Organizations building big data analytics solutions for streaming environments struggle with adapting legacy batch systems for streaming, supporting multiple columnar analytical databases, providing time series aggregations, and streaming Fact and Dimensional data into star schemas. In this session, you will learn how we overcame these challenges and developed an end-user self-service, no-code required “ETL” framework. Extensible and operationally robust, this developer framework includes a Spark Structured Streaming app for Kafka, Hadoop/Hive (ORC, Parquet), OpenTSDB/HBase, and Vertica data pipelines.