Systems | Development | Analytics | API | Testing

Any organization with an online presence can unleash a treasure trove of valuable insights with text-based sentiment analytics. But getting to those insights with a traditional data stack can prove daunting, if not impossible. By combing Live Analytics from ThoughtSpot with text data in Snowflake and the ability to execute custom analytics code with Snowflake Snowpark, sentiment analysis is just a live query away.

In this new series from Sauce Labs, Marcus Merrell addresses ten things he wishes CIOs and CTOs understood about testing. In episode ten, Marcus wraps up the series by reiterating why we test in the first place, to instill digital confidence...and who is responsible for delivering that quality user experience? Everyone. Come along on this ten-episode journey to learn some best practices while Marcus dispels some myths about the testing space.

The Data Chief Live: Designing your winning data strategy Join Maria Villar, Head of Enterprise Data Strategy and Transformation at SAP, Samir Sharma, CEO and Founder at datazuum, and ThoughtSpot Chief Data Strategy Officer, Cindi Howson, to kick start your year by designing a winning data strategy. Hear data leaders in this space share their experience and tips such as.

Ably and Kafka are complementary solutions Both Ably and Kafka are event-driven solutions, sharing similar guarantees, messaging semantics, and characteristics. With Ably as a broker in the middle, you can seamlessly and scalably distribute Kafka data to web, mobile, and IoT clients at the edge.

In Part 1 of the Automation Best Practices with Java workshop, Nikolay introduces the workshop content, reviews setup instructions, and discusses testing strategy for a web application.

In this session, Yaron Haviv, Iguazio's Co-Founder and CTO, discussed how to enable continuous delivery of machine learning to production using Git-based ML pipelines (Github Actions) with hosted training and model serving environments. He touched upon how to leverage Git to solve rigorous MLOps needs: automating workflows, reviewing models, storing versioned models as artifacts, and running CI/CD for ML. He also covered how to enable controlled collaboration across ML teams using Git review processes and how to implement an MLOps solution based on available open-source tools and hosted ML platforms. The session includes a live demo.