The process of data integration has been circulating around for decades in some way, shape, or form. Businesses are constantly seeking ways to bring together their enterprise data and have it available in a centralized area–and through easy and real-time data integration software, they are able to achieve this. The most dominant form of data integration is ETL (Extract, Transform, Load) because it first extracts data from one or multiple sources, transforms it into what the user needs it to be, and then loads it into a targeted data lake. Reverse ETL is the new paradigm, so follow along with this beginners guide to Reverse ETL, as we cover exactly what it is, how things were in the data space prior to the arrival of it, why data has to be moved, how Reverse ETL benefits data, and some primary use cases.
1. What is it
Standard ETL is an essential piece of the enterprise data management puzzle because it extracts information from data sources such as files and SQL databases and loads it into data warehouses, like Amazon Redshift and Google BigQuery.
Reverse ETL basically operates just like its name because it flips around the order of operations within the traditional ETL process, which are ‘extract, transform, load,’ moving data from a data warehouse into third-party systems so that this data becomes operational.
Reverse ETL generally treats the traditional ETL destinations as sources and traditional ETL sources as destinations because it extracts the data from a data lake or data warehouse, transforms it as necessary, and then loads it into a third-party SaaS application or platform.
2. Before this
Reverse ETL has surely been in existence for quite some time. But it didn’t become defined as an ETL process until more recently.
Prior to the arrival of reverse ETL solutions, business teams were stuck having to establish their own API connectors between their data warehousing technology and their operating systems so that business users could access datasets directly within these third-party applications.
The downsides to creating API connectors include:
· Manually making API connectors can take anywhere between several days to over, even longer if those performing that task aren’t technically qualified.
· These connectors have to be maintained by your team to address any alterations in the underlying technologies that can occur at either end.
· Weak API endpoints might not be able to cope with real-time data transfer, making the writing of connectors even more of a challenging process.
These are the issues that have motivated the growing interest in alternative ETL solutions, and reverse ETL allows out-of-the-box connections between the components of your data stack so that the days of you getting bogged down with manually building API connectors are over.
3. Why Data Needs to be Moved
The idea of moving your data out of your data warehouse may concern you. But reverse ETL is necessary. Otherwise, your data can’t get passed on to the staff members that need to rely on it for decisions they are getting ready to make.
It may appear easier to create reports and visualizations using this data in BI tools or SQL, but these insights achieve more power by driving the everyday operations of your various teams in tools like HubSpot, Salesforce, NetSuite, and Zendesk.
This need puts reverse ETL in the pivotal position in the contemporary data stack to close the gap between analysis and activation.
4. How Reverse ETL Benefits Data Teams
There are several ways it will benefit you and your data teams:
· Customer relationship management (CRM) software: use reverse ETL for customer data and transfer it to CRM systems like Salesforce.
· Business intelligence (BI) and analytics: use reverse ETL to move the data that is inside your data warehouse into your BI and your analytics platform, such as Microsoft Power BI.
· Data governance: use it for adopting an enterprise-grade solution instead of trying to make your own manual API connectors.
5. Use Cases for Reverse ETL
Reverse ETL has three primary use cases:
· Operational analytics: putting insights from analytics to business teams in their normal workflow to enable them to make data-informed decisions.
· Data automation: simple solutions for menial tasks that human labor shouldn’t be asked on.
· Data infrastructure: reverse ETL can help with personalizing customer experiences and accessing disparate data sources.
Using Reverse ETL for operational analytics, data automation, and data infrastructure are sure ways to go from being a novice to an organization that is on its way to reap all the professional benefits that will keep you competitive in your industry.