What is a Data Pipeline?
Understanding How Data Pipelines Work
I included this image above to help you think about the concept and make an analogy. What does this image look like? A line of pipes that is probably transporting, for example, natural gas directly from a plant to a factory, a city, a region, and so on.
A fairly simple concept, isn’t it? We encounter it in our daily lives. Well, a Data Pipeline is exactly that. Only, what passes through the pipeline is digital content, the data that we will move from one place to another. Can I conclude this article now? Of course not.
Definition
A Data Pipeline is a means of moving data from a source to a destination, which could be a data warehouse, a data lake, or any other type of repository. We might also use the data in real time, which would already be the destination.
Along the way, the data is transformed and optimized, reaching a state where it can be analyzed and used to develop business insights. Data is rarely ready for use at the source.
Continuing with our analogy, consider oil extraction. If I extract oil, can I use it immediately? Can I put it in my car as fuel? No.
The crude oil extracted undergoes several processing stages before becoming fuel that can power a car. Similarly, data from the source is in a raw format and is rarely ready for use. We need to pass this data through a kind of production line, applying transformations, enrichment, cleaning, etc.
Only then can we use it to feed our data analysis, data science, machine learning processes, and deliver insights to decision-makers.
A data pipeline is essentially a series of steps involved in collecting, organizing, and moving data. When I take data from one place and move it to another, several steps are executed in the middle. This entire process is what we call a data pipeline.
Automation
Modern data pipelines automate many of the manual steps involved in transforming and optimizing data loading. Over the past few years, numerous tools have emerged to automate parts of the process. Many people are wary of automation. However, in technology, every time a tool for automating something emerges, it generates two or three new activities that you must perform.
The company then seeks the most modern tool on the market, everyone is talking about it in the community, and plans to automate everything. They implement the tool and discover they need to configure it, maintain it, and monitor it. The tool does not integrate with other company products, and instead of automating processes, it generates even more work.
Staging Area
Normally, the pipeline includes loading raw data into a staging table or staging area for temporary storage, and then transforming it before loading it into the destination. This staging area makes perfect sense, doesn’t it?
Think along with me. You extracted raw data from the source; it’s not ready for use. You need to apply cleaning, transformation, and organization to the data. Where will you do this? In the ether? No, of course not. It has to be done somewhere.
So, I store the data in a temporary area, apply the necessary cleaning and transformation, and then move the data to another destination. This was usually done in data warehouse projects.
Today, as we have data in various formats, the Data Lake becomes the center of this process. You extract raw data, store it in the Data Lake without modification, then apply the Transformation Pipeline. The result of the transformation, cleaning, etc., is then moved to some destination.
Data Pipeline is a Concept
A Data Pipeline is a concept that can be implemented in many different ways, from automation tools in a local environment, cloud tools, or even programming in languages like Python, R, Scala, C++, or Java.
Remember, a Pipeline is a concept, and you will find numerous tools on the market. A company can build its Pipelines via programming, especially if customization and flexibility are needed that the tools might not offer.
Tools are excellent for simplifying the process but generally offer a bit less flexibility. If I create a Pipeline via programming, will I have more work? Of course, I will, much more. However, I will have more freedom and flexibility to customize that Pipeline to the company’s needs.
Data Processing
A Data Pipeline is a series of data processing steps.
I don’t think I can provide a simpler definition or explanation than this.
What am I doing in a Data Pipeline? I am processing data. I am taking data from one side, applying cleaning, transformation, and delivering it to the other side.
The very pertinent question is, what kind of transformation will I apply in the Data Pipeline? The answer is, it depends.
It depends on the data format at the source, what I will do with the data at the destination, and the business rules that will be considered when cleaning, transforming, and processing the data.
Where Do the Data Come From?
Where do the data come from? From wherever you want. It can come from a relational database, a non-relational database, a web page, a web application, or even a desktop application. It can come from social media posts. Data can be in .txt, .pdf format.
In other words, it depends on the data’s format at the source. Typically, companies implement a data platform that allows you to create, execute, and monitor Pipelines. The data at the source is extracted, inserted into this data platform, and then undergoes all the processing steps.
Next, there are several steps where each step provides an output that is the input for the next step.
If you analyze, you have a pipe connected to the next. So, the data enters a certain step, passes through that step, and the output feeds the next step. This chains together a series of tasks within the data pipeline.
How Many Tasks Can I Have in the Pipeline?
The answer, you already know, is: It depends.
- What kind of transformation?
- Is the data in raw format?
- Do I need to apply a lot of cleaning?
- Do I have to transform the data a lot?
- Is the data sensitive?
- Will I have to apply anonymization techniques to the data?
Depending on these answers, I will define how many steps are necessary in the Pipeline. This continues until the Pipeline is complete. In some cases, if you have a data pipeline with many processing steps and there is some independence between the steps, I can parallelize the execution. This can even be done simply with tools like Apache AirFlow.
Conclusion
And so we have the definition of what a data pipeline is. Building and maintaining data pipelines is one of the main tasks of data engineers. Does this mean that other data professionals are prohibited from creating Pipelines? Of course not.
Typically, the company will hire a data engineer to create and maintain Pipelines and manage the data infrastructure. If the company does not have a data engineer, maybe a data scientist will be responsible for it.
That’s why it is important for any professional to know at least what it is, how to build it, and the main tools for working with data Pipelines.
Thank you for accompanying me this far. 🐼❤️
