We’re honored to win this prestigious award, and we’re doubling down on the Lakehouse architecture with Databricks SQL analytics plans. Fivetran is the proud recipient of the Databricks ISV Partners Innovation Award as announced at this week’s Data & AI Summit Europe event. The award recognizes how Fivetran has collaborated with Databricks to empower data professionals to accelerate time to insights with Delta Lake and the Lakehouse architecture for the modern data stack.

If you need to run a JMeter Load Test and it’s a burden to download Apache JMeter locally on your machine, open it and manually create your JMeter test file, now there is a much easier way to record browser interactions with JMeter. The LoadFocus | JMeter Load Testing in the Cloud Chrome Extension for recording JMeter test script files (.JMX files) from LoadFocus is a free tool to easily record and run load tests in the cloud.

Today is a big day for Kuma! Kuma 1.0 is now generally available with over 70 features and improvements ready to use and deploy in production to create modern distributed service meshes for every application running on multiple clusters, clouds, including Kubernetes and VM-based workloads. Before we unpack this release, a big thank you to the community and to the users that have helped releasing this major version of Kuma with their contributions and feedback.

Today, we’re proud to announce the release of Kong Enterprise 2.2 GA! Kong Enterprise 2.2 is built on top of version 2.2 of our popular open source gateway and brings with it a slew of new features and some Enterprise-only features on top. Let’s dive in! For a long time, Kong has supported not only HTTP/HTTPS traffic with REST and gRPC APIs, but also raw TCP streams. In version 2.2, we now extend our support to include UDP-based protocols as well!

Exactly-once is a desirable (if not critical) message delivery guarantee and a remarkably complex engineering challenge to solve. In this blog post, we will look at what exactly-once means in the context of distributed pub/sub systems, and the exactly-once guarantees that the Ably realtime pub/sub messaging platform provides. Ably often acts as the broker in data streaming pipelines: publishers send messages to our platform, and we deliver these messages to subscribers.

COVID-19 caused a significant shift in consumer sentiment; making mobile shopping ever so popular — and digital brand engagement a competitive necessity. With an increasingly noisy online marketplace and physical stores losing traction, it’s time to adopt immersive consumer tech to attract new shoppers, revenue, and brand recognition.

If your experience as a developer is anything like mine, the best moments are those known as the "flow state.” When distractions drift into the background and all your energy is going in the creative direction of solving the problem at hand. Your brain is directly connected to your users through your code. Months of progress happen in hours. Unfortunately, those moments are rare. But it’s not emails or Slack messages that are the biggest distractions.

Imagine you’ve been working on a new feature for weeks. Finally—after exhaustive QA and testing, and more late coding sessions than you care to count—you release with a feeling of both accomplishment and relief. Only to be woken up at 3am that something’s wrong. Or worse, you get flooded with customer complaints that it’s not working properly. Well, it’s probably a scenario you don’t need to imagine. Every developer has a version of this story to tell.

If the elements of digital transformation weren’t a priority for organizations before COVID-19, they’re at the top of the agenda now. According to Statistica, post-COVID digital transformation spending has skyrocketed in 2020 with an expected 10.4% increase by the end of the year. As the pandemic continues, businesses are facing remote team challenges and pressure to increase operational efficiency. Therefore, the trend toward digital transformation is likely to continue.

One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. Knowing that a transaction is fraudulent is a critical requirement for financial services companies, but knowing that a transaction that was flagged by a rules-based system as fraudulent is a valid transaction, can be equally important.