With the advent of automation, data volume, size, and speed are constantly evolving. The bigger concern that data teams face now is managing this enormous amount of data. Rising amounts of data bring about the need to warehouse it. The data warehousing process consists of three basic steps: extract, transform, and load, generally performed using ETL tools.

ETL tools have numerous business uses. Traditionally, data was more static, and system architecture was monolithic. ETL was carried out in batches, and it would take half to an entire day for the process to complete. One of the first use cases of ETL was to create analytical reports. Recently, data analysts discovered that data is operational, such as data obtained from day-to-day transactions.

ETL vs. ELT vs. Reverse ETL 

The current data warehouse architecture carries out the ETL process in three simple steps. The data present in various forms like flat files, databases, and web services is extracted from multiple sources and then passed over for transformation or processing. In this step, the data is cleansed and processed, after which it is then loaded into the warehouse. The process of ETL ends at a data mart that is present at the top of the data warehouse.

ELT, on the other hand,  involves the same processes but in a different sequence. The data is extracted from various sources, and it is then loaded into the warehouse. The data is then processed according to the different business uses before using it.

Reverse ETL is an exact process reversal of ETL. The need for consistent customer data visibility across all systems gave rise to the emergence of reverse ETL. This tool is used to send data in real time to various SaaS systems. For example, sending data from the warehouse to Salesforce to keep track of the list of all the high-profile customers.

Source: Deloitte

ETL/ELT and Reverse ETL are two sides of the same coin—one is used for data integration, and the other for data operations.

ELT is gaining popularity 

Traditionally, the approach to databases has always been schema on write—the data points had to be given a template before storage. When users wanted to retrieve data, it was already in a manageable format. This practice was to maintain consistency. However, with time and the amount of data, it came off as restricted. Even slightly unstructured data was rejected because it did not align with the template. ETL tools followed schema-on-write. IT experts realized that raw or less structured data was also valuable to the organization. And to extract value from it, it was necessary to modify the approach to databases. Thus came the schema on read.

Schema on read allows both unstructured and structured data to be stored on the system and formatted whenever retrieved. ELT tools follow this approach to make data useful and are more flexible to use. Initially, data used to be stored in on-premises servers; thus, storing data was way costlier than it is today. Many ELT and ETL tools today work hand in hand with cloud data warehouses that autoscale with volumes of data. With the entry of cloud data warehouses, data storage is possible at low costs. ETL and ELT tools are means of data integration using different approaches.

Will reverse ETL replace ETL/ELT?

Now that we know exactly what ETL and ELT tools do, it's time to dive deeper into reverse ETL. Some people may ask, if there are already two approaches for storing data in the warehouse, why go reverse? Is it to replace ETL and ELT?

The short answer is no. Companies have loads of data that lie in the warehouse and remain unused. It needs to be made visible to know what value it has to offer and further activated. While data scientists have built Customer Data Platforms (CDPs) that integrate all customer data under one roof, this can only be a partial solution to unearthing the hidden data. This is where companies need reverse ETL.  

While ETL and ELT tools give business functions clean and processed data, it's crucial to understand if they can actually use this data to make decisions. For example, while marketing teams can store data in Hubspot for campaigns, reverse ETL helps these teams access data related to the campaigns to make targeting more specific. Similarly, a database of customers in Salesforce helps sales teams to target them with specific messages. Many reverse ETL tools move data from data warehouses to various CRMs for different business functions to access this data and make decisions. Reverse ETL makes the data more operational and enriches it to make it relevant to the customers.

Reverse ETL tools help break silos and give various teams visibility to the required data, fulfilling data activation. Operational analytics is an emerging approach to utilizing data; that is exactly what reverse ETL does. Companies must get the data out of centralized silos and put them in various business functions.

The newly created Reverse ETL category on G2 has grown in traffic since its creation. Buyers have shown interest in both ETL and reverse ETL tools, which is evident from the traffic on G2. Traffic to the Reverse ETL category on G2 has seen a growth of more than 100% since inception. Traffic to the ETL Tools category page on G2 is growing steadily—32% from July 2022. It is clear that companies are interested in trying the combination of the tools, which we see as a future trend in the ETL space.

The future of ETL tools

The ETL process has been in use since the old data warehousing methods, and these have changed over time. ELT is the modern approach to storing data using scalable resources, whereas reverse ETL is enriching external systems with cleansed data using ETL/ELT.

A combination of ETL/ELT and reverse ETL can help organizations derive better insights from the data they obtain. Operation-centric teams can access this cleaned data to run new sales and marketing campaigns and copy the data to the applications.

Edited by Jigmee Bhutia