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Managing data ingestion is a serious challenge as the variety of sources and processing platforms expands while the demand for immediately consumable data is unceasing.

Because modern data is so dynamic, dealing with data in motion is not just a design time problem for developers, but is also a runtime problem requiring an operational perspective that must be managed day-to-day and evolved over time. In this new world, organizations must architect for change and continually monitor and tune the performance of their data movement system.

StreamSets, the provider of the industry’s first data operations platform, offers the following 12 best practices as practical advice to help you manage the performance of data movement as a system and elicit maximum value from your data.

This data ingestion best practices can help you:

  • Reduce time required to develop and implement pipelines
  • Create more reliable data movement architectures
  • Elegantly handle data drift (schematic or semantic surprises)
  • Continually manage dataflow performance




About StreamSets

Big data doesn't need to be hard. Whether using Apache Hadoop, Spark or Kafka, leading companies are leveraging StreamSets to streamline their big data journey and deliver success. StreamSets focuses on simplifying the process of building, executing and operating dataflow pipelines. The StreamSets platform combines award-winning open source software for the development of any-to-any dataflows that uniquely handle data drift with a cloud-native control plane that centralizes building, executing and operating dataflow topologies at enterprise scale. Whether you're just starting with big data, or consider yourself an expert, StreamSets can help extend the value of your deployment to deliver greater results for your business.



 
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