Understanding the ETL Architecture Framework

Extract, transform, load, or “ETL”  is the process by which data is collected from its source, transformed to achieve a desired goal, then delivered to its target destination. For any business hoping to turn its data into value, make data-driven decisions, or keep up with data streaming from the cloud, having an effective ETL architecture in place is essential. 

This article explains what an ETL architecture is, how it works, why it’s important in leveraging data from the cloud, common challenges organizations face, and tips for implementing an efficient, high-performance ETL architecture.

 

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ETL architecture overview

Data in its “raw” form, in other words, the state in which it exists which it is first created or recorded, is usually not sufficient to achieve a business’s intended goals. The data needs to undergo a set of steps, typically called ETL, before it can be put to use. Those steps include the following:

  1. First, data is extracted by a tool that collects it from its original source, like a log file or database.
  2. Next, the data is transformed in any ways necessary for achieving the intended results. Transformation could involve processes like removing inaccurate or missing information from the data in order to ensure data integrity, or converting data from one format (such as server log file) to another (like a CSV file).
  3. Finally, once the data is ready to be used, it is loaded into its target destination, such as a cloud data warehouse or an application that will use the data to make an automated decision.

For example, consider an online retailer that wants to use sales data to make product recommendations for visitors to its website. To do this, the business must first extract only the relevant data from the database that it uses to record transactions (which, in addition to information about product trends, might include other, non-relevant information, such as which operating system a customer used for each transaction). 

Then, the data must be transformed by removing missing entries and converted into a format that can be used by a cloud-based product recommendation engine. Finally, the prepared data is loaded into the recommendation engine, which uses the information to suggest products to website visitors.

Although the acronym ETL implies that there are only three main steps in the ETL process, it may make more sense to think of ETL architectures as being longer and more complicated than this, since each of the three main steps often requires multiple processes and vary based on the intended target destination. This is especially true of the transformation step. Not only does this step include running data quality checks and addressing data quality errors, it changes based on whether it occurs on a staging site or in a cloud data warehouse.

These processes, which are essential for ensuring that data can be used to achieve its intended goal, may require repeated passes through each data set.

 

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Building an ETL architecture

ETL processes are complex and establishing an ETL architecture that meets your business’s needs requires several distinct steps.

  1. Identify your business needs in setting up an ETL architecture. What is the ETL architecture’s goal? Is it to improve IT processes, increase customer engagement, optimize sales, or something else?
  2. Document data sources by determining which data your ETL architecture must support, and where that data is located.
  3. Identify your target destination in order to create an efficient ETL architecture relevant to your data’s journey from source to endpoint. Choosing between a cloud data warehouse, an on-premises data warehouse, or legacy database will adjust the necessary steps and execution in your ETL architecture.
  4. Decide on batch vs. streaming ETL. Both approaches are compatible with ETL architectures; the one you choose should reflect your business goals and the type of data you are working with. For example, if your goal is to detect cyberattacks using real-time Web server data, streaming processing makes more sense. If instead you aim to interpret sales trends in order to plan monthly marketing campaigns, batch processing may be a better fit.
  5. Establish data quality and health checks by determining which data integrity problems are common within your data set, and including tools and processes in your ETL architecture to address them.
  6. Plan regular maintenance and improvements. No ETL architecture is perfect. When you build your ETL architecture, be sure to make a plan for reviewing and improving it periodically to ensure that it is always as closely aligned as possible with your business goals and data needs.

Challenges with designing an ETL framework

ETL architectures are complex, and businesses may face several challenges when implementing them:

  • Data integrity: Your ETL architecture is only as successful as the quality of the data that passes through it. Because many data sources contain data quality errors, including data integrity tools that can address them as part of the ETL process is crucial.
  • Performance: An effective ETL architecture not only performs ETL, but performs it quickly and efficiently. Optimizing ETL performance requires tools and infrastructure that can complete ETL operations quickly, while using resources efficiently.
  • Data source compatibility: You may not always know before you design your ETL architecture which types of data sources it needs to support. Data compatibility can therefore become a challenge. You can address it by choosing data extraction and transformation tools that support a broad range of data types and sources.
  • Data privacy and security: Keeping data private and secure during the ETL process is paramount. Here again, it’s important to have the tools and infrastructure that can keep data secure as it undergoes the ETL process. 
  • Adaptability: The demands placed on your ETL architecture are likely to change. It may need to support new types of data sources, meet new performance goals, or handle new data integrity challenges, for example. This is why it’s important to build an ETL architecture that can evolve and adapt along with your needs, by choosing tools that are flexible and scalable.

 

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Automating your ETL pipeline with the cloud

The ETL process can be performed manually or automatically. However, except in cases where the data you are working with is so unusual that it requires manual processing, an automated ETL architecture is the preferable approach. Automation helps you achieve the fastest and most consistent ETL results while optimizing cost, automation is critical.

Although setting up a fully automated ETL architecture may seem daunting, Talend Data Fabric makes automated ETL easy with cloud-based data management tools. By delivering a complete suite of cloud-based apps focused on data collection, integrity, transformation and analytics, Talend Data Fabric lets you set up an ETL architecture that supports virtually any type of data source in just minutes. By leveraging machine learning, enabling integration with a myriad of cloud data warehouse destinations, and ensuring scalability, Talend Data Fabric provides companies with the means to quickly run analytics using their preferred business intelligence tools.

Try Talend Data Fabric today to glean insights from data you can trust at the speed of your business.



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| Last Updated: September 16th, 2019