What is a data pipeline? A guide to the what, why, and how

Data pipelines are essential components of the modern data stack. They allow organizations to extract value from the vast amounts of data generated across various sources.

This article explores data pipelines in detail, discussing their function, components, benefits, and real-world applications.

What is a data pipeline?

So, what are data pipelines? A data pipeline is a series of processes that extract raw data from various sources, transform it into a desired format, and load it into target systems for storage or further use. These sources can include APIs, SQL databases, events, and files.

Transformation processes used in data pipelines may involve augmentation, enrichment, filtering, grouping, aggregating, or applying algorithms to ensure standardization. Once transformed, the data can be used for further analysis or fed into different systems for operational applications.

Organizations typically employ multiple data pipelines for different purposes.

This data pipeline diagram illustrates how data moves from sources through various transformations to downstream business systems.

How do data pipelines work?

Data pipelines operate in one of three modes:

1. Batch processing

Batch processing loads data into a repository in 'batches' at specific times, usually during less busy periods or outside of business hours.

This mode is suitable when immediate analysis isn’t required. Batch processing operates sequentially, with each step building on the previous one. The first step is the ingesting of a set of data. Once this data is processed and transformed, the resulting output becomes the input for the next step. This step might further filter or aggregate the data. The pipeline continues this step-by-step sequence, with each stage completing its task entirely before the data moves to the next process. This approach ensures a controlled and orderly flow of information through the entire pipeline, maintaining data integrity at each stage.

2. Streaming

With data streaming, data is continuously processed as it’s generated. This method is used when real-time updates are necessary, such as for omnichannel inventory management.

In streaming data processing, individual actions, such as a product sale, are treated as 'events'. These related events are grouped into a continuous 'stream' of data. This approach allows for real-time processing, providing faster data updates compared to batch systems. However, streaming systems can be less reliable due to the potential for message loss or delays in the continuous flow of information.

3. Lambda architecture

This approach combines the batch data processing pipeline and data streaming methods, making it particularly useful for big data systems that handle large volumes of data.

How to build a data pipeline: The main components

As seen above, there are different types of data pipelines, but most of them consist of the following fundamental steps:

1. Data collection

Raw data is extracted from various sources, including datasets, SaaS platforms, APIs, or flat files, and loaded into the data pipeline. It is essential to check that the data load is supported at this stage.

2. Data transformation

Collected data is transformed into an appropriate format for further analysis. This often involves cleansing, standardization, and merging data from other sources to ensure that data quality and integrity are maintained.

3. Data testing

Transformed data undergoes rigorous testing to ensure extraction and transformation accuracy.

4. Data sharing

Data is loaded into a target system, such as a data warehouse or data lake, for consumption and analysis. The target system may be a cloud data warehouse, like Snowflake or Microsoft Azure, or it may be an on-premises data center like MySQL.

5. Automation

Data processing pipelines are typically automated and can be scheduled to run at specific times or when triggered by specific events. This means that data can be managed without the need for manual intervention.

6. Monitoring

Continuous monitoring of data pipelines is crucial for maintaining efficiency and catching errors.

7. Documentation

Proper documentation is also vital as it ensures compliance with data governance standards and legal requirements.

What are the benefits of using data pipelines?

Data pipelines play a crucial role in modern business operations by efficiently managing the flow of information. These systems ensure that data moves smoothly from various sources into data warehouses, where it can be accurately analyzed. This analysis provides valuable business intelligence, enabling organizations to make informed decisions and develop effective strategies. The following sections will explore the specific benefits that data pipelines offer to businesses.

Better data quality

Data pipelines improve data quality through a series of processes. After collection, data undergoes cleansing, testing, and validation before analysis. This systematic approach enhances data reliability and ensures compliance. Furthermore, ongoing testing and monitoring of the pipeline allow for early detection and correction of errors, preventing potential issues downstream in the data processing workflow.

Efficient data integration and processing

Data can also be enriched by combining it with data from other sources. Since data is already of higher quality, this makes it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions. This is generally achieved through the use of machine learning (ML) algorithms. ML is a branch of artificial intelligence (AI) where an algorithm ‘learns’ from a huge amount of data and builds a model that is able to make predictions and classify completely new data based on what it has ‘learned’.

Data visualization tools can also be easily applied to cleansed data to obtain graphics such as charts and plots. This helps businesses understand their data better and is also convenient when communicating business insights with the public or other partners.

Real-time insights for decision-making

The modern business environment demands rapid access to up-to-date information. Data pipelines meet this need by providing a continuous flow of real-time data. This enables businesses to respond swiftly and confidently to market changes. The ability to make timely, data-driven decisions gives companies a competitive edge in an environment where delays in information processing can be a significant disadvantage.

Increased scalability and cost-effectiveness

Data pipelines are designed to be scalable so they can efficiently handle growing data volumes. This scalability is particularly important for big data pipelines, which process massive amounts of information using robust architectures and technologies. As data volumes increase, scalable pipelines maintain performance, meeting the needs of modern data-driven applications and data analytics.

Scalability in data pipelines also offers long-term cost benefits. By accommodating growth and change from the outset, businesses avoid expensive system overhauls or the costs associated with system failures. This foresight in design ensures that the pipeline remains efficient and reliable as the organization's data needs evolve.

What’s the relationship between data pipelines and ETL?

As explained above, data pipelines perform the process of raw data collection, transformation, and relocation. ETL–or ‘extract, transform, load—refers to a specific type of data pipeline.

As the name suggests, ETL pipelines extract data, transform it, and then load it into a data repository. They typically use the batch data processing pipeline method to complete the data transformation before the loading step. In contrast, other data pipelines may run in real time. Using ETL pipelines with cloud-based tools has gained popularity as it allows the handling of data from multiple sources and warehouses.

While ETL pipelines often perform basic data transformations, other data pipelines can execute more complex changes or may not involve any transformations at all. The choice of pipeline depends on specific data processing needs and organizational requirements.

Real-life data pipeline examples

Data pipelines serve various purposes across different industries:

1. E-commerce

E-commerce platforms like Amazon and eBay employ data pipelines to gather comprehensive customer interaction data. This may include purchase history, product views, and customer reviews.

These pipelines collect and transfer this information into customer data platforms (CDPs) for analysis. The resulting insights are used to drive product recommendations and personalized marketing campaigns, creating better customer experiences.

2. Social media

Social media platforms, such as TikTok and Instagram, use data pipelines to collect and analyze user interaction data. This analysis enables these platforms to curate more relevant content for users and deliver targeted advertising, enhancing user engagement and improving ad effectiveness.

3. Fraud or malware detection

Data pipelines play a crucial role in fraud detection, particularly in financial services. For instance, they can process millions of credit card transactions in real time to identify trends and patterns. This information is fed into machine learning models, which can detect anomalies and flag potential fraudulent activities more efficiently and accurately than human analysts.

Easily combine your data pipelines and customer data platform with RudderStack

Businesses need data pipelines to extract the maximum value from their data, but implementing and managing them effectively can be challenging.

RudderStack Event Stream seamlessly integrates with existing data infrastructure, eliminating manual data wrangling. It collects data from various sources, including websites and mobile apps, and routes this information to the data warehouse and downstream tools in real time.

By leveraging RudderStack Event Stream, businesses can streamline data collection, ensure data quality and consistency, enable real-time analytics, and scale data operations efficiently. Our solution complements existing data pipeline strategies by providing a flexible, scalable, and secure platform for managing customer data, working in tandem with data warehouses to maintain control while enabling advanced analytics.

As data-driven insights become increasingly crucial, RudderStack Event Stream helps organizations harness the full potential of their data pipelines, turning customer data into a competitive advantage. To discover how RudderStack can enhance your data pipeline strategy and customer data management, contact our team for more information.