Data can be broadly categorized into three types based on its structure:
Unstructured Data: This includes formats like CSV, TXT which don't have a specific structure or schema.
Semi-Structured Data: Data formats like XML and JSON fall into this category. They have a flexible schema but still maintain some level of structure.
Structured Data: Formats like Avro and Parquet are considered structured as they have a defined schema.
Parquet is a popular choice for big data processing tasks, and here's why:
Binary Format with Efficient Compression: Parquet is a binary file format. By default, it uses the Snappy compression algorithm when used with Apache Spark, which provides a good balance between compression ratio and speed.
Columnar Storage: Unlike row-based files, Parquet is columnar. This means it supports column pruning and predicate pushdown, which can significantly speed up queries.
Optimized for WORM: Parquet is optimized for Write Once, Read Many (WORM) operations. This makes it a preferred choice for data lakes and big data processing tasks.
When working with Parquet in big data systems like Spark, choosing the right partition column is crucial. Here are some guidelines:
Avoid High Cardinality Columns: If a column has a lot of unique values, it will create numerous directories. This leads to more overhead during parsing.
Filterable Columns: Choose columns that are frequently used as filters in queries. For example, if you often filter data by a specific "date" or "region", those might be good partitioning candidates.
Low Cardinality Columns: Columns with low cardinality create a small number of directories but each directory contains a large amount of data. This can optimize read operations.
Trial and Error: Sometimes, the best way to determine the optimal partition column is through experimentation. Monitor the performance of your queries and adjust accordingly.
Ensure you have the necessary libraries:
pip install pyspark pandas pyarrow
Benchmarking with Spark
Output is