Wednesday, July 24, 2024

What Is a Data Pipeline? Examples & Types

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

A data pipeline is a set of tools and processes that facilitates the flow of data from one system to another, applying several necessary transformations along the way. At its core, it’s a highly flexible system designed to ingest, process, store, and output large volumes of data in a manner that’s both structured and efficient. As businesses increasingly rely upon massive amounts of data to provide insights that fuel decision making, enhance operational efficiency, and gain a competitive edge in the market, data pipelines are an essential part of their overall data management strategy. This article explains the key components of data pipelines and how they work, and how to best put them to use in today’s data-driven world.

Jump to:

How do Data Pipelines Work?

A data pipeline has four main functions—ingesting, processing, storing, and outputting data—that work in concert to accomplish the task of moving data from one place to another while applying transformational changes to it.

  • Ingestion. Data from one or multiple sources enters the data pipeline—it could be a live stream of data, or data that has been accumulating in a database for a period of time.
  • Processing. Raw data is transformed and processed into a format that’s ready for analysis—the format depends upon the end goal.
  • Storage. Processed data is delivered to its permanent storage location—a data warehouse or a data lake, for example.
  • Output. Processed data is communicated to end-users—analysts, applications, or other data systems, for example.

Workflow of a Data Pipeline

The workflow of a data pipeline is the series of steps and processes it follows as it migrates data from the source database to the destination. Despite following the same primary steps and using—more or less—the same components, every data pipeline’s workflow can vary depending on the business’s needs and requirements.

Factors such as data format, volume, and desired processing complexity all influence the design, leading to custom configurations for different applications. Throughout the journey from the data source, processing, and output, sophisticated mechanisms within the pipeline are responsible for detecting and rectifying failures, gaps, or errors in the data, ensuring the final product’s quality and integrity.

There are five main components critical to every data pipeline.

Storage

Storage is where the data, either raw or processed, resides, and ranges in size and type depending on the need. Typically, data pipelines use traditional databases, cloud repositories, or modern distributed storage systems that can handle live data.

Pre-Processing

Before analysis, data—especially if it’s raw—needs to be cleaned, normalized, and transformed in preparation. The pre-processing stage encompasses all these operations, transforming all input data into a format more amenable to analytics.

Analytics 

At this stage, pre-processed data undergoes rigorous examination using various statistical tools, often using machine learning algorithms. The goal is to extract meaningful patterns, trends, and actionable insights.

Applications

This component integrates the data with various applications, from business intelligence tools to artificial intelligence and machine learning models. It also enables users to access and apply data insights to real-world scenarios.

Delivery

The final stage of a data pipeline is how the data or insights reach end-users. It could be done through dashboards, reports, or automated systems, allowing data pipelines to bridge the gap between data processing and consumption.

Read about the best tools for data analytics.

The Importance of a Data Pipeline

Data pipelines aren’t just a complementary addition to an organization’s data ecosystem—they play a critical role in data management, data processing, and data migration, ensuring data quality and adequate integration. They meticulously handle errors and inconsistencies in raw data coming from various sources and at varying rates.

By harmonizing the data, a pipeline offers a holistic perspective of the business that can be used to fuel decision-making. Additionally, raw data can be morphed through advanced processing into actionable insights and information that cater to the business’s specific needs, circumstances, and place in the industry.

Data Pipeline Types

While they tend to follow a standard format, data pipelines come in various types depending on how they process data.

Batch Data Pipelines

Batch data pipelines process data in batches or groups at predetermined intervals. This type is best used to deal with large quantities of data—however, they tend to lack real-time responsiveness. Examples include daily sales reports and monthly financial statements.

Real-Time Data Pipelines

Real-time data pipelines process data as soon as it’s generated by the source. This allows them to provide immediate insights in real-time—but this can be resource-intensive and more complex to set up. Examples include real-time stock market predictions and social media feed analytics.

Hybrid Data Pipelines

As the name suggests, hybrid data pipelines are a combination of batch and real-time data pipelines. They can seamlessly switch between batch and real-time processing on-demand. Examples include systems that require analysis of legacy data as well as real-time alerts.

Benefits of Data Pipelines

Data pipelines offer a host of benefits, making them a valuable asset for any data-driven organization. Here are some of the most common:

  • Enhanced decision-making. Data pipelines facilitate decision-making through data-driven insights and advanced predictive analytics of both legacy and real-time data. They provide businesses with a clear picture of their operations, helping them identify trends, patterns, and anomalies.
  • Time and cost-efficiency. After the initial setup, data pipelines automate the data processing and migration process, reducing the need for frequent manual intervention and leading to significant time and cost savings and improved accuracy and reliability.
  • Improved data management. Choosing the right type of data pipeline can contribute to improved data management and governance. It can also help businesses comply with various data regulations, reducing the risk of non-compliance penalties.

Data Pipeline Examples and Use Cases

While other options and alternatives exist, data pipelines remain the most-used solution across various industries, thanks to their flexibility and customizability.

e-Commerce Data Pipelines

In the e-commerce industry, data pipelines enable companies to track behavior of consumers while they browse the business’s website. This enables the business to personalize marketing and promotions according to each customer’s unique experience. Additionally, it can aid with inventory management, ensuring optimal stock levels and reducing out-of-stock situations.

Financial Services Data Pipelines

Data pipelines can help aid in real-time fraud detection and risk assessments, both of which are crucial in the financial services sector. They analyze large volumes of transaction data in order to identify suspicious activity and assess the risk of each process, contributing to safer and more secure financial operations.

Bottom Line: What is a Data Pipeline?

Data pipelines are systems that ingest, process, store, and output data into the target locations. They ensure data quality and integration whilst enhancing decision-making and improving overall operational efficiency. From e-commerce to finance, data pipelines are able to transform data operations, allowing companies to remain competitive in their industries.

Learn more about what data pipelines have in common with extract, transform and load (ETL), and when to choose one process over the other.

Subscribe to Data Insider

Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more.

Similar articles

Get the Free Newsletter!

Subscribe to Data Insider for top news, trends & analysis

Latest Articles