Modern data pipelines are responsible for much more data than outdated systems. A vast amount of data is generated daily, and it needs somewhere to go. A data pipeline is a series of actions that drive raw input using a process that turns it into actionable information. It’s a vital element of any system but also exposed to vulnerabilities. Building best practices in the data engineering pipeline architecture is essential to reduce the risks associated with these systems.
This guide will discuss advanced-level practices for managing data pipelines in the real world. Before proceeding, let’s understand more about the modern data pipeline.
What is a Data Engineering Pipeline Architecture?
A data pipeline refers to a series of linked processes that moves data from one point to another, transforming it along the way. It’s linear, with parallel and often sequential executions. The metaphor- “a pipeline”- helps us know why pipelines that transfer data can be so tough to build and maintain.
How should a data pipeline work? Indeed, only reforming it in expected manners. A pipeline must be designed to sustain data or data quality from the ground up. It also includes the four dimensions: governance, fitness, lineage, and stability.
The Challenges with Opaque Data Pipelines
Very often, you can see the pipeline is down, and you don’t know why. Often you will solve it in minutes, but commonly, the diagnosis takes days.
The problem is the method pipelines tend to be built- opaque- the same as real-life oil-bearing pipelines. You can’t identify whether it is a screwy transformation or a leak somewhere; it takes a lot of effort and time to know the actual reason. Even, often the pipeline is not even responsible for the fault.
Main Elements of a Modern Data Pipeline
Companies often find themselves triggered by outdated data engineering pipelines, which include shell scripts, massive files, and inline scripture, which need to be clarified for their modern objectives. It isn’t easy to consolidate these pipelines as most companies use two types:
Extract, transform, load: Data warehousing and business intelligence use extract, transform, and load (ETL) to collect data from various sources, transform it, and load it into a destination database or data warehouse. ETL is a traditional data pipeline architecture generally seen in outdated systems.
Here’s a brief explanation of each step in the ETL process:
- Extract: This step involves extracting data from various sources, such as databases, files, or APIs. The data may be in different formats and structures.
- Transform: It helps clean, filter, and transform the data to ensure accuracy and consistency. This may include removing duplicates, converting data types, and applying business rules and calculations.
- Load: This loads the transformed data into a destination database or warehouse. Depending on the system’s requirements, the data may be loaded in batches or real-time.
- Extract, load, transform: This process is like ETL but involves loading the data into the destination database first and then performing the transformation steps. Big data processing may be more efficient when performed on the destination system, as it makes use of the power of that system to perform the transformations. It is a modern practice which works excellently with other contemporary technologies, such as cloud computing.
It is improbable that any company will have all ELT or all ETL data engineering architecture. Most commonly, they’ll have to use a combination of both. Although challenging, it is common when using a few engineering best practices across the broad.
Engineering Best Practices to Ensure a Secure Data Pipeline Architecture
Simplicity is best in almost everything, and data engineering architecture is no exception. As a result, data engineering best practices around simplifying actions to ensure more effective processing, which drives better outcomes.
- Predictability: An efficient data architecture is predictable, which means it must be easy to follow the data path. This makes tracing it back to its origin easy, even with a delay or issue. Dependencies can be annoying, as they generate solutions that make it hard to follow the path.
Failing any of these dependencies can produce a domino effect that brings more mistakes, making errors tough to trace. Mitigating insignificant dependencies goes a long way toward improving data pipeline predictability.
- Scalability: Data ingestion demands change immensely over partially short periods. Implementing the auto-scaling technique is necessary to manage these changing needs. Building the scalability is based on the volume and its fluctuations, which is why it is significant to integrate this element into another significant piece of monitoring.
- Monitoring: End-to-end visibility of the data pipeline architecture ensures proactive security and consistency. Preferably, this data engineering architecture practice enables exception-based management and passive real-time views. This trigger in the event of an issue.
It also includes the need to verify data within the pipeline, as this is one of the most significant aspects of susceptibility. Knowing how data shifts from one place to another sets the stage for proper testing.
- Testing: It is often challenging in relation to data engineering architecture, as this is different from other testing techniques used in outdated systems. Both the architecture itself- which encompasses several disparate mechanisms and the data quality need evaluation.
Experience is mandatory. When seasoned experts test, review, and correct data frequently, they can ensure streamlined mechanisms with less risk of exploitable vulnerabilities.
- Maintainability: The data pipeline encompasses shell files, big scripts, and several inline scripting, which are unsustainable. Every step taken under a data engineering pipeline architecture needs evaluation of its effect on consumers in the future.
Accountable authorities should enthusiastically embrace refactoring the scripted elements of the pipeline when it makes sense instead of augmenting dated scripts with newer logic. Precise records, strict protocols, and repeatable processes ensure the data pipeline remains sustainable for the coming years.
- Control all four dimensions of data quality: While building data engineering architecture, the four dimensions of data quality matter which are governance, fitness, lineage, and stability. These criteria exist in equilibrium, as data quality cannot be maintained without these dimensions.
Selecting the most appropriate option when deciding on the modern data pipeline architecture help organizations better understand the best practices which make their systems predictable. Anticipatory maintenance and monitoring prevent long-term challenges. Significantly because the data pipeline probably sees several adjustments over its useful life. By considering the best techniques and focusing on simplicity, it is feasible to design a data pipeline that is both efficient and secure.