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Introduction to Apache Iceberg
- Apache Iceberg is an open-source table format for huge analytic datasets. It provides capabilities like schema evolution, ACID transactions, etc.
- Iceberg is designed to work efficiently with big data engines like Apache Spark, Trino, Hive, Presto, and others.
- Apache Iceberg was originally developed at Netflix to address limitations in Hive tables for big data processing.
- The key motivations for creating Iceberg include scalability issues with Hive Metastore, inflexible schema evolution, and a lack of ACID compliance.
- Iceberg addressed these problems by providing a flexible table format, efficient metadata management, and full support for ACID transactions.
- The architecture of Apache Iceberg consists of three main components: Metadata Layer, Data Layer, and Manifests.
- Apache Iceberg provides a range of features that make it a robust choice for modern data lake management, including ACID Transactions, Schema Evolution, Time Travel, Partition Evolution, etc.
- The code example shared demonstrates how to use Apache Iceberg with a Hadoop catalog to create a table programmatically and perform CRUD (Create, Read, Update, Delete) operations.
- Apache Iceberg provides a robust and scalable solution for managing large datasets with advanced capabilities.
- Using Iceberg with Java and other big data engines offer flexibility, performance, and reliability in handling data-intensive applications.
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