Systems | Development | Analytics | API | Testing

How can engineers enable real-time insights when working with high-throughput, data-intensive streams? In this 30-minute session, Imply and Lenses.io show you how to Enable self-service access for developers working with critical, high-velocity data flows in #apache #kafka Ingest and normalize complex data structures, enabling real-time analytics at scale via modern databases like #druid.

ThoughtSpot Analytics Cloud 9.12.5 is now available! Ask questions about your business in natural language using the ThoughtSpot mobile app. Design visual data stories with customized font, background, header styling, and auto-fitting images. Forecast future trends with single-click time series forecasting in SpotIQ.

If you want to #automate the publishing of API products leveraging a Docs-as-Code approach then this video sets out a convention based approach which will get you automating in minutes without having to consume the raw management APIs! Useful links.

In this episode, Frank Kilcommins and Yousaf Nabi, walk us through the new `import-insomnia-collection` command of the Explore.CLI. Now you can easily import collections from Insomnia directly into SwaggerHub Explore. Additionally, Explore.CLI now comes with standalone binary options meaning you do not need to have.NET installed.

While dashboards let you see real-time performance to answer “How am I doing right now?”, Reports take it a step further. With Reports, you can provide a more comprehensive view of your historical performance - with all the context - to your teams, clients, or stakeholders. In this video, you’ll learn: Bookmark this tab AND share the series with your entire company/team! Databox empowers everyone - leaders, managers, and individual contributors alike.

Learn how to leverage the native monitoring capabilities of the Python Kafka producer along with Confluent Cloud’s Metrics API while exploring how linger.ms affects latency and batch sizes.

In this video, Titus Fortner, senior developer advocate, explains the differences between flaky tests and brittle tests. This is followed by a discussion of the three things to manage to minimize flakiness: ensuring known states, maintaining independent data and creating a good synchronization strategy.

88% of Databricks users surveyed are turning to AI to improve bug-fixing effectiveness (Databricks). Why? Troubleshooting modern data stacks is typically a toilsome and manual process. The good news – data teams that use DataOps practices and tools will be 10 times more productive (Gartner). With this in mind, Unravel is hosting this live event to demonstrate how AI-enabled observability for Databricks’ Data Intelligence Platform helps you proactively achieve performance and reliability.