Systems | Development | Analytics | API | Testing

This session will provide a front-row seat during a live build using Application Automation and Qlik Cloud. Here, users will learn from Qlik’s very own, Adam Cooke and Emile Koslowski, leaders of the Application Automation team who will share best practices, techniques, and debugging through different scenarios.

What’s an easy, low-cost way to batch stream data into Snowflake in near real-time? In this episode of Snowflake Bytes, Felipe Hoffa uses RSVP data from Meetup to demonstrate how you can use Google Cloud Pub/Sub and Snowpipe to do just that. Meetup publishes about 4 thousand RSVPs for events per hour that it can use to analyze what’s happening around the world. In the video, you’ll find a link to Hoffa’s blog post, which shares the code he used to create a lightweight pipeline to automatically ingest each of these files into Snowflake so that they can be queried.

On their data analytics platform Uniper is practicing self service. This video explains why and how they’re enabling the governed self service with Talend.

See how easy it is to create a period analysis dashboard with Insight Advisor Analysis Types Smart Sheets.

Tech Talks by Kong are a new webinar series dedicated to our technical developer audience featuring open-source products and topics relevant to you with extended live demos.

Unraveling AI Entrepreneurs Tips is a new video series brought to you by Sandeep Uttamchandani, Chief Product Officer, for engineers and product managers. In Unraveling AI Entrepreneurs episode #7, Sandeep shares ways to report on the progress of your AI project.

What are the top three mega trends for data leaders this year (and beyond)? In this episode, we tackle cloud data platforms, the five sub disciplines of observability, and real-time machine learning. Listen to our conversation with Kevin Petrie, Vice President of Research at Eckerson Group. Hear the Answers to these Questions: Why a cloud data platform is a common destination with many routes? Which tools to standardize the different classes of observability? How the interrelationship between model observability and ML works?