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From data lakes to user applications: How Bigtable works with Apache Iceberg
- The latest Bigtable Spark connector version offers enhanced support for Bigtable and Apache Iceberg, enabling direct interaction with operational data for various use cases.
- Users can leverage the Bigtable Spark connector to build data pipelines, support ML model training, ETL/ELT, and real-time dashboards accessing Bigtable data directly from Apache Spark.
- Integration with Apache Iceberg facilitates working with open table formats, optimizing queries and supporting dynamic column filtering.
- Through Data Boost, high-throughput read jobs can be executed on operational data without affecting Bigtable's performance.
- Use cases include accelerated data science by enabling data scientists to work on operational data within Apache Spark environments, and low-latency serving for real-time updates and serving predictions.
- The Bigtable Spark connector simplifies reading and writing data from Bigtable using Apache Spark, with the option to create new tables and perform batch mutations for higher throughput.
- Apache Iceberg's table format simplifies analytical data storage and sharing across engines like Apache Spark and BigQuery, complementing Bigtable's capabilities.
- Combining advanced analytics with both Bigtable and Iceberg enables powerful insights and machine learning models while ensuring high availability and real-time data access.
- User applications like fraud detection and predictive maintenance can benefit from utilizing Bigtable Spark connector in combination with Iceberg tables for efficient data processing.
- The integration of Bigtable, Apache Spark, and Iceberg allows for accelerated data processing, efficient data pipelines handling large workloads, and low-latency analytics for user-facing applications.
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