What is Apache HCatalog?
Apache HCatalog is an open-source metadata and table management system designed specifically for Apache Hadoop. It provides a central repository for users to store metadata about their Hadoop data, such as data types, schema, and location, and enables the movement of data between different data processing platforms.
HCatalog provides a table abstraction layer for Hive, Pig, MapReduce, and other data processing frameworks, allowing users to access data from multiple data processing tools. It is written in Java and is compatible with all Hadoop distributions.
How Apache HCatalog works
HCatalog stores metadata about Hadoop data in a relational database, allowing users to search for data and view data lineage. It provides an API for managing the metadata and supports multiple partitioning schemes for efficient data access. HCatalog also supports multiple storage formats, including Apache Avro, Parquet, and ORC.
The metadata managed by HCatalog is stored in a central metastore, allowing users to access the metadata from multiple tools. HCatalog abstracts Hadoop storage formats, so users can access data without worrying about the format. HCatalog also provides a command-line interface and a web UI for managing the data and metadata.
Why Apache HCatalog is important
HCatalog provides a centralized metadata management system for Hadoop, simplifying the integration of data between different data processing platforms. HCatalog’s table abstraction layer and multiple partitioning schemes allow efficient data access from a variety of tools, increasing productivity. By abstracting Hadoop storage formats and providing a common metadata repository, HCatalog enables a seamless data integration between storage systems and data processing tools, simplifying the process of data processing and analytics.
Apache HCatalog also provides a consistent view of data across different processing platforms, enhancing data interoperability capabilities. It enables users to work with diverse data sources and formats, facilitating the movement of multi-format data between Hadoop and other data processing platforms. Consequently, it lowers the cost of data integration, simplifies data warehousing and ETL workloads, and increases agility.
The most important Apache HCatalog use cases
- Metadata management: Apache HCatalog offers a single system to store metadata about Hadoop data, including data types, schema, and location. This promotes easy data lineages and facilitates the management of large data pipelines.
- Data integration: Apache HCatalog enables the integration of data from different processing platforms, including Hive, Pig, and MapReduce, making it easier to centralize and process data.
- Data movement: With its support for multiple storage formats such as Avro, Parquet, and ORC, it makes it easier to move data between different storage systems.
- Business intelligence: Apache HCatalog can be integrated with popular business intelligence tools, making it easier to create reports and gain valuable insights from your data.
Other technologies or terms that are closely related to Apache HCatalog
- Apache Hive: Apache Hive is a data warehouse software that facilitates querying and data analysis of large datasets stored in Hadoop files. HCatalog provides Hive with metadata to enable easy access to data, and Hive provides an SQL-like language that can be used to query data stored in Hadoop.
- Apache Pig: Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs. HCatalog provides metadata to Pig to simplify access to data and make it easier to integrate data from different sources.
- Apache Avro: Apache Avro is a data serialization system that can be used for efficient data storage and exchange. HCatalog supports Avro as a storage format, making it easy to integrate Avro data with other data formats stored in Hadoop.
- Apache ORC: Apache ORC is a self-describing columnar storage format that is optimized for Hive queries. HCatalog supports ORC, making it easy to integrate ORC data with other data formats stored in Hadoop.
Why Dremio users would be interested in Apache HCatalog
Dremio users can benefit from Apache HCatalog’s metadata management capabilities, enabling them to access data from multiple processing platforms. As Dremio provides a virtualization layer over Hadoop and other data sources, Apache HCatalog can simplify data discovery and metadata integration. Additionally, Dremio supports multiple storage formats, allowing users to combine data from various data sources with different storage formats. Apache HCatalog can provide Dremio with metadata to support data management, governance, and integration capabilities.
Apache HCatalog can also help organizations optimize their data processing pipelines. By providing a consistent view of data across different processing platforms, Apache HCatalog simplifies integration and data transfer, enhancing data interoperability capabilities. This simplifies data warehousing and ETL workloads, lowers the cost of data integration, and increases agility—allowing organizations to become more data-driven.