Data Integration vs. Data Pipeline: What's the Difference?

What's your strategy for data integration? How is your data pipeline performing? Odds are that if your company is dealing with data, you've heard of data integration and data pipelines. In fact, you're likely doing some kind of data integration already. That said, if you're not currently in the middle of a data integration project, or even if just you want to know more about combining data from disparate sources - and the rest of the data integration picture - the first step is understanding the difference between a data pipeline and data integration.

It's easy to get confused by the terminology.

Luckily, it's easy to get it straight too. First, let's define the two terms:

Data integration involves combining data from different sources while providing users a unified view of the combined data. This lets you query and manipulate all of your data from a single interface and derive analytics, visualizations, and statistics. You can also migrate your combined data to another data store for longer-term storage and further analysis.

A data pipeline is the set of tools and processes that extracts data from multiple sources and inserts it into a data warehouse or some other kind of tool or application. Modern data pipelines are designed for two major tasks: define what, where, and how data is collected, and automate processes to extract, transform, combine, validate, and load that data into some form of database, data warehouse, or application for further analysis and visualization.

And so, put simply: you use a data pipeline to perform data integration.

Easy, right?

Strategy and Implementation

The data integration is the strategy and the pipeline is the implementation.

For the strategy, it's vital to know what you need now, and understand where your data requirements are heading. Hint: with all the new data sources and streams being developed and released, hardly anyone's data generation, storage, and throughput is shrinking. You'll need to know your current data sources and repositories and gain some insight into what's coming up. What new data sources are coming online? What new services are being implemented? Etc.

It also helps to have a good idea of what your limitations are. What kind of knowledge, staffing, and resource limitations are in place? How do security and compliance intersect with your data? How much personally identifiable information (PII) is in your data? Financial records? How prepared are you and your team to deal with moving sensitive data? And, finally, what are you going to do with all that data once it's integrated? What are your data analysis plans?

Once you have your data integration strategy defined, you can get to work on the implementation. The key to implementation is a robust, bullet-proof data pipeline. There are different approaches for data pipelines: build your own vs. buy. Open source vs. proprietary. Cloud vs. on-premise.

Read Data Integration Tools for some guidance on data integration tools. Try Build vs. Buy - Solving Your Data Pipeline Problem for a discussion of building vs. buying a data pipeline. And finally, see Deciding on a Data Warehouse: Cloud vs. On-Premise for some thoughts on where to store your data (Spoiler: we're big fans of the cloud).

The main idea is to take a census of your various data sources: databases, data streams, files, etc. Keep in mind that you likely have unexpected sources of data, possibly in other departments, for example. And remember that new data sources are bound to appear. Next, design or buy and then implement a toolset to cleanse, enrich, transform, and load that data into some kind of data warehouse, visualization tool, or application like Salesforce, where it's available for analysis.

And that's a good starting place. Now you know the difference between data integration and a data pipeline, and you have a few good places to start if you're looking to implement some kind of data integration.