ETLs vs ELTs: Why are ELTs Disrupting the Data Market?
Tags: datascience, database, bigdata, startup
Published: 2021-11-30 13:53:52

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In the business world, cloud technology has become more and more dominant in recent years. Right now, research shows that about [50% of all business data](https://www.computerweekly.com/news/2240241926/Prepare-for-two-modes-of-business-intelligence-says-Gartner) is stored in the cloud, which just demonstrates the importance of external data sources and their place in the modern business environment.

In seeking to keep up with digital transformation and data trends, many businesses are turning to [ELT (Extract, Load, and Transform)](https://seattledataguy.substack.com/p/what-are-etls-and-why-are-they-important) tools. Besides accommodating heavy workloads, ELTs help teams integrate data.

In this post, we'll take a look at ELTs and how they compare to ETLs, and why ELTs have become such a disruptive force in the data market.

Let's dive in.

## ETLs vs. ELTs

While ETLs and ELTs both deal with data, they are, in fact, different tools. ETL is the Extract, Transform, and Load process for addressing data, while ELT is Extract, Load, and Transform. In an ETL model, data migrates from its original source to a data warehouse, where it is staged. In ELT, you will often use a very different data storage paradigm.

Both ELT and ETL involve the following three steps:

Extract

The extraction phase of both ELT and ETL solutions involves pulling source data from the original database. In an ELT model, the data goes right to a data storage system. In classic ETLs you're pulling the data into a staging area.

Transform

Transformation is the process of changing the data's structure and is what ultimately allows the data to integrate with the target data system and the rest of the information it contains.

Load

Loading is the process of moving the data into a data storage system, which prepares it for analysis.

ETL and ELT perform these steps in different orders. Teams deciding between the two solutions will need to determine whether to transform their data before or after moving it into the data storage repository.

In the data science landscape, both ETL and ELT are necessary technologies. Because information sources, including unstructured NoSQL databases and structured SQL databases, almost never use the same formats, data must be transformed and enriched before it can be analyzed as a whole. By digesting this data, ETL and ELT solutions step in to allow business intelligence platforms to do their job.

What each is good for: ETL is beneficial for organizations concerned about data compliance and privacy, since it cleans sensitive and secure data before sending it to the data warehouse. ETL, in contrast, is excellent for sophisticated data transformations and tends to be more affordable than ELT.

## Why Should I Use an ELT?

While both ETLs and ELTs have their place in the data landscape, more and more organizations are choosing to adopt ELT tools to address the volume and speed of their big data sources, which often overload the more traditional ETL tools.

When used correctly, ELT tools streamline analysis data preparation. Because ELTs load data into the framework where it will eventually be processed, staged, and transformed, they allow teams to skip some busy work associated with data transformation.

Here are a few of the benefits of using ELT systems:

## Fewer Physical Infrastructure Requirements

ETL tools serve as a sort of physical location for the steps between extracting data and loading it into repositories. In light of that, organizations that want to integrate data into target systems must purchase and maintain these tools to do so.

ELTs, meanwhile, don't require that intermediate step, which means they require less physical infrastructure and specific resources. Instead, the target system's engine performs the transformation, rather than the engines native to ETL tools.

## More Efficient Data Staging

ETL tools cleanse data and prepare it for transformation. ELT tools, however, stage data *after* loading it into a data warehouse, lake, or cloud storage solution. This makes the data staging process significantly more efficient and reduces latency across the board. Additionally, leading ELT tools make fewer demands on initial data sources and reduce the in-between steps associated with data processing.

## Accelerated Time to Value (Sort of)

Because they transform data within a target system, ELT tools speed up the time to value for teams. This allows data scientists and analysts working with big data to leverage and transform data quickly and to implement machine learning techniques for better analysis.

ETL tools, on the other hand, require a manual coding process to ensure data conformity and uniformity, which adds time to the experience and increases latency across the board.

## The Place of ELTs in the Modern Data Market: How ELTs Impact Data Warehousing

One of the largest benefits of ELT systems is the way they improve both data lakes and warehouses. Regardless of which solution a team is using, ELT tools significantly reduce the time required to prepare data for use. By loading data into a data lake framework, ELTs allow organizations to take advantage of the processing engines within the solution when it comes time to stage and transform data.

This serves a few distinct purposes. Besides providing immense scalability and leveraging parallel processing, it eliminates the requirement that organizations rely on conventional data modeling to unify their data.

Here are a few of the other ways ELT solutions overhaul data warehousing:

## Streamlined Architecture

ELT tools streamline the process of preparing data for use. Because there is no in-between layer with built-in processing power limitations, the ELT can handle both data staging and transformation, which streamlines the experience for users.

## Rapid Data Incorporation

ELT solutions make it possible to incorporate data rapidly into both warehouses and lakes. With traditional methods, these sources can be difficult and clunky to use, leading to unnecessary latency and delays.

## Some ELT Tools

There are lots of options for ELTs. Honestly, ELTs are less about the tool and more about the method.

However, many solutions that market themselves as ELTs is that they also often automated connectors that allow users to quickly develop their pipelines.

## Fivetran

[Fivetran](https://www.theseattledataguy.com/what-is-fivetran-and-why-you-should-use-it/) is a highly comprehensive ELT tool that is becoming more popular every day. This tool allows efficient collection of customer data from related applications, websites, and servers. The data collected is then transferred to other tools for analytics, marketing, and warehousing purposes.

Not only that, Fivetran has plenty of functionality. It has your typical source to destination connectors and it allows for both pushing and pulling of data. The pull connectors will pull from data sources in a variety of methods including ODBC, JDBC, and multiple API methods.

Like many other ELT tools, Fivetran push connectors receive data that a source sends, or pushes, to them. In push connectors, such as [Webhooks](https://fivetran.com/docs/events/webhooks#webhooks) or [Snowplow](https://fivetran.com/docs/events/snowplow#snowplow), source systems send data to Fivetran as events.

Most importantly [Fivetran allows for different types of data transformations](https://fivetran.com/docs/transformations/basic-sql). Putting the T in ELT. They also allow for both scheduled and triggered transformations. Depending on the transformations you use, there is also other features like version control, email notification, and data validations.

## Stitch..Sort Of..

[Stitch](https://www.stitchdata.com/) was developed to take a lot of the complexity out of ETLs and ELTs. One of the ways Stitch does this is by removing the need for data engineers to create pipelines that connect to APIs like in Salesforce and Zendesk.

It also attaches to a lot of databases as well like MySQL. Having access to a broad set of API connectors is only one of the many benefits that makes Stitch easy to use.

Stitch also removes a lot of the heavy lifting as far as setting up cron jobs for when the task should run as well as manages a lot of logging and monitoring. ETL frameworks like Airflow do offer some similar features. However, these features are much less straightforward in tools like Airflow and Luigi.

Stitch is done nearly entirely with a GUI. This can make this a more approachable option for non-data engineers. It does allow you to add rules and set times when your ETLs will run.

## Airbyte

[Airbyte](https://airbyte.io/) is a new open-source (MIT) EL+T platform that started in July 2020. It has a fast-growing community and it distinguishes itself by several significant choices:

Airbyte's connectors are usable out of the box through a UI and an API, with monitoring, scheduling, and orchestration. Their ambition is to support 50+ connectors by EOY 2020. These connectors run as Docker containers so they can be built in the language of your choice. Airbyte components are also modular and you can decide to use subsets of the features to better fit in your data infrastructure (e.g., orchestration with Airflow or K8s or Airbyte's...)

Similar to Fivetran, Airbyte integrates with DBT for the transformation piece, hence the EL+T. While contrary to Singer, Airbyte uses one single open-source repo to standardize and consolidate all developments from the community, leading to higher quality connectors. They built a compatibility layer with Singer so that Singer taps can run within Airbyte.

Airbyte's goal is to commoditize ELT, by addressing the long tail of integrations. They aim to support 500+ connectors by the end of 2021 with the help of its community.

## The Future of ELT

Teams who need to accommodate the power, size, and speed of big data may search in vain for a solution that can help them achieve their motive. Fortunately, ELT is changing that dynamic. Designed to help teams forsake the traditional layers of data processing and transformation and modernize the approach, ELTs simplify both integration and architecture, decreasing latency and offering agile, enhanced performance.

When compared to traditional ETL methods, it's clear that ELTs are the way of the future, as far as data processing is concerned. More sustainable, effective, and timely overall, ELT methods provide more flexibility and customization for organizations who want to control their data integration and implementation.

By offering high speeds, rapid load times, and an invitingly low maintenance requirement, cloud-based ELT systems place the burden of transformation on the data destination, eliminating the need for data staging. This helps organizations enjoy a simpler relationship to their data, without sacrificing power.

If we look into the future of data processing, it stands to reason that ELTs will rapidly become the de facto system for organizations focused on efficiency, scalability, and reliability. While both solutions have their strengths and weaknesses, the ELT has emerged as the undeniable favorite of many organizations around the globe.

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