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MongoDB

nosql_database document_database big_data

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Master Schema Translations in the Era of Open Data Lake

Unity Catalog puts variety of schemas into a centralized repository, now the developer community wants more productivity and automation for schema inference, translation, evolution and optimization especially for the scenarios of ingestion and reverse-ETL with more code generations.Coinbase Data Platform attempts to pave a path with "Schemaster" to interact with data catalog with the (proposed) metadata model to make schema translation and evolution more manageable across some of the popular systems, such as Delta, Iceberg, Snowflake, Kafka, MongoDB, DynamoDB, Postgres...This Lighting Talk covers 4 areas: The complexity and caveats of schema differences among The proposed field-level metadata model, and 2 translation patterns: point-to-point vs hub-and-spoke Why Data Profiling be augmented to enhance schema understanding and translation Integrate it with Ingestion & Reverse-ETL in a Databricks-oriented eco system Takeaway: standardize schema lineage & translation

Lakeflow Declarative Pipelines Integrations and Interoperability: Get Data From — and to — Anywhere

This session is repeated.In this session, you will learn how to integrate Lakeflow Declarative Pipelines with external systems in order to ingest and send data virtually anywhere. Lakeflow Declarative Pipelines is most often used in ingestion and ETL into the Lakehouse. New Lakeflow Declarative Pipelines capabilities like the Lakeflow Declarative Pipelines Sinks API and added support for Python Data Source and ForEachBatch have opened up Lakeflow Declarative Pipelines to support almost any integration. This includes popular Apache Spark™ integrations like JDBC, Kafka, External and managed Delta tables, Azure CosmosDB, MongoDB and more.