Feature Image ETL, ELT and Reverse-ETL_ The What, How and Why for Modern Teams

ETL, ELT and Reverse-ETL?
The What, How and Why for Modern Teams

By Brian Laleye · September 6, 2022 · 9 min read

ETL (Extract, Transform and Load) is the process of extracting data from one or more sources, transforming it into a format that can be loaded into a target database, and writing it to the target database.

ELT (Extract, Load and Transform) is similar, but adds transformation to the third step in the ETL process.

ETL and ELT are often used interchangeably when referring to data warehouse projects.

However, there are differences between them.

The main difference between ETL and ELT is that ETL involves moving data from one place to another while ELT involves moving data from one place to another as well as performing transformations on that data.

Reverse ETL, also known as “extract-transform-load-reverse”, is essentially the same as ETL except it moves data in the opposite direction.

Data is extracted from a source database, transformed according to business rules, and loaded into another database or business application.

Let’s explore all the ETL processes in-depth, their key benefits, use cases and pros and cons when it comes to analyzing vast and disparate sources of data.

What is ETL (Extract, Transform, Load)?

ETL Pipeline is a method of data analysis that involves taking raw data from disparate sources and converting it into a uniform format.

This allows it to be incorporated into an analytics platform for instance, which can then be used to produce insights about the data.

Visual Extract Transform Load

Let’s dive into 3 key steps of any ETL pipeline.

  • Extract

It is the process of extracting raw unstructured or structured data from any source like business applications, flat files, APIs, IoT, databases, web sources, etc.

This step includes parsing, splitting and filtering the data that needs to be extracted from different sources.

The output of this step can be a CSV file containing the data which would be used for further processing in other steps like transformation and loading into the target database.

  • Transform

In this step, we have in our hands a dataset that is not properly fit for any use. 

We perform various transformations like converting string values into numeric values, normalizing date values, etc., so that we can load them into our target database without any problem later.

Here are some of the methods to perform data transformation.

  • Load

Once you have extracted and transformed your raw data from heterogeneous sources, it’s time to load it into your database / data lake or data warehouse.

This step can include creating tables or views in your database system, loading tables into those views using SQL queries, and then populating those tables with the transformed data that has been loaded from an ETL tool or manually.

What is ELT (Extract, Load, Transform)?

ELT pipeline converts raw data into a format that can be easily processed by downstream systems.

This pipeline is often used to enable data integration between applications or systems.

Schema of ELT pipeline

ELT pipeline is an ETL process performed after Data Warehousing.

The purpose of performing ELT is to enrich and refine existing data, creating a more accurate and useful analytical platform.

A goal of each stage in the ELT pipeline framework is to improve data quality before it reaches its final destination.

What is Reverse-ETL?

Reverse-ETL is a software process with the aim of extracting data from systems and other disparate sources, transforming it into an external representation, loading it into a data warehouse, and integrating it with corporate business processes.

Reverse-ETL is essentially the same as ETL except it moves data in the opposite direction.

Data is extracted from a source database, transformed according to business rules, and loaded into another database or business application.

The idea behind doing it in reverse is that you can consolidate all of your company’s information in one central location, allowing for more efficient analysis and decision-making down the road.

By centralizing your data you are also creating a single source of truth for your organization.

ETL, ELT and Reverse-ETL - Key Differences

ETL, ELT and Reverse-ETL are all data-processing methods to move data from Input (sources) to Output (Destinations).

ETL (Extract Transform Load) is a process of extracting data from one source, transforming it into a desired format and loading it into another system. It involves taking the data from the source and cleaning it up before moving it to its destination.

ELT is basically a derivative of ETL in the sense that it generates the same results as ETL. However, ELT is different from ETL in its approach. ELT basically builds on top of a pre-existing data warehouse instead of starting with extracting data from multiple sources and then transforming it into a single source.

The main purpose of this process is to add more information to the existing data warehouse so that new reports can be generated without slowing down old reports.

Unlike ETL, ELT is an approach to integrating sources into an existing data warehouse without needing a complete transformation.

Extracting, loading and transforming data is a complicated process.

ELT makes it easier by adding more information to the stored information instead of starting fresh with a new warehouse.

Here’s the key differences between ETL, ELT & Reverse-ETL:

Criteria

ETL

ELT

Reverse-ETL

Definition

Extracts raw data from sources, transforms it, and then loads it into a target database.

Extracts raw data and loads it directly into a target data warehouse or lake before it gets processed.

Extracts raw data from sources, transforms it, and then loads it into a target business application.

Performance

Data is transformed before loading. Complexity leads to heavier pipelines.

Data is loaded then transformed. ELT is then faster than ETL.

Transformations are needed to make the data useful to stakeholders.

Maturity

Process well-documented, protocols, tools and know-how.

Less tools and protocols since this is a newer process.

Newest process to use reliable data downstream. Need to gain maturity.

Costs

Costly to extract info from raw data.

Cloud-based tools to reduce costs.

Cloud-based tools to reduce costs.

Volumetry

Suited for small and complex datasets.

Suited for large datasets coming from disparate sources.

Suited for both large and small datasets, it’s a case by case implementation.

Output type

Structured

Structured or Unstructured

Structured or Unstructured

ETL, ELT or Reverse-ETL - Pros and cons

Let’s dive into the pros and cons of these pipelines processes:

Pros

Cons

ETL

ELT

Reverse-ETL

The main difference between ETL and ELT is that ETL requires you to create new tables in your database or create new columns on existing tables that aren’t present in your source systems.

It also requires you to write code that performs all of these steps automatically when needed.

ELT doesn’t require any new schemas or code because it uses the existing schemas already in place in your target system(s).

The reverse ETL toolkit is a key driver of a business’s ability to scale its data warehouse, as it allows faster data ingestion and transformations, more reliable deployments and rollbacks, the ability to leverage the latest open source tools and a centralized location for business logic.

Conclusion

Since data management is no longer in the unique scope of work of data and engineering teams, it needs to be widespread across teams, processes and tools. Thanks to new cloud-based tools, it is now possible to leverage data and insights at every level in the organization.

At RestApp, we’re building a Data Activation Platform for modern data teams.

We designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting your data journey, check out our website and create your free account.

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Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He’s a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.
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