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Apache Parquet is an open-source columnar storage format for data analytics that is designed for optimal performance and efficient use of system resources. It is a compressed binary file format that stores data in a columnar fashion, as opposed to the row-based approach used by many traditional storage formats. This makes it an ideal choice for use cases where the focus is on analytics and processing of large datasets because it enables faster query execution and reduces the amount of I/O operations required to access data.
The architecture of Apache Parquet is based on the concept of dividing a large dataset into smaller pieces or chunks, which are then compressed and stored in a columnar format. Each column is encoded separately, resulting in a highly efficient and optimal use of storage space. This approach also allows for improved data compression and better query performance, as only the required columns are read from disk, and not the entire dataset.
Apache Parquet has become an essential tool for data processing and analytics because of its numerous benefits, including:
Apache Parquet is used in many data processing and analytics use cases, including:
There are several technologies and terms related to Apache Parquet that are important to understand, such as:
Apache Parquet is a highly optimized format for data processing and analytics, making it an ideal choice for Dremio users who are looking to improve the performance and efficiency of their data processing and analytics workflows. Dremio users can use Apache Parquet to store and process large datasets efficiently and cost-effectively, reducing storage and processing costs and enabling faster query execution. Additionally, Apache Parquet works well with other Dremio features such as fast data lake scans and column pruning, making it a valuable addition to any Dremio user's toolkit.