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The emergence of big data has created tremendous opportunities for businesses to gain real-time insights and make more informed decisions by leveraging data from the exploding number of digital systems they have in use. However, as often is the case with disruptive technologies, the innovations behind big data have created a critical problem: one that we call data drift. Data drift creates serious challenges for businesses looking to fully harness the insights available from big data.

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.

In this white paper we will discuss the causes of data drift, its technical and business implications, and how StreamSets Data Collector provides an open source solution to address data drift. Specifically, it will provide best practices for data ingestion that can help you:

  • Reduce time required to develop and implement pipelines
  • Create more reliable data movement architectures
  • Elegantly handle data drift (schematic and 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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