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The Basics of Data Pipelines: What You Need to Know

Data Pipelines Feature

What is a data pipeline?

Data pipelines are the backbone of modern data-driven operations. They enable organizations to collect, transform, and analyze their data quickly and accurately. As more businesses become digital, the need for efficient data pipelines becomes even more critical.

This article will provide an overview of the basics of data pipelines – what they are, what they can do, and how they work. We will also discuss the various components that make up a data pipeline, such as data sources, processing systems, and data storage solutions. With this knowledge, you’ll be able to determine the right data pipeline setup for your business.

The basics of a data pipeline

A data pipeline is a sequence of data processing systems that transport data from one source to another. It follows a predefined workflow, which makes it easier to integrate with other systems, such as visualization tools. It also helps businesses to save time and money by reducing manual processes and optimizing the use of resources.

Here are some of the common components of a data pipeline:

  • Data Sources – Data sources are the places where the systems in the data pipeline collect data. For example, an event data source collects information about events that occur within your business, such as a sale or an accident. A customer data source collects information about your customers, such as their names, addresses, and phone numbers.
  • Data Store – A data store is a place where data is stored for later use. In a data pipeline, a data store is used to temporarily store data from one system before it is redirected to another system. Plus, it is often used for loading and unloading data, auditing data, and for transforming data
  • Data Transformation – A data transformation is a process that alters the format of the data, making it easier to understand and use. Data transformation is an important part of a data pipeline, as it helps to integrate different types of data and makes them useful for different departments within the business.
  • Data Enrichment – A data enrichment process adds more value to the data by adding new information to it. An example of data enrichment is adding information to a customer’s address, such as the name of the building and floor.
  • Data Destination: A data destination is where data is sent after it has been processed in the data pipeline. A data destination could be a data store or another system.
Data Engineering Process

Benefits of data pipelines

There are a plethora of benefits to using data pipelines in an organization. Here are some of the major reasons why you might want to implement one:

  • Consistency: When you use a data pipeline, your processes will be more consistent, which allows you to make better and quicker decisions.
  • Accuracy: Data accuracy is crucial for your business. With a data pipeline, you can make sure that the data is consistent, reliable, and accurate.
  • Scalability: As your business grows, your data pipeline should also scale to accommodate it. A data pipeline helps to make sure that your systems can grow with your business. 
  • Interoperability: Data integration is challenging, but with a data pipeline, you can easily integrate different data sources and make them useful for different departments within your business.
  • Cost-effectiveness: Data pipelines not only help you to be more accurate, but they also save you money. They reduce the costs of your operations and make it easier to use resources effectively.

Data pipeline tools

As part of the ETL process (Extract, Transform, Load), here are some of the primary tools of a data pipeline:

  • Data source connectors: Data source connectors allow you to integrate data from a data source into your data pipeline. The most common data source connectors are databases, web services, file systems, message queues, and data feeds.
  • Data transformation connectors: Data transformation connectors are used to alter the format of the data, making it easier to understand and use. The most common data transformation connectors include ETL (extract, transform, and load), database transformation, and data extraction.
  • Data destination connectors: Data destination connectors allow you to send data from the data pipeline to a data destination. The most common data destination connectors include databases, data warehouses, and data feeds.

Big data and data pipelines

With so many businesses using big data, there is an increased need for data pipelines. Traditionally, data pipelines have been used to process smaller amounts of data. With the recent rise of big data, data engineers have had to start building data pipelines that can handle much larger amounts.

This has led to important changes in the way data pipelines are designed and implemented. Data engineers now have to ensure that the data is distributed across multiple nodes in the system, data is processed asynchronously, and the system is scalable. This means that big data and data pipelines go hand in hand.

Big data, distributed systems, and data pipelines make it easier for organizations to collect, transform, and analyze their data quickly and accurately. This, in turn, enables them to make better business decisions. With a large amount of data from a variety of sources, businesses need robust and scalable data pipelines to manage and process it.

How data engineers use data pipelines

Data engineers use a variety of tools to build data pipelines. These tools include ETL tools, a data warehouse, and an event data platform. Data engineers first determine where the data comes from and where it needs to go. They then create a diagram that shows how the data flows through the data pipeline. Finally, they integrate the data sources and data destinations to build the data pipeline.

Data engineers use data pipelines to transform data and integrate it across different systems. Data engineers use data warehouses to store large amounts of raw data, such as sales and inventory data. They then use ETL tools to transform the data and load it into a data lake. An event data platform collects different types of event data and sends it to a data lake. Data engineers then use the data in the data lake to load it into the data warehouse.

Data engineers can also use data pipelines to enrich data and share it across different systems. For example, they can use a data lake to collect customer information and then distribute it to other systems, such as databases, data warehouses, visualization tools, and artificial intelligence systems. This is the stage where data scientists, AI engineers, and business analysts make sense of the data and turn it into action.


In data engineering, data pipelines provide all the legwork for data collection and processing. They allow organizations to collect data without the need for human intervention. This is a good thing because it can also reduce errors and increase the efficiency of operations in an organization.

The benefits of data pipelines are many, including consistency, flexibility, accuracy, scalability, and cost-effectiveness. There are various components that make up a data pipeline, including data sources, data stores, data transformation, and data destinations. Data engineers use these components to build robust and scalable data pipelines that can handle large amounts of data.

Data pipelines are central to the success of businesses that rely on data, such as marketing companies, online retailers, and financial institutions. With this knowledge, you’ll be able to determine the right data pipeline setup for your business.