Strategies for Constructing Effective Data Pipelines
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Chapter 1: Introduction to Data Pipelines
In 2021, notable investments in data pipeline companies included Fivetran with $565 million, Airbyte at $150 million, Matillion securing $100 million, Rivery with $16 million, and Informatica's public offering. Each of these entities is linked to the realm of data pipelines, often referred to by acronyms like ETL, ELT, E(t)LT, and CDC.
Today, our primary focus will be on batch processing and the considerations essential for building effective batch data pipelines, irrespective of the tools at your disposal.
Section 1.1: Key Considerations for Data Pipeline Development
When constructing pipelines, it's critical to recognize that tools and technology are merely that—tools. They alone won't yield impactful results. Without the right processes and human involvement, data won't translate into actionable insights or solutions.
Before embarking on the construction of a data pipeline, it's vital to reflect on several factors.
What Is This Data Being Used For?
Understanding the business context is crucial for engineers. Knowing the specific initiative behind your work serves as motivation and facilitates better design decisions. Hence, comprehending the pipeline's purpose—be it fraud detection, tracking KPIs, or enhancing sales—is fundamental. This connection to tangible data products and dashboards reinforces the significance of your role in the process.
But this prompts the question: what exactly is this data intended for?
The first video titled "What To Consider When Building Data Pipelines - Intro To Data Infrastructure Part 2" delves into the essentials of constructing data pipelines. It outlines key elements that engineers must contemplate for successful implementation.
Is All The Data Valid?
One of the foremost considerations before initiating a data pipeline is the validity of the data. It’s common to encounter tables with fields that are either unsupported or incorrect, even while the majority of the data is accurate. Incorporating flawed data can lead to issues for users unaware of its inaccuracy.
How Often Will You Pull Data?
For non-streaming data, regular intervals for data pulls are often determined by several factors: the timing of data necessity, the volume of data being retrieved, and its frequency of change.
While many pipelines are programmed to run daily at midnight, alternative scheduling—like hourly pulls or real-time data—may be warranted depending on business requirements. Conversely, if a report is only needed weekly, daily pulls may suffice.
Additionally, if a data pipeline requires extensive runtime, consider smaller, more frequent batches instead of increasing frequency. Engage stakeholders to determine how often they will require data insights.
Incremental, Total Extract, Historical Updates
Data extraction can typically be accomplished through a few distinct methods. You may opt for full table pulls, which simplify the process but can be costly, or implement incremental data loads if data is primarily appended. Historical merges, pulling only newly inserted or updated data, can be the most complex approach.
Choosing the appropriate method hinges on understanding how your data is structured within the source system and the volume at hand.
Who Will Manage the Pipeline?
A pivotal inquiry when constructing data pipelines is identifying who will oversee their long-term management. Pipelines are not infallible; they can fail or process erroneous data. Clarity on ownership is essential to ensure that someone is available to address issues as they arise, preventing neglected pipelines from becoming a liability.