Systems | Development | Analytics | API | Testing

Two years ago, we announced our Databricks partnership—including the launch of ThoughtSpot for Databricks, which gives joint customers the ability to run ThoughtSpot search queries directly on the Databricks Lakehouse without the need to move any data. Since then, we’ve empowered teams at companies like Johnson & Johnson, NASDAQ, and Flyr to safely self-serve business-critical insights on governed and reliable data.

Today, we are glad to release the Koyeb Terraform Provider and announce that we are a Hashicorp Technology partner. Terraform has become an essential tool for many developers who manage infrastructure and applications running in the cloud. We are excited to offer our users a way to manage their Koyeb resources using Terraform.

We are excited to announce the release of NeoLoad 9.2. This release delivers several important features, including enhancements to RealBrowser (see the NeoLoad 9.0 release blog for more information on RealBrowser) as well as integrations with opensource performance testing tools like JMeter and Gatling.

Build Pipelines, beta, is now available on all plans for Bitrise users. Organize your CI/CD process, set up advanced configurations with multiple tasks, and run them in parallel—today.

We’re excited to announce the release of Kong Mesh and Kuma 2.1! In this release, we’re shipping the full suite of new and improved policies announced (and started) in 2.0. Additionally, we’re launching some more great UX improvements in the UI and a host of smaller fixes. In order to take advantage of the latest and greatest in service mesh, we strongly suggest upgrading to Kong Mesh 2.1. Upgrading is simple through kumactl or Helm.

Since our last Kong Konnect monthly update, we’ve been hard at work to deliver some more powerful features in Kong Konnect, including consumer groups, dynamic client registration using Curity and Auth0, plugin ordering, and streamlined Docker runtime instance creation. Read on to learn what’s new in Kong Konnect.

Data quality is fairly simple nomenclature to describe the state of the data being processed, analyzed, fed into AI, and more. But this modest little term belies an incredibly critical and complicated reality: that enterprises require the highest level of data quality possible in order to do everything from developing product and business strategies, and engaging with customers, to predicting the weather and finding the fastest delivery routes.