Avro vs Parquet overview

Avro vs Parquet

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Avro and Parquet: Big Data File Formats

Avro and Parquet are both popular big data file formats that are well-supported. Before we dig into the details of Avro and Parquet, here’s a broad overview of each format and their differences.

Parquet

Similar to ORC, another big data file format, Parquet also uses a columnar approach to data storage. Parquet sets itself apart in its support of nested data structures and its many options for data compression and encoding. arquet offers very efficient data compression that allows for economical storage of very large amounts of data.

Avro

Avro uses row-based storage configuration and trades compression efficiency for condensed binary format to reduce data storage needs compared to ORC and Parquet. Avro uses JSON for defining data types and protocols so it’s easy to read and interpret.

Benefits of Using Big Data File Formats

Big data file formats make it possible to store, access, and manage the massive data sets used in a variety of data analytics applications. Here’s how both Avro and Parquet optimize data management. 

More efficient data storage

One of the most valuable benefits of big data file formats is their ability to reduce file sizes significantly using highly efficient data compression techniques, making it possible to store more data using less space. Reducing the amount of space required for storage helps organizations trim their cloud storage costs without sacrificing the value that can be realized from archived data.

Support for schema evolution 

Schema evolution is a feature used to accommodate data as it changes over time. In a dataset, schemas are the column headers and types. Schema evolution enables users to automatically adapt the scheme to add additional columns using an append or overwrite operation. 

Faster analytics workloads

Big data file formats are ideal for boosting the speed and efficiency of data analytics and data wrangling tasks. With more compact storage, data can be queried more efficiently, allowing data analytics workloads to be completed much more quickly with less I/O usage. 

Splittable file formats

As the name implies, splittable files allow individual files to be split apart, allowing processing to be spread between more than one worker node. This results in improvements in disk usage and processing speed.

Arvo vs. Parquet

Depending on the use case, Arvo and Parquet each offer unique advantages over the other. Here are the key differentiators that may tip the scale in one direction or another in an organization’s Avro vs. Parquet decision. 

Avro

First released in 2009, Avro was developed within Apache’s Hadoop architecture. It uses JSON data for defining data types and schemas.

Benefits of using Avro:

• Data definitions are stored within JSON, allowing data to be easily read and interpreted.

• Avro is 100% schema-dependent with data and schema stored together in the same file or message, allowing data to be sent to any destination or processed by any program.

• Avro supports data schemas as they change over time, accommodating changes like missing, added, and changed fields.

• Avro does not require a coding generator. Data stored in Arvo is shareable between programs even when they’re not using the same language. 

Where Avro has the edge:

• Avro offers more highly developed options for schema evolution.

• Avro is more efficient for use with write-intensive, big data operations.

• Row-based storage makes Avro the better choice when all fields need to be accessed.

• Language-independent format is ideal when data is being shared across multiple apps using different languages.

Parquet

Originally developed by Cloudera in partnership with Twitter, Parquet is highly integrated with Apache Spark, serving as the default file format for this popular data processing framework.

Benefits of Parquet:

• Parquet supports complex nested data structures in a flat columnar format.

• Parquet ccommodates all big data formats including structured data, semi-structured, and unstructured data.

• Because it uses data skipping to locate specific column values without reading all of the data in the row, Parquet enables high rates of data throughput.

Where Parquet has the edge:

• Parquet offers numerous data storage optimizations.

• Parquet is more efficient at data reads and analytical querying.

• Parquet is an good choice for storing nested data.

• Parquet compresses data more efficiently.

• If using Apache Spark, Parquet offers a seamless experience.