Introduction

At Bobsled we're building a cross-cloud data sharing platform. One of the things that have been brought up in discussions is the big data file formats.

There are times when I ask my questions right away, and times when I take notes about things I want to dig deeper into and do more research about. Big data file formats were one of them.

These are file formats companies/organizations use to analyze large datasets and extract insights from them.

Big data file formats

Big data file formats are file formats that are specifically designed to store and efficiently process large amounts of data. These file formats are necessary because traditional file formats like CSV and JSON are not well-suited for dealing with big data. This is because traditional file formats are not very efficient in terms of storage, and they do not provide good support for parallel processing, which is important when working with large datasets.

Big data file formats, on the other hand, are designed to be highly efficient in terms of storage and support parallel processing. This allows them to handle large amounts of data without running into performance issues.

On top of that, many big data file formats are columnar, which means that they store data in columns rather than rows. This allows for faster query processing because only the relevant columns need to be read, rather than the entire dataset.

These file formats are necessary to work with large datasets, it would be much more difficult to store and process big data without them.

Without these formats, organizations would be prevented from extracting insights from their data.

Parquet

Parquet is a columnar file format, which means that it stores data for each column in a given row, rather than storing the entire row. It is used for storing large amounts of data and is designed to be efficient in terms of both storage and processing.

Upsides

• Highly efficient in terms of storage, because it only stores data for each column in a given row.

• Highly efficient in terms of processing, because it can be easily read and processed in parallel.

Downsides

• Does not support data schema. therefore it is not well-suited for working with structured data.

• Limited support for data indexing compared to ORC. If you need to index the data in order to improve the performance of some queries, it may not be the best choice.

• Limited support for data compression compared to ORC. This means that if you need to compress the data in order to reduce storage requirements, parquet may not be the best choice.

Befitting

Examples of when it is befitting to use Parquet files:

• When working with large datasets that are too big to fit on a single machine.

• When the data is structured in a way that makes it easy to store and process in a columnar format.

• When the data needs to be accessed and processed quickly, using distributed processing systems.

Avro

Avro is a row-based file format. It is often used for storing and transmitting large amounts of data in a distributed manner. It is a compact and efficient file format.

Upsides

• Compact and efficient file format, which is well-suited for storing and transmitting large amounts of data.

• Supports data schema, which allows data to be stored and processed in a structured manner.

Downsides

• Does not support data schema, which means that data stored in Avro files is not structured in a well-defined way.

• Is not a human-readable format, which means that you need specialized tools and libraries to work with it.

• Is a row-based storage format, which means that it may not be as efficient in terms of storage space as columnar storage formats.

Befitting

You don't always want to use Parquet files. Here are some cases when you'd want to prefer Avro files over Parquet ones:

• When the data has a well-defined schema and needs to be stored and processed in a structured way. Because Avro supports data schema, it is better suited for this type of scenario than parquet, which does not support schema.

• When the data is not well-suited to a columnar storage format. Because Avro is a row-based storage format, it may be a better choice for data that is not easily stored and processed in a columnar format, such as data with a complex structure or data that requires accessing a large number of columns for each row.

ORC (Optimized Row Columnar)

Like Parquet, ORC (Optimized Row Columnar) is a columnar file format, which means that it stores data for each column in a given row, rather than storing the entire row. This makes them more space-efficient than row-based storage formats like Avro, and also allows them to be easily read and processed in parallel.

However, Parquet and ORC do have differences.

Differences to Parquet

• ORC supports data schema and data indexing. This means it is better for working with structured data and for improving the performance of certain types of queries.

• ORC supports data compression. This means it can be used to further reduce the amount of storage space required for large datasets, which can be useful in certain situations.

When to use Parquet files

• If efficient storage and processing are the primary concerns, Parquet is a good choice since it is highly efficient in terms of both storage and processing.

• If the data does not have a well-defined schema, or when data schema is not a concern, Parquet is a good choice because it does not support data schema. While ORC is better for when working with structured data.

Conclusion

In conclusion, there are different types of big data file formats. Big data file formats are necessary for organizations to extract insights from large datasets (big data).