By Devika Gayapu, HEXstream data solutions engineer

Your applications generate a constant flood of data, from customer activity and outage data to IoT-sensor readings and website clicks. Before long, all that information starts piling up, and you need a way to store it that’s quick to access, easy to manage, and doesn’t eat up too much space.

This is where the choice between row-based and columnar-file formats really matters. Both hold the same data, but the way they organize that data can completely change how you store, query and analyze it.

Let’s explore…

Row-based storage

In row-based storage, data is saved record-by-record. Think of it like filling out a paper form for each person—writing their name, age and city together. The next person’s form starts right after the first one.

This method has been used for many years and works well for systems that handle lots of small, complete transactions such as processing payments or storing customer profiles.

The pros of row-based storage:

• Simple and fast when you need to read or write whole records.
• Works well for systems that deal with one record at a time.
• Easy to manage when applications always need the full record.

The cons of row-based storage:

• Slower for analytics because it reads all columns even if you need only one or two.
• Takes more space because mixed data types do not compress as well.

Columnar storage

Now imagine sorting all the forms by column instead. You keep all names together, all the ages together, and all the cities together. That is columnar storage. In this setup, data is grouped and stored by column, which is great for analytics because most queries look at only a few columns across many rows.

The pros of columnar storage:

• Much faster for analytics since it reads only the columns you need.
• Uses less space because similar data types compress better.
• Speeds up queries on large datasets by reducing how much data is scanned.

The cons of columnar storage:

• Slower for updates since one change can affect multiple column files.
• Not ideal when your system needs to frequently read or write full records.

Common big-data file formats

Here are three popular file formats and when to use them.

1. Avro (row-based): Avro stores data row-by-row. It is great for streaming data and real-time systems where you need to write full records quickly. It supports schema changes so you can later add or remove fields without issues. Avro is often used in data pipelines with tools like Kafka and Spark Streaming.

2. Parquet (columnar): Parquet stores data by column and is designed for analytics and reporting. It efficiently compresses data and reads only what is needed, which makes queries much faster. Parquet is widely used in tools like Spark, Hive and AWS Athena for analyzing large datasets.

3. ORC (Columnar): ORC also stores data by column and is optimized for Hadoop and Hive. It includes indexes and statistics that make queries even faster. ORC is useful when you work with large Hive tables or batch-processing jobs.

Which should you choose?

If your system writes or streams a lot of new data, Avro is a good choice because it handles fast-writes and allows flexible schema changes.

If your goal is to run analytics or reports on large amounts of data, Parquet works best because it compresses well and makes queries faster.

If your environment is based on Hive or Hadoop, ORC will give you better performance because it is built for those tools.

In summary, row-based formats like Avro are best when you need to frequently write or update complete records. Columnar formats like Parquet and ORC are better when you need to efficiently analyze large volumes of data.

The best choice depends on how you use your data. If you need faster writes, go with a row-based format. If you need faster reads for analytics, go with a columnar format. Choosing the right one will make your data systems faster, lighter and more cost-effective.

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