What is an ETL Pipeline?

An ETL pipeline (extract, transform, and load) is a data integration process that collects data from source systems, transforms it into a usable format, and transports it to a data aggregation system such as a data warehouse or data lake. ETL pipelines automate movement and preparation of data between systems, readying data for data analysis, reporting, and business and artificial intelligence applications. Modern ETL pipelines can process batch and streaming data at scale.

There are several reasons that ETL pipelines are important for data processing, consolidation, and transportation.

Data consolidation

Businesses continuously collect data from numerous sources, spanning from SaaS applications to event streams and more. ETL pipelines bring together potentially hundreds of disparate sources, allowing companies to collect the data they need to enrich downstream BI applications and analytics.

Data quality and consistency

ETL data pipelines ensure that raw data moves through a consistent transformation process. In the processing stage, an ETL pipeline will inspect data to enforce validation rules, apply pre-established data cleansing logic, and ensure data adheres to structural schemas. These steps all ensure that data is standardized and accurate when it enters a company’s system.

Analytics enablement

ETL pipelines ensure that all data that enters into cloud data warehouses or downstream business analytics tools is in the right format for analysis. Instead of having numerous formats that would be more difficult to query, ETL data pipelines help to ensure data is ready for analytical engines.

Operational efficiency

ETL pipelines automate a great deal of the data processing and movement stages, reducing the need for data engineers to spend time manually cleaning data. Automating this process also reduces the chance for manual errors that decrease data quality. Both of these factors help to enhance operational efficiency while also freeing up data engineers to focus on other aspects of their work.

An ETL pipeline moves data through a specific order of stages, each of which interacts with the data in a different way.

Connecting to data sources

The first stage of an ETL pipeline is to connect to the sources of raw data. The first ETL process involves using connectors or APIs to establish a reliable connection to these data sources, making sure data flows can consistently enter the data pipeline. Connection needs to take into account the different formats data may have, as well as authenticating data and authorizing the sources to join the pipeline.

Data extraction

Extracting data is how data is moved from the source systems into the pipeline. For example, a batch approach might only incrementally pull new records from the data sources, while a streaming pipeline would continuously be extracting data into the system. This stage often uses Change Data Capture (CDC) see and extract the data that has changed or been added since the last capture, reducing operational burden within each source.

Data transformation

In an ETL pipeline, the data transformation stage happens next, in which a pipeline cleans, standardizes, reshapes, and enriches data. Data transformation aims to change data from its raw state into one that’s compatible with the schema and format a business requires for analysis. Data quality is also key here, with the transformation process looking to remove duplicates, join datasets, and filter out information that is not important to the company. Data integration occurs at this stage. ETL pipelines integrate data by normalizing it into structured data types using data integration tools and scripts.

Data loading

Now that the data is in a standardized format, the ETL pipeline will load it into the storage facilities that your business uses. Most of the time, you load data into data warehouses, data lakes, or a lakehouse platform. The various ways of writing transformed data to the target systems include streaming loads, micro-batching, incremental loads, or even full load movements.

Monitoring and observability

Businesses need to have processes in place to ensure data successfully moves through the ETL pipeline. Monitoring active ETL data pipelines allows businesses to better detect issues, identify failed movements, and trace how data moves across their entire system. Having full visibility over the ETL process also simplifies tracking the lineage of data for audits.

There are several different strategies for moving data through ETL pipelines, each of which handles data processing in a distinct manner and has distinct advantages.

Batch processing ETL

Batch processing moves data in scheduled intervals, with the interval time depending on a company’s requirements. More frequent batch updates will require more operational overhead but also result in fresher data. Batch ETL is most effective for analysis where real-time data isn’t necessary.

Real-time ETL

Real-time ETL is for continuous data processing, moving data into systems with minimal latency. Mission-critical systems, and dashboards and analytics tools that need to show information in real-time will use this form of ETL pipeline.

Stream processing is the implementation of real-time ETL. Whenever a data record is updated or a change occurs, streaming ETL instantly incorporates, transforms, and loads that information to the system. Stream processing is best for any use cases that rely on low-latency data processing, such as financial fraud detection systems. As soon as an event occurs, streaming ETL will compute the change and deliver that new knowledge to the endpoint analytics engine.

Change data capture (CDC)

Change data capture is a strategy where only data that changes is updated. If records are modified in the source system, then CDC locates these changes and transmits them through the ETL pipeline. This approach is especially useful for large databases that have numerous near-fixed segments and others that change more frequently.

When implementing an ETL pipeline, businesses should consider the following aspects.

Data movement volume and velocity

When planning an ETL pipeline, businesses should consider whether the architecture they plan to implement is able to support the volume of data and its processing frequency requirements. Especially if the data sets that you intend to work with are likely to scale, ensuring your ETL data pipelines can manage an increase ahead of time will help avoid last-minute architectural changes in the future.

Data quality requirements

How you intend to standardize, cleanse, and validate data all depend on your internal data handling policy. Create and enforce rules that will produce data that works effectively in your downstream systems. Defining data quality requirements as early as possible will help ensure your entire ETL process is uniform and consistent.

Latency tolerance

Many use cases don’t require real-time data, while others cannot use anything apart from continually updating information. Understanding the specific demands of your downstream environments will let you construct data pipelines that meet those requirements. Determine the acceptable delay between data sources and the target system that works for you ahead of time.

Resource allocation

ETL data pipelines can be resource-intensive, especially if they process a large amount of unstructured data with a low-latency window. Where possible, plan your sources, data stores, compute, and network capacity to ensure your ETL pipelines have the resources they need to effectively deliver data from end to end.

Error handling and recovery

Implement error handling into your ETL data pipelines to alert teams when these errors occur and automatically trigger retry mechanisms. Build fault tolerance and regular validation checkpoints into your ETL pipelines to ensure data arrives in the format and quality you expected.

Security and compliance

There are several different data protection and cybersecurity considerations for building ETL data pipelines to improve data protection and ensure regulatory adherence. Use encryption protocols to protect data in transit, access controls to restrict who can interact with data, and regular audits of your ETL pipelines to make sure you meet regulatory standards.

Businesses can use the following best practices to enhance and maintain their ETL pipelines.

Idempotency

Designing for idempotence, which is where a pipeline operation produces identical results when run once or multiple times, is an important method to create consistency in your ETL pipelines. You can design for idempotency by ensuring atomicity in your transactions, using deterministic transformations, and using logging systems.

Data validation

Validating data at each stage of the ETL pipeline helps to ensure that all the data you integrate is accurate and reliable. This is especially important after ETL pipelines transform data, as checking that the cleansing and standardization has been successful will avoid any problems with low-quality data analytics outputs downstream.

Documentation

Clearly documenting all the data sources you use, how you transform data, and any dependencies throughout the process will help with maintenance and future collaboration. Documentation also helps when moving data from one system to another. Having a well-mapped-out ETL pipeline makes it easier to demonstrate compliance when it comes time for an audit.

AWS offers a range of services to build, run, and manage ETL data pipelines and ETL tools at scale:

AWS Glue is a service to 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.

AWS Step Functions is a service that helps you build and orchestrate distributed applications faster with visual workflows. Transform complex business logic into clear visual workflows through a drag-and-drop interface, enabling faster development and easier troubleshooting.

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