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Parquet File Format – Everything You Need to Know!
- With the exponential growth of data, finding optimal ways to store various data flavors has become a significant challenge.
- Apache Parquet, existing since 2013, addresses the need for analyzing raw data efficiently.
- Parquet is preferred due to features like data compression, columnar storage, language agnosticism, open-source format, and support for complex data types.
- Comparing row-store and column-store approaches, Parquet's columnar storage enhances analytical query performance by scanning only necessary columns.
- Parquet introduces row groups, optimizing projection and predicate operations in OLAP scenarios.
- Metadata in Parquet files aids in improving query performance by providing essential data about data.
- Parquet enhances performance by skipping unnecessary data structures and applying compression algorithms like dictionary encoding and Run-Length-Encoding.
- Delta Lake, an enhanced version of Parquet, offers features like ACID compliance, time travel, and DML statements for advanced data management.
- Parquet file format stands out as an efficient storage option in the evolving data landscape, balancing memory consumption and query processing efficiency.
- Overall, Parquet's benefits make it a powerful choice for organizations dealing with diverse big data requirements.
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