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Learn how to use an ecosystem of data integration technologies to easily ingest data from applications, data stores, mainframes, files, and more into Delta Lake

Data teams are looking for the most complete and recent data possible for data science, machine learning, and business analytics, but it can be difficult to reliably load this data from hundreds of different sources into a centralized data lake. Delta Lake is quickly becoming the open-source standard for building fast and reliable data lakes at scale.

Learn how Databricks Ingest makes it easy to load into Delta Lake from various sources – applications like Salesforce, Marketo, Zendesk, SAP, and Google Analytics; databases like Kafka, Cassandra, Oracle, MySQL, and MongoDB, and file storage like Amazon S3, Azure Data Lake Storage, Google Cloud Storage. Capabilities include:

  • Data Ingestion Network: Leverage an ecosystem of partners like Fivetran, Qlik, Infoworks, Streamsets, and Syncsort to easily ingest data into Delta Lake from an easy to use partner gallery
  • Auto Loader: Ingest data continuously into your data lake from cloud storage like AWS S3 or Azure Data Lake Storage, ensuring data recency without any having to manually setup job triggers or scheduling