Keeping the data lake stocked with complete, current and high quality data is often the first problem the enterprise encounters with its brand new Hadoop or Spark cluster. Incomplete, inaccurate or late data leads to false positives, missed insights and negative business impact. To address data ingestion, you must solve for three things: the growing variety of data sources, the need to ingest continuously to meet real-time demands, and the insidious problem of data drift—unexpected changes to schema or semantics—that silently corrodes data quality.
In this webinar, we will show you how to take a structured approach to big data ingestion that solves these problems and ensures your architecture will thrive over the long-term. Drawing from real-world enterprise examples you will learn how to implement an efficient and effective operation built on top of a reliable, continuous and fully automated data ingestion infrastructure.