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Organizations have a wealth of information siloed in various data sources. It could be relational databases, on-premises data warehouses, big data storage systems like Hadoop, ERP/CRM systems, or real-time streaming sources. Many analytics use cases require not only efficient processing of this data but also a unified approach to produce meaningful reports and predictions. To start this journey, organizations need to ingest data from different sources into a single location. In this chapter, we will look at how to ingest data from various sources incrementally and efficiently, using Lakeflow Connect and other techniques, along with failover, into your Delta Lake.

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Lakeflow Connect: Data Ingestion for the Lakehouse

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Jason Yip,
  • Nikhil Gupta,
  • Marcin Wojtyczka

摘要

Organizations have a wealth of information siloed in various data sources. It could be relational databases, on-premises data warehouses, big data storage systems like Hadoop, ERP/CRM systems, or real-time streaming sources. Many analytics use cases require not only efficient processing of this data but also a unified approach to produce meaningful reports and predictions. To start this journey, organizations need to ingest data from different sources into a single location. In this chapter, we will look at how to ingest data from various sources incrementally and efficiently, using Lakeflow Connect and other techniques, along with failover, into your Delta Lake.