Apache Spark: The new enterprise backbone for ETL, batch and real-time streaming

Despite investments in big data lakes, there is widespread use of expensive proprietary products for data ingestion, integration, and transformation (ETL) while bringing and processing data on the lake.

However, enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can handle end-to-end needs for data processing, analytics, and machine learning workloads.

In this webinar, we will discuss why Apache Spark is a one stop shop for all data processing needs. We will also demo how a visual framework on top of Apache Spark makes it much more viable.

The following scenarios will be covered:


  • Data quality and ETL with Apache Spark using pre-built operators

  • Advanced monitoring of Spark pipelines

On Cloud

  • Visual interactive development of Apache Spark Structured Streaming pipelines

  • IoT use case with event-time, late-arrival and watermarks

  • Python based predictive analytics running on Spark


Recent Posts

View more posts


50X faster time to value with Confluent and Gathr...


Data + AI Summit 2023: A must-attend for data scientists,...


Move away from batch ETL with next-gen Change Data Capture


ETL vs ELT: Which data integration practice is right for...