ETL (extract, transform, load) is a core component of the data integration process. It's the backbone of modern business intelligence (BI) and analytics workloads, transporting and transforming data between source and target.

But it's one thing to know how ETL works, and quite another to build a powerful ETL architecture for your organization. Just like construction projects in the real world (or even Lego projects!), ETL architecture needs to follow a plan and have a solid foundation that supports your long-term goal. 

What is ETL architecture exactly, and what are the components of a successful ETL architecture? In this article, take a deep dive into ETL architecture and start building rich, streamlined data pipelines today.

Table of Contents 

What is ETL Architecture, Anyway?

ETL architecture is a “blueprint” for how your ETL processes will execute from start to finish. It describes how data will flow from the source to target locations, as well as a list of the transformations you will execute when moving this data.

Every data integration expert is familiar with the three basic steps of ETL:

  • Extract: When an ETL tool extracts data from an internal or external source. These sources may include CSV, JSON, and XML/HTML files; CRM and ERP systems; Excel spreadsheets; relational and non-relational databases; and data from websites obtained using APIs or web scraping tools.
  • Transform:  When an ETL tool transforms data to make it suitable for, and compatible with, the schema of the target data warehouse (or data lake). This may include cleansing the data to remove duplicate or out-of-date entries; converting data from one format to another; joining and aggregating data; sorting and ordering data; and many more processes.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: When an ETL tool loads data to the target data warehouse. Once inside the data warehouse, you can query that data for BI and analytics.

Each of these steps may contain many subprocesses and subroutines.

ETL architecture often comprises a diagram that outlines the flow of information from start to finish in your ETL pipeline. Data originates from sources files and databases before entering the ETL transformation engine. From here, you can load the data 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. You will apply few, if any, transformations to the data in the landing area. If you perform 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, you can analyze data to get valuable insights and make better business decisions. This area may comprise an enterprise data warehouse spanning the entire organization or a data mart that serves the needs of a single team or department. optimizes the ETL process from start to finish, making it a crucial component of your data integration strategy. Schedule a demo now to learn more.

What to Consider When Designing 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. 

Consider these questions when designing your architecture:

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.

To ensure you have high-quality data in your ETL pipelines, perform data validation and quality checks. Some useful business rules for ensuring data quality in ETL architecture are:

  • Verify that the source and target databases have compatible schemas. Transform the data if necessary to fit a target schema.
  • Check for 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 or data redundancy by comparing unique identifiers and eliminate any such errors.
  • Scan for anomalies, such as records created in the future or too far in the past.

What are your data sources and targets?

Taking the time to craft a suitable ETL architecture will pay dividends in the long run. 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 live 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 right locations depending on the use case.

Batch ETL or streaming ETL?

Traditionally, ETL tools have handled data using batch processing. That means data enters the ETL pipeline in batches rather than instantaneously. These batches usually correspond to a period ranging from several hours to a full day. You can create new batches according to a defined schedule or when the amount of data in that 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 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.

What about data governance?

One of the biggest challenges of building ETL architecture in 2021 is the ever-growing number of data governance frameworks that dictate how you process data in ETL workloads. Legislation like GDPR, CCPA and, if you work for a healthcare organization, HIPAA can all influence how you create your architecture. That's because these frameworks stipulate large penalties for non-compliance with data protection standards. (For example, processing a customer's data without their permission.)

Using the right ETL tools can simplify data governance. The best ones transform data so it complies with any data protection legislation that pertains to your industry or location.

How Helps You Optimize Your ETL Architecture is your go-to platform for ETL. This powerful tool extracts data from a huge range of internal and external sources, transforms that data into the correct format for big data analytics, and then loads the data to a final destination like a warehouse or lake. The result? You can build streamlined data pipelines without the fuss. 

Even if you lack a sizeable data engineering team or don't have advanced programming skills, you can still build custom data pipelines based on your ETL architecture requirements. That's because comes with a broad range of pre-built data integrations that require little or no code. You can move data from one location to another without breaking a sweat.

Read more: What is No-Code?

There are other benefits.

  • ensures high-quality data. It cleanses, corrects, and transforms data to the correct format for analytics in a few simple steps. 
  • helps you define your data sources and targets. The platform comes with simple workflow creation that makes it easy to move data from one location to another, so you can spend more time analyzing data and less time worrying about whether data will flow from source to target.
  • helps you adhere to data governance frameworks and avoid penalties for non-compliance.

With, you get access to top-tier customer service, including the opportunity to communicate with data engineers who live and breathe ETL. Plus, the company's super-simple pricing model only charges you for the connectors you use and not the amount of data you consume, which can work out considerably cheaper for many businesses. Other perks include Salesforce-to-Salesforce integration and enhanced security.

Read more: Top 7 Features

If you are thinking about building ETL architecture in your data-driven organization, consider a tool like This data management solution drives your ETL workflows with an incredible range of features. Schedule a demo now to discover more.