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Parquet Tools

Apache Parquet is a columnar storage format for big data processing systems. It is designed for efficient storage and fast data access, and can handle complex data structures. Parquet is widely used in distributed data processing environments such as Hadoop and Spark.

Both Apache Parquet and Avro are columnar data storage formats for big data processing systems, but they have different design goals. Parquet is optimized for efficient query performance and supports advanced data types and nested structures, while Avro is optimized for data serialization and supports schema evolution. Additionally, Parquet is often used with distributed query engines like Apache Spark, while Avro is often used for data serialization in messaging systems.

PARQUET TOOLS FUNCTIONALITY

data lakes

Parquet tools are a collection of command-line utilities that allow users to inspect and manipulate data stored in Parquet files. They include tools for viewing metadata, schema, and statistics, as well as converting between Parquet and other data formats. Parquet tools are part of the Apache Parquet project and are often used in conjunction with other big data processing tools.

PARQUET AND SNOWFLAKE

Snowflake natively supports Apache Parquet format for data storage and querying, allowing users to create tables directly from Parquet files or load Parquet data into existing tables. Snowflake also supports automatic schema detection for Parquet data and provides advanced query optimization techniques for efficient processing of Parquet data. Additionally, Snowflake offers seamless integration with Parquet data stored in external data lakes or cloud storage services.

VIRTUAL HANDS-ON LAB: SNOWFLAKE FOR DATA LAKES IN 90 MINUTES

This hands-on workshop focuses on how Snowflake can be used for data lake workloads, which includes detecting the schema and analyzing files in Parquet format.