Having established itself as a provider of continuous data ingestion and management software to support agile data operations, StreamSets has closely aligned itself with the DataOps movement since 451 Research last update. The two are a natural fit, given that DataOps seeks to bring the agility and automation advantages of DevOps to data operations. The latest update to the rebranded StreamSets DataOps Platform is Transformer, a native execution engine based on Apache Spark that boosts StreamSets' ability to perform both batch and streaming data transformations in support of ETL (extract, transform and load) and machine learning workloads. Download this report to learn how StreamSets transforms its DataOps platform with Spark-based execution.

Apache Kafka is a scalable and fault tolerant messaging system common in publish and subscribe (pub/sub) architectures. Apache Kafka is used for a range of use cases including message bus modernization, microservices architectures and ETL over streaming data. High throughput -- Each server is capable of handling 100s MB/sec of data. High availability -- Data can be stored redundantly in multiple servers and can survive individual server failure. High scalability -- New servers can be added over time to scale out the system.

In today's world, there isn't a single IT organization that can control and engineer all aspects of the data that their enterprise needs. Data is now the connective tissue upon which complex enterprise logic is being built, spanning numerous applications. DataOps enables faster delivery of existing and new data services and products in the face of changing environments, requirements, infrastructures and semantics while preventing data threats. DataOps enables applications to be good citizens of the ecosystem by respecting the implied contracts between them despite unexpected data drift that emerges from changing technologies. Download the exclusive white paper and see how companies plan for organizational, process, and technology shifts to deliver on the promise of DataOps.