Parquet File Format: What is it?

Parquet file format is a popular column-oriented data format known for its efficient storage of large datasets and its support for fast analytical queries. On the other hand, PostgreSQL is a powerful relational database system capable of handling complex datasets and transactions. In this blog post, we will demonstrate how to import Parquet files into PostgreSQL using Python, with the assistance of the awswrangler library.

What is Parquet File Format?

Parquet is an open-source columnar file format for big data analytics. Apache Software Foundation developed the Parquet File Format as part of the Hadoop ecosystem. Parquet stores data in columns, instead of rows, which allows for more efficient compression and encoding. This also supports nested and repeated data structures, as well as schema evolution and metadata.Big data applications, such as Spark, Hive, Impala, and Presto used Parquet file widely, because they can significantly improve the performance and scalability of analytical queries. Many other tools and languages, including R, Python, Go, and Java also reads Parquet files as efficient storage format.

Here are some of the key benefits of using Parquet file format – everything you need to know:

• Efficient compression and encoding: Parquet supports a variety of compression and encoding techniques, which can significantly reduce the size of data files. This can improve storage efficiency and reduce network bandwidth requirements.
• Columnar storage: Parquet stores data in columns, rather than rows. This allows for faster queries, as only the necessary columns need to be read from disk.
• Nested data support: Parquet supports nested and repeated data structures, which are common in big data applications.
• Schema evolution and metadata: Parquet supports schema evolution and metadata, which makes it easy to change the data schema and add new information to data files without having to rewrite the entire file.

The Perfect File Format Unveiled: Parquet vs. CSV

CSV and Parquet are two popular data formats for storing and querying tabular data. CSV is a simple and ubiquitous format, while Parquet is a columnar storage format designed for large and complex data sets.

Advantages of CSV:

• Easy to create, read, and debug
• Supported by many tools and frameworks

Disadvantages of CSV:

• Does not support complex data types or nested structures
• Does not preserve the schema or data types of the columns
• Does not support efficient compression or encoding schemes
• Does not allow skipping irrelevant data when querying

Advantages of Parquet:

• Supports complex data types and nested structures
• Stores the schema and data types of the columns within the file
• Uses efficient compression and encoding schemes
• Allows skipping irrelevant data when querying

Performance and cost

Apache Parquet file format can be up to 10 times smaller than CSV files, and up to 2 times faster to read and write. Parquet files are also compatible with many data processing frameworks and query services.

Parquet files are a better choice than CSV files for storing and querying large data sets, as they offer higher efficiency, lower cost, and greater flexibility. However, CSV files are still useful for smaller or simpler data sets, or for applications that require wide compatibility and easy debugging.

What is postgresql database?

PostgreSQL is a powerful, open-source relational database management system (RDBMS) that supports both SQL and JSON querying. Ihis is a reliable, robust, extensible, and scalable database. PostgreSQL can handle a wide range of data types, including text, numbers, dates, arrays, geometric shapes, JSON, XML, and binary data. It also supports advanced features, such as table inheritance, partitioning, foreign data wrappers, triggers, stored procedures, and full-text search.

PostgreSQL is widely used as a backend database for web applications, mobile applications, and analytics applications. It is also a popular choice for data warehousing and machine learning applications. PostgreSQL can run on a variety of operating systems, including Linux, Windows, macOS, and BSD.

Why Import Parquet File Format to PostgreSQL?

There are many reasons why you might want to import Parquet files to PostgreSQL, including:

• Performance and scalability: Parquet files are designed for analytical queries, and they can significantly improve the performance and scalability of your PostgreSQL database.
• Flexibility and functionality: PostgreSQL is a feature-rich relational database management system (RDBMS) that offers a wide range of functionality, including ACID transactions, foreign keys, and complex queries.
• Cloud storage integration: Parquet files are often stored in cloud storage services, such as Amazon S3 or Azure Blob Storage. PostgreSQL supports foreign data wrappers (FDWs) that allow you to query Parquet files directly from your PostgreSQL database, without having to download them to your local machine.
• Big data integration: Parquet files are commonly used in big data frameworks, such as Spark and Hive. PostgreSQL supports a variety of tools and libraries that make it easy to import Parquet files from big data frameworks.
• Long-term retention and compliance: PostgreSQL is a reliable and durable database that is well-suited for storing historical and archival data. Parquet files are also designed for long-term retention, as they support efficient compression and encoding.

Conclusion

In this blog post, we showed you how to import parquet files to PostgreSQL using Python and awswrangler. We explained what are parquet and PostgreSQL, why you might want to import parquet files to PostgreSQL, and how to do it with a few lines of code.

You can check our article to know how to read parquet file in Python using diffrent modules like Pandas, PyArrow, FastParquet and PySpark.

We hope you found this post useful and informative. If you have any questions or feedback, please leave a comment below.