People often associate streaming with high costs, complexity, and systems that must run continuously. However, Spark Streaming breaks this mold by offering a flexible, custom solution that processes data in real-time, near-real-time, or on any custom schedule—all without the burden of always-on operation or constant maintenance. In this session, Jitesh will reveal how Spark Streaming serves as a dynamic design pattern for data pipelines, making it easier to integrate into your data strategy without the traditional constraints of streaming technologies.

* Learn how Spark Streaming transforms the concept of streaming from an expensive, high-maintenance system to a versatile tool for any data processing interval.
* Discover its flexible design pattern that supports real-time, near-real-time, or scheduled data processing with a single codebase.
* Watch a live demo showcasing the ingestion of large-scale data—merging data into a table with billions of rows with a single machine.

I plan to share my computer screen and speak as part of the presentation. Therefore, I will have AV requirements.