ETL: Meaning and function explained

Have you been wondering what ETL stands for—and what it does? ETL software has probably been mentioned in business meetings, or even been the subject of your company’s new data warehousing project or compliance measures. It’s a solution that’s becoming increasingly well known and important in today’s data-driven business environment.

That’s why we’ve written this short, handy guide. After reading this, you’ll know all about both ETL and the relatively recent phenomena of reverse ETL. Armed with this, you’ll be an ETL expert in no time at all.

What is ETL?

ETL stands for extract, transform, and load. Essentially, an ETL pipeline extracts data from a number of different source systems and then transforms the data by applying calculations to store the data in the data warehouse system. This is opposed to ELT, which executes these events in a different sequence. It sounds technical, but it’s nothing new. It’s a term that’s been around since the 1970s.

For example, data could flow from several databases, spreadsheets, and other data sources into the ETL then be stored in the data warehouse system ready for use.

ETL is critical to business intelligence. Without it, the company would fail to make timely, accurate, practical, and efficient business decisions. This is because reports and analyses are pulled from the summary, meta, and raw ETL data stored in the data warehouse.

How does ETL work?

Extraction

The first stage of ETL (or the “E” part, otherwise known as extraction), is where data is extracted from a source. The source could be anything, some examples include Google AdWords, Iterable (email marketing provider), Shopify, customer relationship management (CRM) software, like Salesforce, or other business tools where relevant data is created and managed.

The information is then moved into the staging area. The staging area is a buffer between the data warehouse and the source data. This is because data can be coming from several sources and may be in various formats and levels of data cleanliness. If data were loaded directly to the warehouse, it could result in incomprehensible or corrupted data.

The staging area organizes and cleans the data for the user. A key challenge in this stage of the process is how your data warehouse handles structured and unstructured data. Unstructured data like web pages, email, etc., can be hard to extract without the correct tool. This means you will most likely need a tool with good unstructured data deciphering capabilities to ensure that no issues occur down the line.

Transformation

The transformation stage typically has three parts. This means that the data that was extracted in the last stage will be cleaned and organized and then moved into a single system format. The goal of the “Transform” stage is to improve overall data quality.

All of the data from multi-source systems will be normalized and transformed into a single system format. This step is critical to ensure that data quality is high and that the data remains compliant with relevant rules and regulations.

Data also goes through a clean filter during the transformation stage, involving the joining, sorting, splitting, duplication, and even summarization of data depending on how the end user or system will use it.

Without the transformation stage working correctly, data can become inaccurate and confused and can cause serious issues if not transformed correctly. This is especially true of unstructured data that, even when coming from the same source as structured data, can change format quickly without notice.

Loading

Once the data has been extracted and transformed, it’s time to move to the loading phase of the process. This means that the data has now been loaded into the data warehouse.

Depending on your business needs, data can be loaded into the warehouse in batches or all at once. Whether the former or the latter is performed depends on the data source or ETL tools in use.

Data is regularly loaded into the data warehouse at varying intervals. The rate, speed, and frequency of loads will depend on the requirements and vary according to what system you use. At the end of the loading phase, the data should be ready for use by the multiple data marts i.e., sales, purchasing, operations, marketing, inventory, etc. This allows it to be loaded into user analysis, reporting or data mining reports and tools.

What is reverse ETL?

Reverse ETL is the process of sending data in the data warehouse to different systems. Typically, when we talk about ETL data, we see the data warehouse as the final destination. However, with reverse ETL, the data can be copied from the data warehouse and sent to different systems or programs that the company or organization uses.

The purpose of reverse ETL is to feed data back into operational systems so that users can make data-driven decisions in their everyday workflows, even helping to enable automation of several different types of tasks.

Get your data to the right places with Cloud Connect

With the right reverse ETL solution in place, your teams can access critical, granular data in your warehouse and feed it to downstream tools via data streaming without having to move the data around.

Cloud Connect from Lytics allows your IT and data teams to enable highly personalized experiences for customers. Easily build detailed customer segments and target audiences and sync them with over 80 destination tools. Cloud Connect allows you to make your data actionable, and generate customer insights that will affect your strategy.

To get a deeper look into Cloud Connect™ watch our explainer video below or read more in our introductory blog.

Or try it for yourself. Try Cloud Connect free and test your first segments today.