Building a Data Pipeline: What You Need to Know

How a Data Pipeline Works

Good data pipelines require an efficient flow of data from one location to another, such as a software application to a data center. This can be data-at-rest or data-in-flight, and the pipeline will separate it appropriately for processing. But as we know all too well, there can be hiccups with IT, and in our data-driven world this can cause organizations a major headache. Problems could include: corrupted data, conflicting or duplicate data being processed from multiple sources, or any number of bottlenecks which often result in latency.


Benefits of a Data Pipeline

If you’re reading this, you’ve probably already determined you need a data pipeline—but not every organization does. Those benefiting most from this typically generate or store large amounts of data (often in the cloud), maintain siloed data sources that need to be consolidated, or require real-time data (such as a financial firm, healthcare organization, or government agency).


Data Pipelines and ETL

Before looking at the main types of data pipelines, it’s important to understand how pipelines differ from ETL (Extract, Transform, and Load). Extraction is the process of taking data from numerous sources, such as: online, brick-and-mortar, legacy, and more—and prepping it for transformation. Transformation, then, is what paves the way for data integration. This may mean reformatting data, similar to converting dollars to euros. Lastly, loading involves putting this new data into a database or data center in one large chunk.

Setting up a data pipeline is similar, yet different. It involves moving data from one system to another, but data may not be transformed, and it is often processed via streaming, instead of one large patch; this means it may be processed in a continuous flow, a benefit for data that needs constant updating.


Four Types of Pipeline Solutions

1. Batch

Organizations moving large volumes of data regularly, and not in real-time, may choose this option, as it’s an efficient way to process large amounts of data. It can be used offline, giving managers control over when to process the data (usually at night). Of course, an issue can arise during batch processing and without a dedicated IT team, trying to fix the system may result in the need for outside help.

2. Real-time

Organizations that need data to be constantly streamed, like financial stock markets or traffic systems, will find this pipeline best suited to their needs. One challenge organizations may have with this solution is the data output needs to be as fast as the data input, otherwise the result could be storage and memory issues.

3. Cloud Native

This pipeline is most effective for organizations that are already in the cloud, because that is where it’s hosted. This reduces costs associated with building and maintaining a pipeline, and organizations can be assured that with a reputable provider there will always be a team of IT experts on hand, should anything go wrong.

4. Open Source

These tools are publicly available, and may be effective for organizations wanting a low-cost alternative to commercial providers; it also helps to avoid vendor lock-in. However, organizations will need to have an experienced IT staff to set one up and make necessary modifications over time, and many do not or cannot afford them.


Building a Data Pipeline

Now, actually explaining how to build a pipeline on your own is far too complex to explain here, but you should know that it can be a costly undertaking. Expensive experts will need to be brought on board or pulled from more valuable projects, and it’ll take a while—possibly months (and that’s assuming everything goes smoothly).

If you’re considering building a data pipeline, the experts at DSM can review your options with you and discuss which may be best for your organization. Even if you ultimately decide on a non-cloud-based pipeline, we’re here to help.

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