A Guide to Data File Object Formats

When working with big data, managing and processing vast amounts of information efficiently is crucial. The structure of data files plays a significant role in this process. Proper file formats ensure data is stored, accessed, and analyzed efficiently, which is essential for tasks ranging from real-time analytics to long-term archival storage. The right file structure can dramatically improve performance, reduce storage costs, and enhance data interoperability.

Why File Structures Matter

File structures matter for several reasons - and there are so many better options than CSV that are available to our development teams. They optimize performance by speeding up data retrieval and processing times, which is essential for handling large datasets. Efficient file formats also reduce the storage footprint, saving costs and improving access times. Interoperability is another key benefit; standardized file formats ensure compatibility across different tools and platforms, facilitating seamless data exchange and integration. As data volumes grow, scalability becomes vital. The right format can handle increased data without significant performance degradation. Additionally, proper file formats include features for data validation and security, ensuring data integrity and safety.

Now, let's delve into some of the most popular data object formats: Parquet, JSON, ORC, and Avro. Each has its unique strengths and use cases, along with certain limitations.

Parquet

Parquet is a columnar storage file format optimized for use with big data processing frameworks like Apache Hadoop and Apache Spark. Parquet files can support complex nested data structures in a flat format and with various compression options. Parquet files are composed of row groups, header and footer. Each row group contains data from the same columns. The same columns are stored together in each row group. The main strengths of Parquet files include: 

  - Columnar Storage - Stores data in columns, making it highly efficient for read-heavy operations and analytical queries. Also, lends well to storing data that was held in a relational database as it is easy to convert to parquet. 
  - Compression - Provides excellent compression and encoding schemes, reducing storage costs and improving I/O efficiency. This makes parquet files much more efficient and better at managing large data set compared to CSVs.
  - Schema Evolution - Supports adding new columns to existing files without rewriting the entire dataset. 
  - Compatibility - Widely supported by big data tools and frameworks. Additionally, is easily read and written to by a variety of popular coding languages such as Python and Spark. 

Parquet Files have many areas where they are useful such as: 

  - Analytical Queries - Ideal for scenarios where data is read more often than written, such as business intelligence and reporting. Parquet has built-in support for predicate pushdown and column pruning, which can improve query performance.
  - Big Data Processing - Perfect for environments using Hadoop, Spark, and other similar tools. Parquet formatting is designed to support fast data processing for complex nested data structures like log files and event streams at scale. 

While Parquet files have many strengths, there are some factors to consider: 

  - Write Performance - Can be slower for write-heavy operations compared to row-based formats. 
  - Complexity - Requires understanding of columnar data management for optimal use.

JSON

JSON (JavaScript Object Notation) is a lightweight data interchange format, easy for humans to read and write and easy for machines to parse and generate. This is a popular format to hold unstructured and object-oriented data. JSON is a string whose format very much resembles JavaScript object literal format. You can include the same basic data types inside JSON as you can in a standard JavaScript object – for example: strings, numbers, arrays, booleans, etc.  Some of the advantages of JSON include: 

  - Human-Readable - Easy to understand and write by humans, making debugging and development straightforward. Generally, very easy for developers to work with and clearly align to common patterns for APIs and other software development practices.
  - Flexibility - Allows for a wide variety of data structures, including nested objects and arrays. Data structure or schema is not enforced, so JSON is ideal for holding data objects that vary in their structure. 
  - Language Support - Supported by virtually every programming language, making it highly versatile. There is also a large community and knowledge base about best ways to leverage JSON objects and troubleshoot common issues. 

JSON is best used for:

  - Web APIs - Commonly used for data exchange in web services and APIs due to its simplicity and readability. Also works consistently across may coding languages making it a reliable format when multiple coding languages are expected. 
  - Configuration Files - Frequently used in configuration files due to its ease of reading and editing by humans. Thus, it is easy for developers to edit them and leverage for configuration of variables in applications. 
  - Document Databases -  JSON or BSON objects are commonly used in NoSQL Document databases like MongoDB or CouchDB. JSON is used to hold a data object in a document and is grouped in collections to support a database. 

When working with JSON objects, these are the key factors to consider. 

  - Performance - Not optimized for large-scale (over 5TB) data storage and can be slow to parse for large datasets. Works better as a transactional data store that is write heavy but does not accurate large consistent volumes. For example, JSON is a great way to store output from IoT devices as opposed to an aggerated data lake. 
  - Size - Larger file sizes compared to binary formats due to its text-based nature. Because JSON files are usually processed in-memory, larger files begin to degrade performance. One fix for this is to use a BSON file which counteracts this problem.

ORC

ORC (Optimized Row Columnar) is a columnar storage file format for Hadoop ecosystem, designed to overcome the limitations of other formats. How the file structure works is that ORC stores data in a series of stripes, and each stripe is a collection of rows. Each stripe is further divided into a series of data chunks, where each chunk stores the data for a specific set of columns. The chunks are compressed using a combination of techniques such as predicate filtering, dictionary encoding, and run-length encoding. There are many advantages to using this format:

  - Efficient Compression - Advanced compression techniques reduce storage requirements significantly. It uses several compression algorithms such as Snappy, Zlib, Gzip etc. which reduces the storage space required to store the data. All this optimization helps significantly reduce storage costs when storing large datasets. 
  - Fast Performance - Optimized for high performance with complex data structures. Integrates well with data that was stored in a relational data structure. Specifically, the data is stored in a way that is optimized for column-based operations like filtering and aggregation
  - Indexing - Includes lightweight indexes for efficient data access and retrieval. Indexes are stored as columns in ORC so that only the columns where the required data is present are read. Index data consists of min and max values for each column as well as the row positions within each column.
  - Schema Evolution - Supports changes to data schema without major rewrites. Schema evolution allows for changes to the schema used to write new data while maintaining backward compatibility with the schema of your old data. As a result, you can read it all together as if all the data has one schema.

Organizations should lean towards ORC when they are facing the following scenarios: 

  - Data Warehousing - Ideal for scenarios requiring fast read access and efficient storage, such as data warehouses. Given the strength ORC has in compression, it usually is chosen over other structures when optimizing for large volumes is the main priority. 
 - Hadoop - Best suited for use within Hadoop environments where it can leverage its performance optimizations. If you are not using any Hadoop functionality, the value from ORC may be lost. 

There are some important factors that would lead teams to use a different format:

  - Complexity - Requires familiarity with columnar data and Hadoop for optimal usage. Will probably be too complex for simple data storage operations.
  - Write Latency - Higher latency for write operations compared to row-based formats. 

Avro

Avro is a row-based storage format designed for data serialization, offering compact storage and efficient schema evolution. What is unique about Avro stand out as a file format is that it is self-describing. Avro bundles serialized data with the data’s schema in the same file – the message header contains the schema used to serialize the message.  This enables software to efficiently deserialize messages. Arvo has strong benefits such as:

 - Compact Storage - Binary format results in smaller file sizes compared to text formats.
  - Schema Evolution - Robust support for schema evolution, making it ideal for systems where data schemas frequently change. It supports dynamic data schemas that can change over time; therefore, it can easily handle common schema changes such as missing fields, added fields, or edited/changed fields.
 - Interoperability - Designed for data exchange, with support for diverse programming languages.
  - Splittable - Data can be split into chunks for parallel processing. This is essential when working with large data sets, as sometimes the data cannot be processed within one machine due to volume and needs to be broken into chucks to complete the entire workload. 

When considering using Avro focus on the following use-cases:

  - Data Serialization - Ideal for scenarios requiring efficient data serialization and deserialization, such as data pipelines.
  - Streaming Data - Suitable for streaming data applications where schema changes are frequent. Arvo scales well with large data volumes so it is ideal for this kind of use case. 
  - Cross-Language Data Exchange - Great for systems where data needs to be shared across different programming environments.

Consider the following when using Avro: 

  - Complexity - Requires understanding of schema management and binary encoding. These file types are less human readable and need to be edited and maintained through automated processes and Arvo configuration. 
  - Parsing Overhead - Higher overhead for parsing compared to simpler formats like JSON. 

Conclusion

Choosing the right data object format depends on the specific needs of your data processing and storage requirements: 

Understanding the strengths and limitations of each format will enable you to make informed decisions, optimizing your data management strategies for performance, storage efficiency, and scalability. Hope you found this overview helpful! 

By Ellie Najewicz