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What is a data pipeline?

The efficient flow of data from one location to the other — from a SaaS application to a data warehouse, for example — is one of the most critical operations in today’s data-driven enterprise. After all, useful analysis cannot begin until the data becomes available. Data flow can be precarious, because there are so many things that can go wrong during the transportation from one system to another: data can become corrupted, it can hit bottlenecks (causing latency), or data sources may conflict and/or generate duplicates. As the complexity of the requirements grows and the number of data sources multiplies, these problems increase in scale and impact.

How is a data pipeline different from ETL?

You may commonly hear the terms ETL and data pipeline used interchangeably. ETL stands for Extract, Transform, and Load. ETL systems extract data from one system, transform the data and load the data into a database or data warehouse. Legacy ETL pipelines typically run in batches, meaning that the data is moved in one large chunk at a specific time to the target system. Typically, this occurs in regularly scheduled intervals; for example, you might configure the batches to run at 12:30 a.m. every day when the system traffic is low.

By contrast, "data pipeline" is a broader term that encompasses ETL as a subset. It refers to a system for moving data from one system to another. The data may or may not be transformed, and it may be processed in real-time (or streaming) instead of batches. When the data is streamed, it is processed in a continuous flow which is useful for data that needs constant updating, such as data from a sensor monitoring traffic. In addition, the data may not be loaded to a database or data warehouse. It might be loaded to any number of targets, such as an AWS bucket or a data lake, or it might even trigger a webhook on another system to kick off a specific business process.

Who needs a data pipeline?

While a data pipeline is not a necessity for every business, this technology is especially helpful for those that:

  • Generate, rely on, or store large amounts or multiple sources of data
  • Maintain siloed data sources
  • Require real-time or highly sophisticated data analysis
  • Store data in the cloud.

As you scan the list above, most of the organisations you interface with daily — and probably your own — would benefit from a data pipeline.

What are the different types of data pipelines?

There are several different data pipeline solutions available, and each is well-suited for different purposes. For example, you might want to use cloud-native tools if you are attempting to migrate your data to the cloud.

The following list shows the most popular types of pipelines available. Note that these systems are not mutually exclusive. You might have a data pipeline that is optimised for both cloud and real-time, for example.

  • Batch. Batch processing is most useful when you want to move large volumes of data at a regular interval, and you do not need to move data in real-time. For example, it might be useful for integrating your Marketing data into a larger system for analysis.
  • Real-time. These tools are optimised to process data in real-time. Real-time is useful when you are processing data from a streaming source, such as the data from financial markets or telemetry from connected devices.
  • Cloud-native. These tools are optimised to work with cloud-based data, such as data from AWS buckets. These tools are hosted in the cloud, allowing you to save money on infrastructure and expert resources because you can rely on the infrastructure and expertise of the vendor hosting your pipeline.
  • Open-source. These tools are most useful when you need a low-cost alternative to a commercial vendor, and you have the expertise to develop or extend the tool for your purposes. Open-source tools are often cheaper than their commercial counterparts, but require expertise to use the functionality because the underlying technology is publicly available and meant to be modified or extended by users.

If you’re ready to learn more about how Ronan Analytics can help you solve your biggest data collection, extraction, transformation, and transportation challenges, please contact us.