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Data Pipeline Basics: From Raw Data to Insights

Making better decisions quicker is a vital competitive edge in the corporate world. Yet, getting timely insights from data might be harder than it appears. Most businesses currently use between 40 and 60 different applications per department. In addition, they also use legacy software and databases. How can you combine the information from these different sources and make it actionable? Here is where your data pipeline comes in. Let’s examine data pipelines, the differences with ETL, and how to use them for business value creation.

What Is a Data Pipeline?

A data pipeline is a method for transferring data from one location (source) to a destination (such as a data warehouse or lake). Data is optimized and modified along the journey, eventually reaching a stage where it can be examined and used to generate business insights. These are the basic components:

  • Sources: Your data might be in an internal database powered by SQL. Or it could be in a cloud platform like Snowflake or HubSpot. Regardless of the origin, your source is wherever your data begins its journey.
  • Transformation: Data transformed happens in a variety of ways, such as standardization, sorting, deduplication, validation, or verification. All in all, the transformation stage is when your data changes in some way before it reaches its final destination.
  • Destination: The destination is where you want your data to end up at the end of its processing. Usually, it’s some kind of data warehouse or data lake where you can store data until you’re ready to use it.

The Process and Steps

The basic components described above intertwine with the data pipeline process. If the data is not already in the data platform, it is read from the source and ingested at the start of the pipeline. Afterward, there are a series of steps in which each step provides an output that serves as the input for the following one. This keeps happening till the pipeline finishes. 

In more detail, the process follows these steps: 

  • Data Ingestion: There are two different data ingestion types. On one hand, batch processing loads data periodically into the data pipeline. On the other, stream processing ingests and loads the data as soon as it’s created.
  • Data Loading: In this step, data is loaded into the destination, which might include a cloud data warehouse, a data lake, or a business intelligence/dashboarding engine, where the data is analyzed.
  • Data Processing: The processing stage is when you prepare the data to be analyzed. What this stage holds will depend on what state your data is in when it’s loaded into the pipeline. Additionally, how you want the data to look by the end of the process will also affect this step. You might standardize the data, remove duplicates, or verify the data. 

Pipeline Architecture Examples

Data pipeline architecture can look a variety of different ways, depending on the specific insights you want to drive from your data. The most basic architecture is data passing through a certain amount of operations until it reaches its destination. But data pipelines can have more complex architecture as well. 

data pipeline architecture example

Here’s a hypothetical example of what a data pipeline might look like. A company’s marketing stack comprises various platforms, including Google Analytics, Hubspot, and LinkedIn Ads. Let’s say a marketing analyst wants to understand the effectiveness of an ad. They’ll need to build a data pipeline to manage the consolidation and standardization of data from these disparate sources into a specific destination for analysis.  

A data pipeline can be as simple as this example, or it could be more complex with a variety of different batches, master data, and different layers. It all depends on the data you have and what you’re trying to do with it.

Data Pipeline vs. ETL

Data pipelines are often confused or used interchangeably with ETL. But are ETL and data pipelines the same process, or are they different? ETL stands for “extract, transform, and load” and it refers to a type or sub-process of a data pipeline. They are similar in the sense that they move data from a source to a destination. 

But while an ETL process is limited to pulling data out of a source, transforming it, and loading it into the destination, a data pipeline is broader; it is the entire process involved in ingesting data from raw sources to the complete transformation of the data for analytics consumption, encompassing transformations before or after loading data to the destination.

Data Pipelines Challenges

Only robust, end-to-end data pipelines can adequately prepare you to source, consolidate, manage, analyze, and use data efficiently to provide cost-effective business value. However, the process can be time-consuming, where each step can take multiple tasks to make possible. As data pipelines grow in sophistication, data teams trade complexity in orchestration for simplicity in logic. Consequently, the one big job becomes a dozen scoped steps structured into a dependency graph.  

Eventually, when you have too many tasks to manage, orchestration complexity also becomes a challenge and has a significant cost associated with understanding the impact of change and managing the change across all connected pipelines.   

That’s why automation, or automated orchestration, is becoming a growing approach to the data pipeline process. Automation streamlines data engineering day-to-day tasks and automates the change management process, which makes data pipeline architectures more efficient and accurate. Overall, automation makes transporting your data from one place to another simple and easy, which is why so many data teams are relying on it.

Final Thoughts About Data Pipeline Basics and Next Steps

Overall, data pipelines are a highly valuable method to work on and manage your data. Like the water in your sink travels from the pipes to your hands quickly and efficiently, data pipelines bring data from one place to another quickly and effectively. With an eye to the near future, automated pipelines will become the standard to help decrease human error, increase speed, and grow the business value exponentially.

Additional Reading and Resources