Systems | Development | Analytics | API | Testing

Free Kafka tooling: 6 annoying tasks to offload

You didn’t become a developer to spend hours hunting down missing messages, or debugging consumer issues. Yet here we are. Valuable dev time evaporates as you wrestle with Apache Kafka, or wait for a central team to unblock you, when you should be finding, prepping, and shipping streaming data in minutes. Lenses Community Edition tackles these everyday frustrations.

Scale Your Python Analytics With Pandas On Snowflake

Massive data sets can overwhelm native Pandas, causing memory issues and slow performance. Pandas on Snowflake eliminates these constraints by running Python code directly in Snowflake, with no rewrites needed. This demo shows how to transform and visualize large data sets using the familiar Pandas API with Snowflake’s distributed compute. Boost your data workflows and maintain security and governance, all while staying within the Pandas ecosystem.

The FinOps Advantage: Aligning Data Teams with Financial Goals in Snowflake Environments

Aligning technical operations with financial objectives is crucial yet challenging. Join us for this session in our Weekly Walkthrough drop-in series, "Controlling Cloud Costs," where we'll explore how to unite data teams and finance for Snowflake success. You will gain the knowledge and tools to create a FinOps framework that aligns your Snowflake operations with your organization's financial goals. With Unravel's Data Actionability Platform, you can see deep insights into your cloud spending and make informed decisions that balance innovation with cost-efficiency.

Data-Driven Testing vs. Keyword-Driven Testing: which is better?

Test automation has become a critical component of modern software development. However, choosing the right automation strategy can be challenging, as different approaches offer varying benefits depending on project needs and team expertise. Two widely used methods in test automation are data-driven testing and keyword-driven testing. Both approaches aim to enhance test execution by making tests more reusable, scalable, and maintainable, but they differ in their implementation and use cases.

Reduce bandwidth and processing overhead with conflated subscriptions

For some realtime applications, the latest state matters more than the full stream of updates. If you’re tracking stock prices, monitoring live sports scores, or displaying GPS locations, you don’t need to process and transmit every intermediate update—just the most recent one. To help you minimize bandwidth usage, processing costs, and system load, we’ve introduced conflated subscriptions to Ably Pub/Sub.