ETL vs ELT Based on 19 Parameters [+Case Study]

According to a research report* by MarketsandMarkets, the data integration market is expected to grow from USD 11.6 Billion in 2021 to USD 19.6 Billion by 2026. This implies the huge potential of data integration and the two approaches to data management– ETL and ELT. However, in the battle of ETL vs ELT, choosing one over the other depends on the technicalities of business use cases for transforming data.

In this blog, besides discussing ETL vs ELT in details, we are going to break down the 19 top differences between ETL and ELT along with a case study. By the end of this post, you’ll be able to understand why ELT is the best approach for your data management needs.

Let’s get started! 

ETL vs ELT: An In-Depth Comparison

	ETL	ELT
Process	The order is as follows: Extract data -> transform it within a staging area -> load to data warehouse	The order is as follows: Extract data -> load to destination -> data transformations based on analytical purposes
Flexibility in transformation	Only transforms and loads the data that you want to	Loads all data, and allows you to choose which data to transform post loading
Raw data extraction	Using API connectors	Using API connectors
Data security	Shields data from breaches and unintentional disclosure	Sensitive info can be affected by hacks and accidental disclosures if your chosen ELT tool does not have the requisite security features
Availability of tools and experts	Easily available	Difficult to locate experts
Speed of analysis	Faster than ELT	Slower
How long is in this field	In use for more than 20 years	A new technology
Adding calculations	Existing columns will either be replaced by the results of calculations. Or you can attach the dataset to the target data system to push the results of the calculations	Adds calculated columns to the current dataset
Type of transformations	For complex transformations	Easier transformations
Compatibility with data lakes	Not a solution for data lakes. For integration with a structured relational data warehouse system, it transforms data	Can ingest unstructured data into data lakes. The data is then transformed as needed for analysis
Compliance	Easier to satisfy GDPR, HIPAA, and CCPA compliance standards	Sometimes tends to violate GDPR, HIPAA, and CCPA standards. But, easier with tools like Hevo Data which already complies with them
Data size handled	Best for small data sets	Best for large amounts of data
Aggregations	Becomes more complicated as the dataset gets bigger	Efficiently processes large amounts of data if you have a cloud-based target data system
Supported data types	Relational or structured data format	Structured, unstructured, semi-structured, and raw data types
Cost	Platforms provide scalable plans that can change with data ingestion. On-site ETL solutions for businesses are more expensive.	Platforms provide customizable plans with affordable choices for smaller-scale transformations.
Time for loading and transforming data	Comparatively takes more time	Lesser time
Supported data warehouses	Cloud-based and onsite data warehouses.	Cloud-based data warehousing solutions
Hardware needed	Cloud-based ETL platforms don’t need special hardware	Doesn’t need special hardware
Maintenance	Automated, cloud-based ETL solutions- don’t need frequently. On onsite ETL solution- need frequently	Need very little maintenance.

You have seen the technical differences between ETL vs ELT. Now, let’s get straight into why ELT over ETL. 

Why is ELT Better than ETL?

• Easy to process large amounts of data: Using ELT will be helpful if your project requires loading and analyzing a lot of data. ELT will take less time than ETL to gather your data in a single place. This is possible because ELT will process your data using the quick processing capabilities of cloud storage after loading it to the destination.
• Fast storing of data in the destination: An ELT solution allows you to collect all of your raw data very quickly in the destination, since all the data transformations happen in the destination. 
• Availability of unprocessed historical data for new use cases in the future: If your company will benefit from studying data trends, or new use cases come up, you might need to maintain unprocessed historical data on hand. You won’t need to reload the data during analysis because ELT stores all of your raw data in the data warehouse. 
• Flexible data integration process: ELT is a flexible process that can accommodate your company’s needs when data sources and formats change regularly.

Next, let us walk you through a case study to understand the potential of ELT to help your data team.  

Case Study on How  ELT Made Data Analysis More Efficient at GetSales 

GetSales is a company that helps businesses create and optimize end-to-end sales, marketing, and hiring funnels for conversion. The speed of integrating with partners’ data is critical to connect with leads and convert them quickly.

Integrating large amounts of data from various sources for each partner was challenging. Here is where an ELT solution became their savior.  

First, they tried to build data pipelines internally. But it was highly time-consuming and hard to maintain. That in turn led to long delays to access data and connect with leads and candidates for recruiting.

GetSales used Hevo’s automated ELT solution to quickly sync partners’ data from disparate sources to BigQuery, where it was transformed easily as needed. These heavy transformations would have been hard to manage with an ETL tool.

Take a look at what the Head of Data and Business of GetSales has to say about using Hevo for their data integration needs:

Instead of building a lot of these custom connections, ourselves, Hevo Data has been really flexible in helping us meet them where they are.

– Josh Kennedy, Head of Data and Business Systems

Earlier, frequent changes in data format at the source would disrupt the data flow in their data pipeline, impacting performance and speed of analysis. This ceased to be a problem once they opted for ELT, which could easily manage the changes in source data. Opting for ELT enabled the sales and recruitment teams at GetSales to take quick action on each candidate, achieve 10% more conversion rates, and onboard partners 10x faster.

Wrapping Up

ELT is preferred over ETL when you want to quickly collect a large chunk of data in a single destination. This will process your data using the quick processing capabilities of cloud storage. It also enables you to process historical data for new use cases in the future easily as ELT stores all of your raw data in the data warehouse. ELT also designs a flexible process to accommodate your company’s needs when the data sources and formats change regularly.

That’s it about ETL vs ELT! Now it’s your turn to analyze your user data management requirements and take action.  

Reference

* https://www.marketsandmarkets.com/PressReleases/data-integration.asp

If you want a high-performing ELT with low latency and accurate replication, you should try Hevo Data’s no-code, zero-maintenance data pipeline solution. It supports both pre-load and post-load transformations.

Visit our Website to Explore Hevo

Hevo is a No-code Data Pipeline Solution. It has pre-built integrations with 150+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial. Also, check out our unbeatable pricing to decide better.

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