Real-time DataFrames | New features in Quix Streams 2.9.0

Quix Streams is a fast and general-purpose processing framework for streaming data. Build real-time applications and analytics systems on data streams using Python DataFrames and stateful operators, all without having to install a server-side engine. Familiar to anyone already working with DataFrames for ETL and ELT jobs, Quix Streams lets you build on your existing batch data processing skillset. Already using Quix Stream or currently developing solutions for batch data processing using Python? Join Tim, as he walks you through the exciting new features in Quix Streams 2.9.0, THE alternative to Kafka Python for anyone creating data processing workflows. It’s an easy-to-run, no JVM alternative to Faust, Apache Beam, and Spark. You can leverage your existing knowledge and tools while enhancing your data workflows, making it easier to handle large datasets efficiently. Don't forget to subscribe for more updates and tutorials on the Quix Streams Python library! Check out Quix Streams on GitHub https://github.com/quixio/quix-streams -- 0:00 Introduction & Overview of Quix Streams 2.9.0 0:26 Optional Installs 2:07 Serializers 4:16 Sinks 8:47 Commit Every 10:13 Drop Ignore 11:05 End