Description
Streaming data pipelines can fail due to various reasons. Since the source data, such as Kafka topics, often have limited retention, prolonged job failures can lead to data loss. Thus, streaming jobs need to be backfillable at all times to prevent data loss in case of failures. One solution is to increase the source's retention so that backfilling is simply replaying source streams, but extending Kafka retention is very costly for Netflix's data sizes. Another solution is to utilize source data stored in DWH, commonly known as the Lambda architecture. However, this method introduces significant code duplication, as it requires engineers to maintain a separate equivalent batch job. At Netflix, we have created the Iceberg Source Connector to provide backfilling capabilities to Flink streaming applications. It allows Flink to stream data stored in Apache Iceberg while mirroring Kafka's ordering semantics, enabling us to backfill large-scale stateful Flink pipelines at low retention cost. Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram:…
Description from YouTube. Full content on the video page.
More from Databricks
NewsGovern MCP servers in Databricks #databricks #mcp #aigovernance
Databricks Unity AI Gateway now governs MCP servers, centralizing their management alongside built-in foundation models and LLMs. This integration allows for easier governance and orchestration of various AI components and agents within Databricks.
NewsHow Suntory Turns Data into Faster Decisions with Databricks
Suntory uses Databricks to integrate diverse datasets, including internal sales, macroeconomic factors, and consumer behavior, into "Project Brain" for faster decision-making and product launches. The company also implements an all-employee upskilling program, "Manabi no Michi," to empower its workforce to leverage AI for improved performance and efficiency.
NewsAIA Group x Databricks: Turning Regulated Data into Real-Time Intelligence
AIA Group leverages Databricks to manage regulated data across 18 markets, addressing challenges like data residency and varying tech maturity with features like Unity Catalog for governance. The platform enables real-time intelligence for investment decisions, fraud detection, and personalized agent coaching, with future plans for conversational analytics and autonomous AI.
TutorialsConnect Google Sheets to Databricks
The Databricks Google Sheets add-in allows users to explore, import, and refresh governed data from the Databricks Lakehouse directly within Google Sheets. It demonstrates how to browse Unity Catalog, select tables or metric views, apply filters, schedule data refreshes, and use direct SQL queries with parameters.
NewsNo More Table Locks for Multi Statement Transactions #databricks #dataengineering #sql
Databricks now supports multi-table transactions, allowing changes to multiple tables within a single atomic transaction that rolls back all changes if any part fails. This feature, managed by Unity Catalog, prevents table locking during updates and supports up to 100 tables per transaction using a simple "BEGIN ATOMIC...END" syntax.
NewsMay 2026 Databricks Updates: No Code ETL, New GPUs and Death of the Dashboard
Databricks announced several updates including AI Prep Search for document chunking and vector database preparation, SQL vector functions for embedding mathematics, and the general availability of multi-table transactions. They also introduced Lakeflow Designer for visual, no-code data pipeline creation and updated their serverless GPU offerings to include H100s.