about what that means, and how you work with customers like Paylocity to make their developers’ lives easier?

SA: Sure, from the very beginning, MongoDB was created to empower innovators to create, transform and disrupt industries by helping them unleash the power of software and data.

And, as we like to say, MongoDB was built by developers, for developers. Our developer data platform is a powerful database with an integrated set of related services that allow development teams to address the challenging requirements for today’s wide variety of modern applications — all within a unified and consistent user experience.

Always looking ahead, we address developers’ ever-growing needs through cutting-edge products to help them make the most of their data. Examples include MongoDB Atlas Search enabling developers to build full-text search at scale, Atlas Stream Processing for working with data in motion and at rest, and Atlas Vector Search to implement retrieval-augmented generation (RAG) in AI applications.

And we hear from customers all the time just how much MongoDB has helped them operate more efficiently. Like Rent the Runway, who was able to achieve a 67% decrease in inventory processing time using MongoDB Atlas. Or, GE HealthCare, which used MongoDB to manage the lifecycle of its medical IoT devices and saw an 83% decrease in retrieval time, resulting in better care for GE HealthCare customers.

We love tech-forward innovator brands like Paylocity. We strive to help them remove blockers so that they can focus on what they do best to better serve their customers.

VB: Paylocity has evolved a lot since its inception. Stephen, what are you doing right now that’s got you and your customers excited?

SD: One of our core values is “think next generation.” It keeps innovation at the forefront of everything we do. For example, with our recent acquisition of Airbase, we’re expanding our product capabilities into the office of the CFO. It will allow our customers to use management and financial solutions alongside our HR and payroll tools, providing a complete suite of services to manage both people and finances, under a single unified platform.

We heard from our customers that they were looking for greater control over the balance sheet. So, we’re excited to take these new capabilities to market. Airbase’s technology will empower our customers with tools for expense management, bill payments and corporate card management and will enable customers to streamline their operations, reduce financial complexity and drive more accurate financial forecasting.

VB: What else can we expect to see from Paylocity moving forward? 

SD: We hear frequently from smaller clients that they need to move away from spreadsheets and siloed workbooks. From our Enterprise clients, we hear about the need to provide deeper connectivity between departments and richer insights. As we move beyond the borders of traditional HCM, our customers benefit from deeper connectivity and advanced capabilities that scale with their business. 

That’s why we’re continuing to innovate. That’s why Paylocity is on a trajectory of growth. We’re driving further integration of HR, IT and financial functionalities into a single platform. Our customers will see simplified processes, fewer redundant systems and lower overhead.

That doesn’t mean we will lose our focus on HCM. Our commitment to HCM is rock solid and we’re proud of the impact our products have had. Our Community product, powered by MongoDB has helped create connected workplaces. And we’re looking forward to future partnerships that allow us to have an amplified impact on the workplaces around us. 

VB: And Sahir, what’s in the pipeline for MongoDB, and what upcoming innovations are you excited to share with developers?

SA: I’d say that we’re particularly excited about the chance to help developers make the most of AI.

Specifically, we recently announced the general availability of the MongoDB AI Applications Program (MAAP). It’s a first-of-its-kind program that’s designed to help organizations take advantage of AI. MAAP offers customers a variety of resources to put AI applications into production: reference architectures, integrations with leading technology providers, professional services and a unified support system to help customers quickly build and deploy AI applications.

For more — including details of the MAAP ecosystem of companies — check out the MongoDB AI Applications website.

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Artificial Intelligence (AI) is rapidly transforming industries, making AI skills some of the most sought-after competencies in today’s job market. But what exactly are these skills, and how can they empower individuals and businesses to stay ahead?

At the core of AI are foundational skills in computer science and mathematics. Proficiency in programming languages like Python, R, and Java is crucial, as they are commonly used for developing AI models. Understanding algorithms and data structures is equally important because AI fundamentally revolves around building efficient algorithms to process and analyze data.

A significant aspect of AI involves machine learning, where the ability to design, train, and evaluate models is pivotal. Machine Learning (ML) skills allow individuals to create systems that can learn from data and improve over time. Having a firm grasp on ML libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn is essential for anyone looking to build or manage AI systems.

Furthermore, data analysis and data engineering skills play a critical role in AI. Since AI models rely on large volumes of data, skills in data preprocessing, visualization, and storage are necessary to ensure data is clean and accessible. Familiarity with SQL and NoSQL databases, as well as cloud services like AWS and Azure, enhances one’s capacity to handle and process data effectively.

Finally, possessing strong problem-solving and critical-thinking skills can distinguish an AI professional. AI often involves tackling complex challenges that require innovative solutions and the ability to think analytically.

By developing these AI skills, individuals and organizations can leverage the potential of AI technologies, driving efficiency, innovation, and competitive advantage in an increasingly digital world.

Mind-Blowing AI Skills That Will Transform Your Career!

The rise of artificial intelligence is not only reshaping industries but also creating new lifestyles and opportunities worldwide. Beyond the technical competencies needed for developing AI systems, there’s a burgeoning demand for AI ethics and AI governance skills. But why are these emerging skills essential, and how do they affect us?

New AI applications are continuously raising ethical questions about privacy, bias, and transparency. Understanding AI ethics helps individuals and organizations navigate controversial scenarios, ensuring AI implementations align with societal values and established laws. Consequently, skills in ethical decision-making and legal compliance are fundamental for businesses leveraging AI in sensitive contexts.

As AI continues to evolve, it’s also crucial to focus on AI governance. This includes the development and enforcement of policies and frameworks to manage AI systems responsibly. Skills in governance ensure that AI solutions are reliable and safe, preventing unintended consequences and misuse.

Question: What are artificial intelligence skills beyond technical expertise? AI skills also encompass ethical considerations and governance, ensuring systems are developed and deployed responsibly.

The influence of AI on communities is profound, creating new job prospects in tech and other sectors. However, it also poses challenges like worker displacement and societal inequities. Balancing these dynamics is key, prompting countries to invest in education and reskilling programs to prepare their workforce for AI’s impact.

For further insights on how AI is shaping our world, explore IBM, Microsoft, and Google, leaders in AI innovation. Embrace the AI era by expanding your skills, ensuring a positive and transformative impact on your career and community.

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