Did you know that Apache Parquet, Apache Avro, and Apache ORC (Optimized Row Columnar) are three popular storage formats used in big data and analytics? Each format has its own special features and is optimized for different use cases. Cool, right?

I’ve been playing with some data formats lately, and reading about Apache has made me look a little further into some data storage formats. Here are some of my takeaways.

Parquet is a storage format that is great for reading lots of data at once. It stores data in columns, which makes it fast to find and read only the data you need. This helps make queries faster and reduces the amount of data that needs to be read.

Parquet can use different methods to compress the data, like Snappy, Gzip, and Brotli, which gives flexibility in choosing the best compression. But, changing the structure of the data in Parquet can be complicated and needs to be done carefully to make sure the data stays accurate.

A columnar storage format available to any project in the Hadoop ecosystem, regardless of choice of data processing framework, data model, or programming language

Avro is a type of storage format that’s great for tasks that involve a lot of writing and organizing data. It’s perfect for situations where you need to quickly write and then read through lots of information in order, like when you’re using Apache Kafka.

Avro also has a cool feature that lets it handle changes in the way data is organized without any problems. This means that even if the way we organize the data changes, we can still understand it. However, when it comes to tasks that involve a lot of reading and analyzing data, Avro is not the best choice as it’s not very efficient for those kinds of tasks.

ORC is a type of storage format made for fast data analysis. It uses special compression to save space and has built-in support for different types of compression. ORC can index data and read only the needed columns, which makes queries faster.

It works well for data warehousing and ETL processes because it handles complex data types and provides high query performance. Although ORC can handle changes to data structure, it’s not as flexible as Avro in dealing with schema changes.

In short, it seems like to me that Parquet works best for tasks that involve reading a lot of data for analysis because of how it stores data and compresses it efficiently. Compressed data is always good for reading.

Avro is great for tasks involving a lot of writing and data organization, offering flexibility with data formats and easy data description.

Similarly, ORC is well-suited for fast analytical queries, with strong compression and indexing capabilities, making it a good fit for tasks like data warehousing and ETL processes.

Each format has its own strengths and weaknesses, so the choice between them depends on what you need for your specific task.

I hope you enjoyed the reading just like I did researching and writing. Let me know in the comments if you have any questions.