There are several columnar data formats that are popular, including:
- Apache Parquet: An open-source columnar storage format that is widely used in the Hadoop ecosystem. It is efficient for storing and processing large amounts of data and supports compression and data splittability.
- Apache ORC: Another open-source columnar storage format that is designed for high-performance and is commonly used in the Hadoop ecosystem. It supports efficient compression and data splittability.
- Apache Arrow: An in-memory columnar data format that is designed for high-performance data processing. It is widely used in big data and analytics systems.
- Delta Lake: An open-source columnar storage format that is built on top of Apache Parquet. It adds features such as data versioning, transactions, and time travel to Parquet, making it easier to build reliable data pipelines.
- Cloud Data Fusion: A cloud-based data integration platform that supports columnar data formats like Apache Avro and Apache Parquet. It is designed for building and managing data pipelines in the cloud.
Both Apache Parquet and Apache ORC (Optimized Row Columnar) are open-source columnar data storage formats that are designed for efficient storage and processing of large amounts of data. They both support compression and data splittability, which can help reduce the amount of disk space required to store data and improve the performance of data processing jobs.
One key difference between the two formats is the way they store data. Parquet stores data in a column-wise fashion, while ORC stores data in a combination of rows and columns. This can make ORC slightly more efficient for certain types of queries, as it may require less I/O to read the necessary data.
Another difference is that ORC includes support for indexing, which can make it faster to retrieve specific rows of data. However, this also means that ORC files are generally larger than Parquet files, as they include the index data in addition to the data itself.
In general, both Parquet and ORC are designed for efficient storage and processing of large amounts of data, and both support compression and data splittability to help improve performance. Parquet is generally better suited for columnar-based queries, such as aggregations, while ORC is generally better suited for row-based queries and includes support for indexing to further improve performance.
