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Extract, Transform, Load (ETL) is an important process for understanding business data. All online businesses collect data about customers and their interactions with their websites.
Typically, a business will have useful data in multiple systems like a CRM, a billing software, a customer support software, and so on. Being able to gather all of these data in the same place to make sense of them is key to help improve one’s business.
That’s when ETL comes into play. ETL’s goal is to help businesses gather all of their business data in the same place and make sense of it. Ultimately, the objective is to make the business more successful.
For example, you can track how many users land on your website after clicking on a specific ad. Let’s say you have two ads online from different advertising platforms (Google ads and Facebooks for example). An ETL process brings data from both platforms into the same place, called a data warehouse. A data warehouse is just a type of database that’s optimized for storing big volumes of data and analytics.
Once you’ve gathered your ads data into your data warehouse, you can compare which one of the two ads is bringing more traffic. This will help you optimize your campaigns, understand what works and what doesn’t and ultimately get more customers for a lower advertising cost.
Or, you can find out how much time users spend browsing a specific section of your blog. ETL helps you create a well-organized data set. So you can easily understand which type of blog post keeps users entertained for a longer time. This knowledge will be useful the next time you set up your editorial calendar.
ETL comes in handy when you need to retrieve metrics from a legacy system and convert them into an up-to-date format.
Modern businesses tend to have tons of data sources. Even a simple blog collects data from Google AdSense, Google Analytics, affiliate links, and so on. Often, all these sources make it hard to really understand data. Let alone finding actionable insights to help your business grow.
That's why ETL is so important. It gathers data in a single place and makes it easy to analyze. This way, all metrics are accessible to everyone at your company. The better you understand your data, the more you can drive growth.
In this article, we’ll explain in more detail how the ETL process works. You'll also discover a series of tools you can use for your business.
What does ETL mean?
Again, ETL stands for Extract, Transform, Load. This process first extracts raw data from multiple sources. The sources could be databases or marketing tools you installed on your site.
Then, ETL transforms the data into a format that’s easily understandable by humans. Imagine a simple dataset in Excel with all of your customers and a bunch of columns with customer attributes like email address, phone numbers, etc. Typically, you would use a Business Intelligence tool to build dashboards and visualize this data to gain business insights.
When you can actually read and understand your data, you can use it to determine what works for your business, and what doesn’t. For example, you can see which social media platform is bringing more traffic to your website. You’ll probably decide to invest more in that platform.
The last step is loading data into a data warehouse of your choice. To sum it up, ETL is a set of three processes (Extract, Transform, Load).
How does the ETL process work?
Let’s explain ETL’s three steps in more detail:
Extracting
Most businesses collect data from various sources. Let’s take the example of an online store. It likely gets metrics about visitor behavior from Google Analytics. And Google Ads provides data about online campaigns.
Add a few other marketing apps, and keeping tabs on these metrics becomes difficult. And most businesses use lots of apps. Think about payment processors like PayPal and Stripe, and invoicing software like QuickBooks. Most businesses also use CRMs to track communications with their customers. All of these apps collect data.
An ETL tool automatically retrieves data from all these apps.
Transforming
Let’s say your ETL tool combines Google Analytics and Google Ads data for you. There will be redundant data. Google Analytics might have already tracked website visits from your Ads campaigns.
During the transforming phase, ETL removes redundant metrics. It also standardizes the data format for more clarity. Maybe your marketing tools use different wording to express the sex of your visitors. Let’s say a tool uses “female” and the other uses “F”. In the final data, you’ll only read “female”.
ETL also removes inconsistencies and anomalies, and corrects missing values.
Loading
Loading brings data from different sources into a single data warehouse. As mentioned above, a data warehouse is a specific type of database optimized for analyzing and understanding your metrics.
Let’s continue with our example. Metrics from Google Ads, Analytics, and five other apps would be in the same data warehouse.
This step can happen in two ways: full loading or incremental loading. Full loading exports all data into a database of your choice.
Incremental loading only loads new data. It compares the data you want to load with what you already have in the target database. Then, it only uploads what’s new without overwriting existing metrics.
Every time you perform full loading, you get an entire new record. That’s why, in most cases, you’re better off with incremental loading. It saves you tons of memory space, and the reports are easier to keep up with.
What's the difference between ETL and ELT?
ETL transforms and cleans up your data before loading it into the target database. ELT doesn’t. You’ll need the database itself to remove redundant values, make the data intelligible, and so on.
Most cloud-based data warehouses allow doing the transformation process inside the data warehouse. For example, Microsoft Azure, Google BigQuery, and Amazon Redshift do.
The pro of ELT is that you always have the raw data on hand. Raw data has not undergone the transformation process yet. Accessing it might be useful in case of data processing errors or inconsistencies.
Let’s pretend you’re reviewing metrics about your e-commerce traffic. You notice a sudden stop of pageviews after a certain day. You’re 100% sure you have ads online, so it can’t be a traffic drop. With access to raw data, your developers can find out what’s wrong.
Historically, most businesses would do ETL because it would reduce the volume of data inside the data warehouse. But as data storage cost is trending toward zero, then the best practice is to gather all of your raw data inside the warehouse and then transform it there.
ETL tools: what tools to consider and how to decide
An ETL pipeline is the set of processes that make ETL happen: extracting, processing, and loading. Back in the day, if you wanted to build one, you had to call your engineers and developers. They would need to write custom code to create connections to each software, and so on.
Today, there are many automated ETL tools on the market. They make the job much faster and cheaper. Some of them do all the work for you, even if you don’t know how to code. Others require a little intervention from your developers. Still, you don’t have to build the whole pipeline from scratch.
Here are some tools that make ETL much less intimidating:
1. Stitch
Stitch is compatible with over 130 data collection apps. Anyone can get set up in minutes without writing a single line of code.
The tool has advanced encryption and security features for your peace of mind. As a big plus, Stitch offers compliance with data security regulations, including GDPR.
Enterprise customers get full onboarding assistance. Stitch’s experts will perform a custom assessment to help your business make the most out of the ETL tool. Phone or chat support is available, based on the type of subscription you have.
2. AWS Glue
AWS Glue pulls data from Amazon Web Services applications. It features a simple wizard-style interface. You can use the tool code-free, or create Glue Jobs using the Spark programming language.
The tool is serverless, so there’s no need to have huge IT resources available. Access and browse your data easily in the AWS Glue Data Catalog.
For the transformation step, there's AWS Glue DataBrew. It offers over 250 code-free data cleansing processes. With a few clicks, you can standardize the format of data from different sources. So you don’t see both “female” and “F” values in the same report, which is pretty confusing. There are also processes to correct anomalies and more.
3. Fivetran
Fivetran is a fully-managed solution. It does all the ETL work for you and updates automatically. 99,9% uptime and 24/7 customer support make for a worry-free ETL experience.
After writing data into your target destination, Fivetran erases it from anywhere else. This makes things much easier to manage in case of a data breach. You only have to worry about the destination data warehouse, not the sources or other servers.
The tool anonymizes all sensitive information. A role-based access system gives you full control over who can access data at your company.
4. Airbyte
Airbyte promises you’ll get your ETL pipeline set up in five minutes. Appealing, right? Such a short setup time is possible thanks to pre-built connections with over 140 apps. If you need an app that’s not on the list, implementing it only takes 2 hours of coding. Your IT team can use any programming language of their choice.
This tool supports both full and incremental loading. It’s open-source, which means that your developers can customize it however you like. The sky's the limit! A real-time error logging system helps your team out with troubleshooting. Airbyte also notifies you immediately when data fails to sync. This prevents data loss.
The pricing is based on compute time, so it’s fairly predictable.
5. Dataddo
This is another code-free ETL solution. Dataddo’s team also takes care of all maintenance work for you. The tool is compatible with most data sources and destinations. If you have compatibility issues, you can always ask the team for a custom connector.
Even non-technical personnel can easily analyze data from Dataddo’s dashboard. Filters let you select which metrics you want to view. Filtering out data you don’t need makes your reports easier to digest.
Adding or modifying data sources and destinations just requires a few clicks. The tool offers strong encryption and is GDPR-compliant.
How to choose the right tool
Compatibility with your apps you use is the first thing you want to consider. Also, keep in mind that you might want to use different apps in the future. It’s nice to have the option to set up manual app integration. So if you ever want to use an unsupported app, a developer can make it work for you.
Your staff’s IT literacy is critical, too. If you don’t have an IT team and aren’t ready to hire one, a code-free ETL tool is perfectly fine. Yet, custom platforms provide extra options you may consider as your business grows.
Lastly, better to be safe than sorry when it comes to data security. You should be fine with the tools mentioned above. Make sure that the one you choose complies with all local regulations in your area.
Why you'd want to implement an ETL process in your company
First, because data is important! Imagine you can track everything a customer does on your website. You could discover which behaviors lead to abandoned carts. And what type of customers typically leave the site without finalizing the purchase. Only well-organized data allows you to do this.
After you gather this information, you can act on it. Maybe you notice several customers abandoning the checkout process during a certain step. Now you know that this specific step is difficult or confusing. You can ask your IT team to make it more user-friendly.
Secondly, after the ETL process, you can perform reverse ETL. It may sound counterintuitive to do a process, then the reverse. Yet, reverse ETL is important to make your data actually useful and actionable.
With ETL, you bring data from your apps to a data warehouse. Reverse ETL brings the metrics from the data warehouse back into the apps you use.
Let’s say your ETL pipeline brought PayPal metrics into your data warehouse. These metrics contain information about a customer who hasn’t paid their invoice. With reverse ETL, this information can go from the data warehouse into Salesforce, where you can create a task for one of your employees to contact the insolvent customer.
Before the ETL process, PayPal metrics were raw and difficult to understand. In the data warehouse, these metrics are well-organized. So you can make sense of them and, with reverse ETL, send them to the right app.
Lastly, if you change the apps you use to collect data, your metrics won’t get lost. As your business grows, you may want to upgrade to marketing tools with more features. A good ETL tool unifies metrics from the old apps and the new ones in the same data warehouse.
Now you know what ETL is and how to start using it without headaches. We hope our advice helps you drive actionable insight from your data!
