What is a data pipeline?

Before data flows into a data repository, it typically undergoes data processing and transformation. This can include filtering, masking, standardization and aggregations, which ensure data is clean, consistent and suitable for downstream use cases.

These steps are particularly important when a dataset’s destination is a relational (SQL) database. This type of data repository has a defined schema which requires alignment—that is, matching data columns and types—to update existing data with new data.

As the name suggests, data pipelines act as the “piping” for modern data-driven applications, including business intelligence dashboards, analytics platforms and machine learning workflows. Data can be sourced from a wide range of systems, including APIs, relational databases, NoSQL databases, applications and file-based storage systems.

During sourcing, data lineage is often tracked to document the relationship between enterprise data in various business and IT applications, for example, where data is currently and how it’s stored in an environment, such as on-premises, in a data lake or in a data warehouse.

Data preparation tasks usually fall on the shoulders of data scientists or data engineers, who structure the data to meet the needs of the business use cases and handle huge amounts of data. Once the data has been appropriately filtered, merged and summarized, it can then be stored and surfaced for use.

Well-organized data pipelines provide the foundation for a range of data projects; this can include exploratory data analyses, data visualizations and machine learning tasks.

There are several types of data pipelines, each appropriate for specific tasks on specific platforms. Common types include

• Batch processing pipelines
• Streaming data pipelines
• Data integration pipelines
• Cloud-native pipelines

The development of batch processing was a critical step in building data infrastructures that were reliable and scalable. In 2004, MapReduce, a batch processing algorithm, was patented and then subsequently integrated into open-source systems, such as Hadoop, CouchDB and MongoDB.

As the name implies, batch processing loads “batches” of data into a repository during set time intervals, which are typically scheduled during off-peak business hours. This way, other workloads aren’t impacted as batch processing jobs tend to work with large volumes of data, which can tax the overall system.

Batch processing is usually the optimal data pipeline when there isn’t an immediate need to analyze a specific dataset (for example, monthly accounting), and it is more associated with the ETL data integration process, which stands for “extract, transform, and load.”

Batch processing jobs form a workflow of sequenced commands, where the output of one command becomes the input of the next command. For example, one command might kick off data ingestion, the next command may trigger filtering of specific columns, and the subsequent command may handle aggregation. This series of commands will continue until the data has been completely transformed and written to the repository.

Unlike batch processing, streaming data pipelines—also known as event-driven architectures—continuously process events generated by various sources, such as sensors or user interactions within an application.

These events are processed and analyzed, and then either stored in databases or sent downstream for further analysis. In many architectures, these events are organized into event streams that can be consumed by multiple applications and services in real time.

Streaming data is used when data must be continuously updated. For example, apps or point-of-sale systems need real-time data to update inventory and sales history of their products; that way, sellers can inform consumers if a product is in stock or not. A single action, such as a product sale, is considered an “event,” and related events, such as adding an item to checkout, are typically grouped together as a “topic” or “stream.”

These events are then transported via messaging systems or message brokers, such as the open-source offering, Apache Kafka. Streaming pipelines are also commonly used alongside change data capture (CDC) tools, which capture changes made to source databases and publish them as events for downstream systems to process.

Since data events are processed shortly after occurring, stream processing systems have lower latency than batch systems, but aren’t considered as reliable as batch processing systems as messages can be unintentionally dropped or spend a long time in queue. Message brokers help to address this concern through acknowledgements, where a consumer confirms processing of the message to the broker to remove it from the queue.

Data integration pipelines concentrate on merging data from multiple sources into a single unified view. These pipelines often involve extract, transform, load (ETL) processes that clean, enrich or otherwise modify raw data before storing it in a centralized repository such as a data warehouse or data lake.

Data integration pipelines are essential for handling disparate systems that generate incompatible formats or structures. For example, a connection can be added to Amazon S3 (Amazon Simple Storage Service)—a service that is offered by Amazon Web Services (AWS) that provides object storage through a web service interface.

A modern data platform includes a suite of cloud-first, cloud-native software products that enable the collection, cleansing, transformation and analysis of an organization’s data to help improve decision making.

Today’s data pipelines have become increasingly complex and important for data analytics and making data-driven decisions. A modern data platform builds trust in this data by ingesting, storing, processing and transforming it in a way that ensures accurate and timely information, reduces data silos, enables self-service and improves data quality.

Three core stages make up the architecture of a data pipeline:

1. Data ingestion
2. Data transformation
3. Data storage

The terms data pipeline and ETL pipeline are often used interchangeably, but they are not the same thing. An ETL pipeline is a specific type of data pipeline that follows a predefined process for moving and preparing data. While all ETL pipelines are data pipelines, not all data pipelines are ETL pipelines.

They are distinguished by three key differences:

As the abbreviation implies, ETL pipelines extract data, transform data, and then load and store data in a data repository. Not all data pipelines need to follow this sequence.

In fact, ELT (extract, load, transform) pipelines have become more popular with the advent of cloud-native tools where data can be generated and stored across multiple sources and platforms. While data ingestion still occurs first with this type of pipeline, any transformations are applied after the data has been loaded into the cloud-based data warehouse.

ETL pipelines are traditionally associated with batch processing, where data is collected and processed at scheduled intervals. However, the broader category of data pipelines also includes stream processing, where data is ingested, processed and delivered continuously in near real time. As a result, data pipelines can support a wider range of use cases, from nightly reporting jobs to real-time analytics and event-driven applications.

Another key difference is that data transformation is a defining component of ETL pipelines. Data is expected to be cleaned, enriched, standardized or otherwise modified before it reaches its destination.

By contrast, a data pipeline may simply move data from one system to another without performing transformations. While this approach is less common—especially in analytics environments—it highlights the flexibility of data pipelines as a broader architectural concept.

As big data continues to grow, data management becomes an ever-increasing priority. While data pipelines serve various functions, the following are for business applications:

• Fraud detection: Data pipelines enable organizations to collect and process transaction, user activity, and behavioral data from multiple sources in near real time. By continuously moving and transforming data, pipelines help fraud detection systems identify suspicious patterns, flag anomalies, and trigger alerts before fraudulent activity can cause significant damage.
  
  
• Exploratory data analysis: Data pipelines support exploratory data analysis by aggregating, cleansing, and preparing data from disparate sources for investigation. By ensuring analysts and data scientists have access to accurate, up-to-date datasets, pipelines make it easier to discover patterns, identify anomalies, test hypotheses, and uncover insights that inform business decisions.
  
  
• Data visualizations: Data pipelines provide the foundation for dashboards, reports, and other data visualizations by delivering consistent, reliable data to business intelligence and analytics platforms. This enables organizations to create charts, graphs, infographics and other visual representations that help stakeholders understand trends, monitor performance, and make data-driven decisions.
  
  
• Machine learning: Data pipelines play a critical role in machine learning workflows by collecting, transforming, and delivering high-quality data to machine learning models for training and inference. Through the use of statistical methods, these models can make classifications or predictions, uncovering valuable insights and supporting data-driven decision-making.
  
  
• Data observability: Data pipelines help organizations implement data observability by continuously monitoring data quality, freshness, lineage, and reliability throughout the data lifecycle. Observability tools use pipeline-generated metadata and metrics to detect anomalies, identify data issues, and alert teams to potential problems before they impact downstream analytics, reporting or business operations.

When designing and operating data pipelines, several key factors must be considered to ensure reliability, performance and security at scale:

Data pipeline automation uses software to orchestrate and manage the movement, transformation and delivery of data.

Automated data pipelines streamline key data management steps and often incorporate monitoring, testing and governance capabilities. Pipeline automation is beginning to evolve into agentic, AI-supported systems with self-adapting and self-healing capabilities. These approaches can diagnose issues and optimize execution using contextual signals instead of static rules.

Without these capabilities, traditional data pipelines can struggle with rising data volumes, fragmented environments and the demands of real-time analytics and artificial intelligence.