Functional Data Engineering, as defined by Maxime Beauchemin, is a paradigm that has gained traction in recent years among data teams, especially with the rise of big data and distributed computing technologies. However, it is not universally known or adopted by all data teams, as the familiarity with the concept can vary based on factors such as team size, industry, experience, and the specific tools or technologies being used.

Maxime Beauchemin, the creator of Apache Airflow and other data engineering tools, introduced the Functional Data Engineering concept as an approach that emphasizes immutability, idempotence, and the use of functional programming concepts in data engineering. This approach encourages designing data pipelines as a series of deterministic, stateless, and composable functions that can be easily scaled, tested, and debugged.

While some data teams are quite familiar with Functional Data Engineering and have adopted it as a core part of their data engineering practices, others may not be fully aware of it or may still be using more traditional approaches.

Today, let us examine the most important aspect of functional data engineering: Immutable data. Immutable data in data pipelines refers to data that cannot be changed or modified once it has been created. We gain this property by ensuring that all the data in your data pipeline, starting from the raw data, the results of its intermediate processing steps, and the final outputs and reports, are immutable.

To obtain an unbiased opinion that reflects industry-wide consensus on the benefits of immutability, I asked ChatGPT (Model: GPT-4) to summarize it for me. This was my prompt:

What are the benefits of designing a data pipeline to consist of immutable data rather than mutable data? Be detailed and thorough.

Designing data pipelines with immutable data has several benefits compared to mutable data. Immutable data is data that cannot be changed once it has been created, whereas mutable data can be modified after creation. Here are some of the key benefits:

1. Data Consistency: Immutable data ensures that once data is written, it remains consistent throughout the entire data pipeline. This eliminates the risks of data corruption, accidental overwrites, or unintended modifications, leading to more accurate analysis and decision-making.
2. Traceability and Auditability: Since immutable data cannot be changed, it offers a clear and transparent audit trail. This is particularly valuable in industries with strict compliance requirements, as it simplifies tracking data changes and identifying the source of any discrepancies.
3. Simplified Error Recovery: In case of errors or failures in the pipeline, immutable data makes it easier to recover or rollback to a previous state. You can quickly identify the erroneous data and reprocess it without affecting other data points or worrying about inconsistencies.
4. Enhanced Concurrency and Parallelism: Immutable data enables better support for concurrent and parallel processing. Since data cannot be changed, there’s no need for complex locking mechanisms or synchronization, allowing different stages of the pipeline to run simultaneously without conflicts.
5. Idempotent Processing: Immutability enables idempotent data processing, meaning that the same operation can be applied multiple times without changing the result beyond the initial application. This simplifies retries and guarantees consistent results across multiple runs or retries of a pipeline stage.
6. Improved Security: Immutable data reduces the risk of unauthorized data manipulation or tampering. By ensuring that data remains consistent and unchangeable, you can better protect the integrity of your data and prevent unauthorized changes.
7. Easier Debugging and Troubleshooting: Debugging and troubleshooting are simpler with immutable data, as you can easily identify the exact state of the data at any point in time. This helps you quickly find the root cause of issues and resolve them efficiently.
8. Simplified Data Versioning: Immutable data naturally supports data versioning. Each new version of the data is stored separately, which makes it easy to compare different versions, track changes, and maintain a historical record of the data.
9. Reduced Complexity: Immutability reduces the complexity of the pipeline by eliminating the need for complex state management, locking, and synchronization mechanisms. This makes the pipeline easier to develop, maintain, and scale.
10. Better Performance: By eliminating the overhead of managing locks, synchronization, and state changes, immutable data can lead to improved performance in your data pipeline.

In summary, designing data pipelines with immutable data offers numerous benefits, including data consistency, traceability, simplified error recovery, enhanced concurrency, idempotent processing, improved security, easier debugging and troubleshooting, simplified data versioning, reduced complexity, and better performance. These benefits can lead to more reliable, efficient, and secure data pipelines.

As you can see, immutable data in your data pipeline has a lot of benefits. One benefit we consider important, but ChatGPT missed, is that immutable data is easy to reuse, facilitating collaboration across data teams.

With all these benefits, why are they not popular? Again, I asked ChatGPT this question.

If immutable data has so many benefits, are the majority of today’s data pipelines consisting of immutable data? If not, why not?

While the benefits of immutable data are well-established, not all data pipelines today consist of immutable data. There are a few reasons for this:

1. Legacy systems and processes: Many organizations still rely on legacy systems and processes, which were designed with mutable data in mind. These systems may be difficult or expensive to modify, and transitioning to an immutable data paradigm might require significant time, effort, and resources.
2. Lack of awareness or understanding: Some data teams may not be familiar with the benefits of immutable data or the Functional Data Engineering paradigm. They might continue to use mutable data due to a lack of knowledge about the advantages of immutability or because they are more comfortable with traditional, imperative approaches to data engineering.
3. Complexity of data sources and transformations: In some cases, working with immutable data may add complexity to the data pipeline, especially when dealing with multiple data sources, complex transformations, or frequent updates. Managing this complexity may require advanced tools or expertise that not all data teams possess.
4. Real-time or streaming data: For certain use cases, such as real-time analytics or stream processing, mutable data structures might be more suitable. In these scenarios, data is constantly being updated or appended, and the benefits of immutability may be outweighed by the need for low-latency processing.
5. Industry-specific requirements: Some industries, such as finance or healthcare, have unique data processing requirements that may not always be compatible with an immutable data paradigm. In these cases, mutable data may be necessary to meet specific regulatory or operational needs.
6. Tooling and technology limitations: The data processing tools or technologies used by an organization might not fully support immutable data or Functional Data Engineering principles. In such cases, data teams may need to use mutable data to work within the constraints of their existing infrastructure.

While the adoption of immutable data and Functional Data Engineering is growing, there are still various factors that can prevent data pipelines from fully utilizing these concepts. However, as more organizations recognize the benefits of immutability and new tools and technologies emerge to support it, we can expect to see an increasing shift towards immutable data in data pipelines.

While these are valid reasons, we want to tackle these challenges head-on with our platform, Trel.

Trel and immutable data

Trel is a novel data automation platform with a data catalog specifically designed to help build data pipelines consistent with Functional Data Engineering, including immutable data. We have invented a new data catalog with features that takes full advantage of the fact that data is immutable. We have replaced DAG-based automation with a new approach called “Catalog-based Dependency” that allows you to gain all the benefits of immutable data presented by ChatGPT and some more.

Catalog-based dependency provides the flexibility needed to automate complex data sources and transformations, something DAG-based approaches struggle with. Legacy systems can be supported by computing results as immutable data and copying them to mutable locations, avoiding many of the pitfalls of mutable data.

Our platform, Trel, takes the effort out of building immutable data pipelines by making them easier to design and build than traditional DAG-based pipelines. It is the only platform in the market to provide all the tooling support your data team needs to adopt Functional Data Engineering.

If you want to learn more, don’t hesitate to get in touch with us for a free consultation to determine if Functional Data Engineering and our platform, Trel, are a good fit for your data team.