Introduction to Data Engineering Concepts | Storage Formats and Compression

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When working with large-scale data systems, it’s not just what data you store that matters—it’s how you store it. The choice of storage format and compression strategy can make a significant difference in performance, cost, and usability. These decisions affect how quickly you can query data, how much storage space you need, and even how compatible your data is with various processing tools.

In this post, we’ll explore the most common data storage formats, the role of compression, and how these choices impact modern data engineering workflows.

Why Storage Format Matters

Raw data often arrives in simple formats like CSV or JSON, and for small volumes, these formats work just fine. But as data grows into gigabytes or terabytes, inefficiencies start to show.

Text-based formats like CSV are easy to read and parse, but they lack schema enforcement, are verbose, and are slow to process in distributed systems. JSON adds some flexibility by allowing nested structures, but it can still be quite large and inefficient when stored at scale.

Columnar formats, by contrast, are designed for analytics. Instead of storing data row by row, they store values column by column. This layout enables faster queries and better compression—especially for workloads that scan only a few columns at a time.

Imagine a table with hundreds of columns, but your query only needs five. With a row-based format, the system must read everything. With a columnar format, it reads just what’s needed. This is a game-changer for performance and cost in systems like data lakes and warehouses.

Common Formats in Practice

Several formats are widely used in data engineering, each with trade-offs.

CSV remains popular due to its simplicity and universal support. But it lacks strong typing and is prone to edge-case issues, such as inconsistent delimiters or quoting problems. It’s best used for small datasets or temporary interoperability.

JSON and XML are useful for semi-structured data. JSON, in particular, is common in APIs and logs. However, it’s not space-efficient and can be slow to parse at scale.

Parquet is a columnar format developed by Apache. It’s optimized for big data workloads and supports advanced features like nested schemas and predicate pushdown. Parquet is well-supported across tools like Spark, Hive, Dremio, and data warehouses like BigQuery and Snowflake.

Avro is a row-based format with support for schema evolution. It’s often used in streaming applications and data serialization. While it’s not as query-efficient as Parquet, it excels in write-heavy and messaging scenarios.

ORC (Optimized Row Columnar) is similar to Parquet but originally developed for the Hadoop ecosystem. It offers strong compression and performance benefits for read-heavy workloads.

Choosing between these often comes down to the nature of the workload. If you’re doing analytics over large datasets, columnar formats like Parquet or ORC are usually the right call. If you’re capturing events or streaming messages, Avro might be a better fit.

The Role of Compression

Compression reduces file sizes by encoding repeated or predictable patterns more efficiently. In distributed systems, this saves both storage space and network bandwidth, speeding up data movement and reducing cost.

Compression can be applied at the file level or at the column level (in columnar formats). Modern formats like Parquet support multiple compression codecs, including Snappy, Gzip, Brotli, and Zstd.

Snappy offers fast compression and decompression, making it a good default choice when speed matters more than maximum size reduction. Gzip provides better compression ratios but is slower. Zstd and Brotli strike a balance, offering both speed and compression efficiency.

When choosing a compression strategy, consider the use case. For interactive querying, speed matters, so faster codecs like Snappy are preferred. For archival data or large transfers, stronger compression may save more money in the long run.

Compatibility and Ecosystem Support

Storage format decisions also impact which tools you can use. Most modern data tools support Parquet and Avro natively, but compatibility can vary depending on the processing engine.

For example, if you’re building a data lake on S3 and using Apache Spark for processing, Parquet is almost always a safe choice. It integrates well with tools like Hive Metastore, Presto, Trino, and Dremio.

If you’re using Kafka or other message queues, Avro is a common format due to its compactness and schema registry support.

It’s also worth considering schema evolution—how well a format handles changes in the data structure over time. Avro and Parquet both support schema evolution, which allows you to add or remove fields without breaking downstream systems. This is crucial in agile environments where data changes frequently.

Putting It All Together

The best storage strategy balances performance, flexibility, and compatibility. There’s no one-size-fits-all answer, but understanding the characteristics of each format—and how compression affects storage and query speed—allows you to make informed choices.

As data engineers, our job is to pick the right tools for the job, not just default to what’s familiar. Thoughtful decisions at the storage layer can ripple across the entire data stack, affecting cost, speed, and scalability.

In the next post, we’ll turn our attention to data quality and validation—because no matter how well your data is stored, it’s only as good as it is accurate, complete, and trustworthy.