Modern data pipelines are responsible for much more information than the systems of the past. Every day, 2.5 quintillion bytes of data are created, and it needs somewhere to go. A data pipeline is a series of actions that drive raw input through a process that turns it into actionable information. It’s an essential component of any system, but it’s also one that’s prone to vulnerabilities, some of which are unique to a pipeline’s placement in the lifecycle. Establishing best practices in the data pipeline architecture is vital to eliminate the risks these critical systems create.
Modern data pipelines are far more streamlined than those of the past, but most organizations still have parts of a legacy system (or two) to contend with when transmitting information from their data warehouse. By understanding their current system, they can look at best practice-based improvements to streamline their program.
Components of a Modern Data Pipeline
The days of 36-hour data transfers and build processes are far behind us—or at least, they should be. Organizations often find themselves troubled by older data pipelines that include massive files, shell scripts, and inline scripting that don’t make sense for their modern purposes. It can be hard to integrate all these pipelines because most organizations leverage two types: Extract, Transform, Load, and Extract, Load, Transform.
Extract, Transform, Load (ETL)
Extract, Load, Transform (ELT)
|ETL is the traditional pipeline architecture commonly seen in legacy systems. In this, data is fully prepped before sending it to the warehouse. This is a long process that often challenges users.||Here the transformation occurs within the warehouse. This streamlines the transform step and helps to speed processing. ELT is a modern method that works great with other contemporary solutions like cloud computing, since you can bring distributed cloud computing horsepower to bear.|
It’s unlikely that any large organization is going to have either all ETL or all ELT pipelines. Most likely, they’ll have to manage a combination of both. While this is a challenge, it’s not insurmountable when applying some DevSecOps best practices across the board.
Best Practices in Ensuring a Secure Data Pipeline Architecture
Simplicity is best in almost everything, and data pipeline architecture is no exception. As a result, best practices center around simplifying programs to ensure more efficient processing that leads to better results.
A good data pipeline is predictable in that it should be easy to follow the path of data. This way, if there’s a delay or problem, it’s easier to trace it back to its origin. Dependencies can be troublesome, as they create situations in which it becomes hard to follow the path. When one of these dependencies fails, it can create a domino effect that leads to other errors, making problems hard to trace. The elimination of unnecessary dependencies goes a long way towards enhancing data pipeline predictability.
Data ingestion needs can change drastically over relatively short periods. Without some method of auto-scaling, it becomes incredibly challenging to keep up with these changing needs. Establishing this scalability will depend on the volume and its fluctuations, which is why it’s necessary to tie this piece into another critical component—monitoring.
End-to-end visibility of the data pipeline ensures consistency and proactive security. Ideally, this monitoring allows for both passive real-time views and exception-based management in which alerts trigger in the event of an issue. Monitoring also covers the need to verify data within the pipeline, as this is one of the largest areas of vulnerability. Knowing what data is moving from place to place sets the stage for proper testing.
Testing can be a challenge in data pipelines, as it’s not exactly like other testing methods used in traditional software. Both the architecture itself—which can include many disparate processes—and the data quality require evaluation. Experience is essential. When seasoned experts review, test, and correct data repeatedly, they can ensure a streamlined system with less risk of exploitable vulnerabilities.
Data pipelines that include massive scripts, shell files, and lots of inline scripting aren’t sustainable. Every action taken within a data pipeline requires evaluation of its impact on users in the future. Maintainers should wholeheartedly embrace refactoring the scripted components of the pipeline when it makes sense, rather than augmenting dated scripts with newer logic. Accurate records, repeatable processes, and strict protocols ensure that the data pipeline remains maintainable for years to come.
Choosing the most straightforward options when configuring the data pipeline architecture will help companies better follow the best practices that make their systems predictable. Proactive monitoring and maintenance also prevent long-term issues, as the data pipeline will likely see many adjustments over its useful life. By keeping the best practices in mind and focusing on simplicity, it’s possible to build a data pipeline that is both secure and efficient.