What is a Data Pipeline?

A data pipeline is a series of processing steps to prepare enterprise data for analysis. Organizations have a large volume of data from various sources such as applications, Internet of Things (IoT) devices, and other digital channels. However, raw data is not useful; it must be moved, sorted, filtered, reformatted, and analyzed for business intelligence. A data pipeline includes various technologies to verify, summarize, and find patterns in data to inform business decisions and enhance operational efficiency. Well-organized data pipelines support various big data projects, such as data visualizations, exploratory data analyses, and machine learning tasks.

Data pipelines help you integrate data from different sources and transform that data for analysis. Data pipelines help to remove data silos and make your data analytics more reliable and accurate. Here are some key benefits of a data pipeline.

Improved data quality

Data pipelines use transformation logic to clean and refine raw data and improve its usefulness for end users. Data pipelines standardize formats for fields, such as dates and phone numbers, while checking for input errors. Data pipelines can also remove data redundancy and ensure consistent data quality across the organization.

Efficient data processing

Data engineers perform many repetitive tasks while transforming and loading data. Data pipelines allow these people to automate data transformation tasks and instead focus on creating systems to derive the most useful business insights. Data pipelines also help data engineers process time-sensitive raw data more quickly, reducing time-to-insights.

Comprehensive data integration

A data pipeline abstracts data transformation functions to integrate data sets from disparate sources. The data pipeline can cross-check values of the same data from multiple sources and fix inconsistencies. For example, imagine that two customers in different locations purchase from your ecommerce platform. However, they have two different purchasing currencies. The pipeline can normalize and convert to one currency before sending the data for analytics.

Just like a water pipeline moves water from the reservoir to your taps, a data pipeline moves data from the collection point to storage. A data pipeline extracts data from a source, makes changes, and then saves it in a specific destination. We explain the critical components of data pipeline architecture below.

Diverse data sources

A data source can be an application, a device, a database, a stream, or another source. Disparate sources push data into the pipeline. The pipeline may also extract data by pulling: using an API call, webhook, or data duplication process. You can synchronize data extraction for real-time processing or collect data in scheduled intervals from your data sources.

Transformations

As raw data flows through the pipeline, it transforms to become more useful for business intelligence. Transformations are operations, such as sorting, reformatting, deduplication, verification, and validation, that change data. Your pipeline can filter, summarize, or process data to meet your analysis requirements.

Dependencies

When changes happen sequentially, specific dependencies may exist that reduce the speed of moving data in the pipeline. There are two main types of dependencies: technical and business. For example, if the pipeline has to wait for a central queue to fill up before proceeding, it’s a technical dependency. If the pipeline has to pause until another business unit cross-verifies the data, it’s a business dependency.

Data storage in destinations like data lakes and data warehouses

The endpoint of your data pipeline can be a data warehouse, data lake, or another data storage, which is then linked to your business intelligence or data analysis application. Sometimes the destination is also called a data sink.

An extract, transform, and load (ETL) pipeline is a special type of data pipeline. ETL tools extract or copy raw data from multiple sources and store it in a temporary location called a staging area. They transform data in the staging area and load it into data lakes or warehouses.

Not all data pipelines follow the ETL sequence. Some may extract the data from a source and load it elsewhere without transformations. Other data pipelines follow an extract, load, and transform (ELT) sequence, where they extract and load unstructured data directly into a data lake. They perform transformation changes just in time for analytics.

Batch processing pipelines run infrequently and typically during off-peak hours. They require high computing power for a short period when they run. In contrast, stream processing pipelines run continuously but require low computing power. Instead, they need reliable, low-latency network connections.

Stream processing pipelines

A data stream is a continuous, incremental sequence of small data packets. It usually represents a series of events occurring over a given period. For example, a data stream could show sensor data containing measurements over the last hour. A single action, such as a financial transaction, can also be called an event. Streaming pipelines process a series of events for real-time analytics.

Streaming data requires low latency and high fault tolerance. Your data pipeline should be able to process data even if some data packets are lost or arrive in a different order than expected.

Batch processing pipelines

Batch processing data pipelines process and store data in large volumes or batches. They are suitable for occasional high-volume tasks such as monthly accounting.

In this case, the data pipeline contains a series of sequenced commands, and every command is run on the entire batch of data. The data pipeline gives the output of one command as the input to the following command. After all data transformations are complete, the pipeline loads the entire batch into a cloud data warehouse or another similar data store.

Here are some architectural styles of data pipelines that you may encounter in the literature.

Cloud-native pipelines

Cloud-native data pipelines run entirely in the cloud. Often, organizations choose to scale resource consumption for their cloud configuration so that their data pipelines can always meet demand. Unlike traditional sources, these elastically scaling pipelines can automatically adjust based on consumption, making them useful for variable workloads such as those that feed machine learning tools. Other times, organizations choose fixed cloud resources to run their data pipelines.

In cloud data pipelines, data ingestion, data processing, and data storage are all independent but loosely coupled services. You can run batch processing and streaming data pipelines in the cloud, ingesting both historical datasets and real-time data sources into your cloud data warehouse.

This approach to data ingestion helps to improve operational efficiency by reducing overheads associated with always-on, large-scale data consumption infrastructure. Especially in ELT (extract, transform, load) workflows, you can move large volumes of data for downstream processing, data analysis, machine learning, or data visualization workflows.

Data integration pipelines

A data integration pipeline combines data from multiple data sources into one view. It’s especially useful if you need to enrich data before it becomes useful. By combining disparate systems, you can ensure high data quality and usability.

These typically build out of existing data pipelines, providing new connections. Often, organizations will do this when they want to expand a data pipeline to work with unstructured data or high-volume transactional data from a different domain.

Organizations collect a large volume of information from connected devices, applications, and systems, meaning that one single data pipeline is rarely going to be sufficient. You’ll be working with different types of data, each with different requirements.

Here are some of the main data types you’ll interact with and how to process data in each.

Structured, semi-structured, and unstructured data

Data pipelines work with structured, unstructured, and semi-structured data, each of which typically requires a different strategy.

With structured data, you can map it to a predefined schema, with the data then often loaded into a relational database or data warehouse. Unstructured data, on the other hand, can include anything from objects to text documents and images. You’ll also commonly come across semi-structured data, such as XML or JSON files.

You will need to strategically build data transformation logic for each format to process data effectively. For example, you’ll need to use parsing, metadata extraction, and enrichment for unstructured data to get it ready for analytics. Structured data may only need normalization and aggregation, making it simpler to handle.

You should also use validation checks to keep data quality high throughout.

Streaming data and real-time inputs

Typically, when working with stream processing pipelines, you’ll be ingesting a continuous data stream rather than discrete files. Streaming data could be anything from sensor readings from connected IoT devices to clicks from a website. Acting upon real-time data means that your pipeline must tolerate out-of-order events while still maintaining consistency. You can plan for this by embedding event-time processing and windowing strategies into data processing jobs.

Transactional and historical datasets

Data pipelines often ingest transactional and historical datasets, especially via batch pipelines. Your pipelines should be able to combine these data sets, allowing teams to analyze recent transactional activity and historical performance. You can create robust data pipelines for transactional and historical datasets by using incremental processing and data reconciliation.

Plan out data processing pipelines to ensure that your organization can handle complex data workflows now and into the future. As your organization grows, your data pipeline architecture must scale alongside it. Here are some best practices when building reliable data pipelines for high data volumes.

Design for fault tolerance and reliability

A well-designed data pipeline will continue working even if individual components fail. Especially when working with big data pipelines, a temporary outage or delayed input from time to time is fairly inevitable. However, you need to be able to maintain data accuracy and ensure data integrity despite interruptions.

Reliable pipelines achieve fault tolerance by validating records at every processing stage to isolate failures. This process may look distinct for different data pipelines. For example, in batch processing, you can automatically retry failed steps instead of running an entire job again. These pre-disaster management steps will help preserve data consistency across your entire system.

Scale batch and streaming workloads independently

Batch processing and real-time data processing have distinct requirements for their separate data pipelines. When working with batch data, you’ll need a pipeline that can scale its resources, as they’ll only be fully active for a fraction of the time. Real-time data pipelines may require a fixed-size pipeline, or the need to be able to scale continuously to handle fluctuating-size events, depending on the use case.

Plan for evolving data formats and volumes

The specific data processing steps you follow for your existing formats may not always be the same types of data you need. Designing flexible data transformation logic helps pipelines adapt without requiring complete rearchitecting.

Handling customer data or other sensitive data also means that data security is critical. Enforce access controls, encryption systems, and regularly audit your pipelines to ensure that your data governance supports your data infrastructure throughout.

AWS has a range of services to help at every stage of your data pipelines, including post-pipeline analytics and machine learning capabilities.

Explore our pipeline services:

AWS Glue is a serverless service that makes data integration simpler, faster, and cheaper. You can discover and connect to more than 100 diverse data sources, manage your data in a centralized data catalog, and visually create, run, and monitor data pipelines to load data into your data lakes, data warehouses, and lakehouses.

Amazon Kinesis is a fully managed service that allows you to collect, process, and analyze real-time video and data streams, to derive insights in minutes, not days.

Get started with data pipelines by creating a free AWS account today.