Executing analytical queries on massive data volumes with traditional databases and batch ETL processes is complex, expensive, and time-consuming.
Open-source distributed computing technologies like Apache Spark and Hadoop provide an efficient and cost-effective data processing paradigm. Apache Spark enables end-to-end ETL workflows for incessant data streaming from heterogeneous sources and overcomes the constraints imposed by legacy ETL processes. Explore how Apache Spark provides a powerful and efficient approach to ETL. Join our upcoming webinar where experts at Impetus talk about:
Senior Solutions Architect, Impetus StreamAnalytix
Senior Product Engineer, StreamAnalytix