ETL (extract, transform, load) is a core component of the data integration process. It's the backbone of modern business intelligence and analytics workloads, efficiently transporting and transforming data between source and target.
But it's one thing to know how ETL works, and quite another to actually build a powerful ETL architecture for your organization. Just like construction projects in the real world, ETL architecture needs to follow a plan and have a solid foundation to ensure that your workflow will fit your current needs and scale with you as you grow.
What is ETL architecture exactly, and what are the components of a successful ETL architecture? In this article, we’ll perform a deep dive into the question of ETL architecture, so that you can get started building rich, streamlined data integration pipelines that help you answer tough questions for your business.
Table of Contents
What is ETL Architecture?
ETL architecture is a “blueprint” for how your ETL processes will execute from start to finish. This includes a description of how data will flow from the source to target locations, as well as a list of the transformations that will be enacted on this data.
Every data integration expert is familiar with the three basic steps of ETL:
- Extract: First, data is extracted from a variety of internal and external sources. These data sources may include CSV, JSON, and XML/HTML files; Excel spreadsheets; relational and non-relational databases; and data from websites obtained using APIs or web scraping tools.
- Transform: Second, data is transformed in order to make it suitable for, and compatible with, the schema of the target data warehouse. This may include: cleansing the data to remove duplicate or out-of-date entries; converting data from one format to another; joining and aggregating data; sorting and ordering data; and many more processes.
- Load: Third, and finally, data is loaded into the target data warehouse. Once inside the data warehouse, data can be efficiently queried and used for business intelligence and analytics.
However, this is only a high-level description of the three steps of ETL. In actuality, each of these steps may contain many different subprocesses and subroutines.
ETL architecture often consists of a diagram that outlines the flow of information from start to finish in your ETL pipeline. Data originates from sources files and databases, where it's taken up and entered into the ETL transformation engine. From here, the data may be loaded into any or all of the following locations:
- Landing area: The landing area is where data first arrives after being extracted from a source location. Few, if any, transformations are applied to the data in the landing area. If you are performing ETL batch processing, the landing area may store multiple batches of data before moving it through the ETL pipeline.
- Staging area: The staging area is a temporary, intermediate location for performing ETL transformations. This area may take the form of a relational database, or binary or text files. The transformations performed in this area include joining and consolidating multiple data sources, cleansing and validating source data, and standardizing and aligning information.
- Data warehouse area: The data warehouse area is the final destination for data in an ETL pipeline. From here, your data can easily be queried and analyzed in order to obtain valuable insights and make better business decisions. This area may consist of an enterprise data warehouse spanning the entire organization, or a data mart that has been set up to serve the needs of a single team or department.
What to Consider When Designing an ETL Architecture
ETL is an abstract concept that describes how your enterprise data makes the journey from the source location to the target data warehouse. But how do you turn the three steps of ETL into a practical ETL architecture implementation that powers your BI and analytics projects?
In this section, we’ll discuss three things to keep in mind while you design and implement your ETL architecture.
1. How will you ensure high-quality data?
Most software developers are familiar with the concept of GIGO: garbage in, garbage out. Your ETL architecture is only as good as the quality of the data that you feed into it. If your input data is incorrect or outdated, the insights you obtain from this information will be limited at best, and flat-out wrong at worst.
To ensure that you have high-quality data in your ETL pipelines, perform data validation and quality checks at regular intervals. Some useful business rules for ensuring data quality in ETL architecture are:
- Verify that the source and target databases have compatible schemas, and transform the data if necessary to fit this target schema.
- Check for the presence of problematic values such as NULL, 0, or -1 that can throw off your BI and analytics workloads and introduce errors into the final report.
- Identify duplicate records by comparing unique identifiers, and eliminate any such duplicates.
- Scan for anomalies, such as records that have been created in the future or too far in the past.
2. What are your data sources and targets?
ETL architecture defines the elements of your ETL workflow, as well as the relationships between those elements. For the best chance at obtaining cutting-edge insights, you need to identify the data sources that are right for your ETL architecture, as well as the target location where you will store this extracted data.
Taking the time to craft a suitable ETL architecture will pay dividends in the long run. According to TDWI’s BI Benchmark Report, for example, it takes an average of 7.1 weeks to add a new data warehouse source once the system has already been built.
The “right” data sources will highly depend on the particular use case for a given ETL workflow. For example, if you want to run BI queries about your sales and marketing efforts, you’ll need to extract information from sources such as order entry systems, CRM software, and advertising platforms such as Google AdWords and Facebook Ads.
Almost as important is the question of where you will store this data. Will it be in a structured data warehouse or data mart, an unstructured data lake, or a combination of both? Large organizations often have multiple targets for their ETL processes, efficiently routing data to the appropriate locations depending on the use case.
3. Batch ETL or streaming ETL?
Traditionally, ETL tools have handled data using batch processing. This means that data enters the ETL pipeline in batches rather than instantaneously. These batches usually correspond to a time period ranging from several hours to a full day. New batches are created according to a defined schedule, or when the amount of data in the batch reaches a given threshold.
However, many ETL tools now offer streaming ETL processes (also known as “event-driven ETL”) as an alternative to batch processing. These solutions can handle data as soon as it arrives in the ETL pipeline in real-time, making it available for querying and analysis almost instantaneously.
Both streaming ETL and batch ETL have their upsides. Streaming ETL is best suited for use cases where data is constantly arriving in the pipeline, or where having fresh, up-to-the-minute data is essential. Conversely, batch ETL is best suited for use cases where some degree of data latency is permissible, or where you need to perform operations over large quantities of data at the same time.
There is a lot to consider when building an ETL architecture for your data integration workflows. With the spiraling complexity of ETL architecture, it’s no surprise that more and more organizations are choosing a low-code ETL platform like Xplenty.
Xplenty is a powerful, feature-rich, low-code ETL platform, with more than 100 built-in integrations to help get your data pipelines flowing as soon as possible. Ready to get started building long-lasting, structurally sound ETL architecture? Get in touch with our team today and experience the Xplenty platform for yourself.