ETL vs ELT: 9 Critical Differences

on ETL, Tutorials • October 19th, 2020 • Write for Hevo

Want to know the difference between ETL vs ELT ? The explosion of big data has put a huge strain on the data warehouse architecture. Organizations handle huge volumes of different types of data including social media, customer behavior, and big data. Organizations that have data warehouses use either extract, transform, load or extract, load, transform data integration methods. 

These Data Integration processes are very necessary for data science because data sources, whether structured SQL databases or unstructured NoSQL databases, will rarely use similar or compatible formats. Therefore, you must clean, transform, and enrich your data sources before integrating them into one data source. 

This blog lets you understand the ETL vs ELT processes in great detail highlighting the 5 key differences between the two. 

These are the two most popular methods used by businesses to collect data from multiple sources and store it in a data warehouse that can be accessed by all users in an organization. ETL is the traditional approach to data warehousing and analytics, but the popularity of ELT has increased with technology advancements. 

It then becomes possible for business intelligence tools like Tableau, Looker, and Data Studio to derive insights from the data. Regardless of what process an organization uses, the data transformation/integration process takes the three steps given below:

  • Extract: Extraction is the process of pulling the source data from the original data source or database. For ETL, the data goes into a temporary staging area while for ELT, the data goes into a data lake storage system. 
  • Transform: Transformation is the process of changing the structure of information so that it can integrate with the target data system and the other data in the system. 
  • Load: Loading is the process of depositing information into a data storage system. 

These two Data Integration processes perform the above steps in a different order. So, should data transformation be done before or after loading the data into the data repository?

The blog starts with a brief introduction to these processes before diving into their differences.

Table of Contents

What is ELT?

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In this method, data can be loaded directly into the data warehouse after extracting it. The data doesn’t have to be moved to a temporary staging area. Data transformation is then done within the target database. 

This process is used for data lakes that accept both structured and unstructured data. This means there is no need to transform the data before loading it. 

Advantages of ELT

The primary benefit of this process is its flexibility and ease of storing new, unstructured data. It allows you to save any type of information, even if you haven’t transformed and structured it. It gives you immediate access to information whenever you want it. 

Other advantages of this Data Integration process include:

  • High Speed: It allows all data to get into the data warehouse immediately. Users can then get the data they need for analysis and transformation. 
  • Low Maintenance: This process is cloud-based, hence, it relies on automated solutions for maintenance. 
  • Quicker Loading: Since the transformation is done inside the warehouse, it reduces the time taken to load the data to its final destination. 

What is ETL?

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In this process, data is first extracted from the homogeneous/heterogeneous data sources and then deposited into a staging area. The data is then cleaned and transformed into the required format. Finally, the data is uploaded to the data warehouse. 

This process is a good component of Online Analytical Processing (OLAP) based data warehouses like Microsoft Analysis Server, SAP NetWeaver, IBM Cognos, Jedox OLAP Server, etc. Since OLAP only accepts structured data, you must transform the data before loading it. Its traditional methods had a waiting period for the data to go through each phase.  This made them time-consuming. 

However, its modern solutions are much faster and easier. 

Advantages of ETL

One of the major advantages of this Data Integration process is the pre-structured nature of the OLAP data warehouse. Once the data has been transformed, it allows for a more efficient, speedier data analysis. 

On the other hand, ELT is not suitable when a speedy data analysis is required. Another advantage of this process is compliance. Companies regulated by HIPAA, GDPR, or CCPA are required to encrypt specific data fields for privacy purposes. 

This Data Integration process offers a more secure way of doing it because it transforms the data before moving it to the data warehouse. On the other hand, ELT requires users to upload sensitive data first. This data can show up in logs that SysAdmins can access. 

Hevo, A Simpler Alternative to Perform ETL

Hevo Data, a No-code Data Pipeline, helps to transfer data from 100+ sources to your desired data warehouse/ destination and visualize it in a BI Tool. Hevo is fully-managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using various BI tools such as Power BI. 

Get Started with Hevo for Free

Check out some of the cool features of Hevo:

  • Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
  • Real-time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
  • 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
  • Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.  
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources that can help you scale your data infrastructure as required.
  • 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
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ETL vs ELT: Finding the Right Approach

In this discussion on ETL vs ELT for data management, finding the right approach mainly depends on these things:

  1. Fundamental storage technology
  2. Data storage architecture
  3. Application of Data warehouse

ETL vs ELT Comparison

A broad comparison between ETL and ELT is explained in the table below.

Order of the ProcessData is transformed and then loaded into the desired data warehouseThe data transformation takes place within the desired data warehouse. The data is first loaded and then transformed.
Key FocusLoading data into databases after transforming data, masking data, normalizing, joining between tables in-flight.Loading into Data Warehouses and then mapping schemas directly into the warehouse.
Privacy ComplianceSensitive information can be edited before loading into the target systemData is uploaded in its raw form without any sensitive details removed. Masking will be handled in the target system.
Maintenance RequirementsYou have to manually understand the transformation logic and schema change.The data warehouse takes care of the transformations required.
LatencyHigher latency Lower latency
Analysis flexibilityUse cases and report models are well-defined.Data can be added at any time with schema evolution. Analysts can build new views off the target warehouse.
Table Source: striim

Data Integration with ETL vs ELT

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Here are the differences between the two Data Integration processes that you can keep in mind before deciding on a process for your business use case:


ELT is the more affordable option of the two Data Integration processes and can be leveraged by businesses of all sizes from small-scale to well-established companies. It is also more scalable as compared to ETL which is more suitable for small to medium-sized businesses.

Data Warehousing Support

In terms of Data Warehousing Support, ETL trumps ELT as it offers support for both on-premise and cloud-based Data Warehouses. ELT, on the other hand, works only with cloud-based Data Warehouses.

Loading Time

Due to the staging process in ETL, the loading time is much more than for the alternative.

Use Cases

Here are a few examples of prototype cases where ELT is preferred over ETL:

  • Companies with Huge Volumes of Data: This process is good for managing huge volumes of both structured and unstructured data. Its Cloud-based solutions help users process large volumes of data quickly. 
  • Companies that Need Instant Data Access: It is a suitable option for a company that needs immediate data access. Since transformation is done as the last step, it gives first priority to loading data into the data repository. 

Data Lake Compatibility

ELT provides a pipeline for Data Lakes to ingest unstructured data which can then be transformed on an as-needed basis. ETL does not offer this functionality. It simply transforms data for integration with a structured relational Data Warehouse system.

Waiting Time to Load Information

The ETL Load Times are longer than its alternative because it is a multi-stage process that involves loading the data into the staging area followed by transformations and ending with the data being loaded to a Data Warehouse. However, once the data is loaded analysis of ETL data is faster than the alternative where the data is loaded only once into the target data system.

Waiting Time to Perform Transformations

For the ELT process, the data transformation takes place after loading and on an as-you-need basis. This means you will transform only the data you need to analyze at the time. Therefore, the transformations for this process happen a lot faster. But the need to continually transform data slows down the total time it takes for analysis.

ETL vs ELT Differences

Here is a summary of all the differences between the two Data Integration processes listed in this article:

PriceCostlier than its alternative and is suitable for small-to-medium-sized businessesCheaper than its alternative and is scalable. It’s affordable to businesses of all sizes
Data warehousing supportCan work with both on-premise and cloud-based data warehousesWorks with cloud-based data warehousing solutions
Loading timeLoads data slowly due to the staging processLoads data faster
Use CasesThis process can be used for use cases where data access need not be instant.This process is preferred for companies that need instant data access and huge volumes of data.
Data Lake CompatibilityTypically not meant for Data Lakes.This process offers a pipeline for Data Lakes that can be used to ingest unstructured data.
Waiting Time to Load InformationThe Loading time is greater than its alternative since it’s a multi-stage process.Data Loading happens faster here since you don’t need to wait for transformations plus the data loads only once into the target data system.
Waiting Time to Perform TransformationsTransformations take a lot of time over here. Transformations take place a lot faster since the transformations take place after loading and on an as-needed basis.

ETL vs ELT: Pros And Cons

Limitations of ETL

Here are the limitations of this Data Integration process:

  • Difficult to Debug and Fix Errors: Since this process selects data early, it is difficult to debug and fix errors later in the process. 
  • Slow to implement Process Changes: This process has become outdated. It is only efficient when using the rigid structure of old data formats. 

Limitations of ELT

Here are the limitations of this Data Integration process:

  • More Resources are Required: The storage of data in this process requires more resources. 
  • Maybe Slower: This process can run slowly due to persisting each step of the process. 

When should you use ELT instead of ETL?

In the battle of ETL vs ELT, here are some practical use cases on when to use ELT instead of ETL.

  • When you want to store data at a high speed: You can store all the raw data at a higher speed and in less time using an ELT tool.
  • When you require a flexible data integration process: In case you want to format the data frequently based on your organization’s data sources, an ELT tool is preferable.
  • When you have a huge amount of data to process: When you have heaps of data to load and analyze, choose an ELT over an ETL.
  • When you want to retain raw historical data for future analysis: If you are analyzing the data trends, you will need the old data. ELT will have all the old raw data in and will not require to reload again.

Operationalize Your Data Warehouse with Reverse ETL

The data stored in Data Warehouses is primarily used for Analytical workloads by Business Intelligence and Data Analysis tools such as Microsoft Power BI, Tableau, etc. However, businesses have now started recognizing how this data can further be leveraged for Operational Analytics. Operational Analytics is used for day-to-day decision-making in an organization to improve the efficiency and effectiveness of its internal operations. Operational Analytics is empowered when businesses implement a Reverse ETL process between Data Warehouses and their operational tools. Along with using data to identify long-term trends and influence long-term strategy, Operational Analytics helps form the strategy to improve a business’s day-to-day operations. Some examples of Operational Analytics are as follows:

  • Businesses can identify at-risk customers by integrating customer usage data with Customer Relationship Management (CRM) tools. A system can be set up to notify teams when usage goes below a given threshold, thereby allowing the Sales team to take necessary actions to prevent the customer from churning.
  • Operational Analytics can also be leveraged to build personalized Marketing campaigns. By leveraging Sales, Support, and Product data, campaigns can be created for each user based on their requirements instead of sharing a single campaign with all users.

Operational Analytics allows decision-makers to ensure that every decision made is an excellent strategic choice and backed by real-time data. This entire process, however, cannot be implemented without Reverse ETL.

Sending real-time data to Software-as-a-Service (SaaS) systems can help make sure there is a consistent view of the customer across all systems. For example, pushing data to Salesforce from a Data Warehouse or a Business Intelligence tool means all teams have access to an up-to-date list of each customer’s Lifetime Value, Product Qualified Lead (PQL) and Marketing Qualified Lead (MQL) information, Customer Health, Propensity Score, ARR/MRR, Funnel Stages, etc.

5 Best Reverse Etl Tools & Their Alternatives

Some of the best Reverse ETL solutions available in the market are as follows:

1) Hevo Activate

Reverse ETL - Hevo Logo
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Hevo Activate helps you directly transfer data from Snowflake, Amazon Redshift, etc., and various other sources to SaaS applications, CRMs such as Salesforce, HubSpot, etc., Support tools such as Zendesk, Intercom, and a lot more, in a completely hassle-free & automated manner. Hevo Activate is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Hevo Activate takes care of all your data preprocessing needs and lets you focus on key business activities, and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. GET STARTED WITH HEVO FOR FREE

Users can leverage Hevo Activate to perform the following operations:

  • Create User Segments: Creating user segments allows the Marketing and Sales teams to understand how resources should be utilized for different kinds of users. This allows teams to focus on channels that convert better and maximize their Return On Investment (ROI).
  • Build 360 View of Customers: This can be used to understand each customer better and plan strategies accordingly to ensure maximum revenue. This information can also be leveraged to help Support teams prioritize Enterprise customers. Businesses can seamlessly sync all customer data into their support software and respond quicker with a holistic customer background.
  • Sync Product Data into Sales CRM: Hevo Activate can be leveraged to get all product data in the CRM tool of choice, allowing businesses to track user activity easily. Users can be segmented based on their activity, and that information can be used to improve product adoption and prevent churns.

2) Hightouch

Hightouch is a Reverse ETL Data Platform that allows businesses to effortlessly sync their data from Data Warehouses to customer-facing teams’ Software-as-a-Service (SaaS) Customer Relationship Management (CRMs), Marketing, and Support tools such as Salesforce, Hubspot, Marketo, Gainsight, Zendesk, etc. It also allows businesses to use their Data Warehouse as an Operational Center and an Analytics Center. This gives them the ability to use this analysis to power their operational workflows.

Hightouch syncs data with the tools at the user’s specified frequency, and it only syncs the data that has changed since the most recent call. Hence, it can avoid data transfer limits imposed by the APIs of their operational tools.

Hightouch Pricing

Reverse ETL - Hightouch Pricing
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Hightouch offers two paid tiers, i.e., Team and Business, along with its Free tier. The pricing for each paid tier depends on the number of records a user is expected to integrate. The Team tier offers 10k Active Records at $150/month, 30k Active Records at $300/month, and 100k Active Records at $600/month. The Business tier is a custom tier for large Enterprises with complex requirements. However, Hightouch does not follow a transparent pricing model for this tier, and the final price depends on your unique business and data requirements.

More information about Hightouch can be found here and its pricing can be found here.

3) Census

Reverse ETL - Census Logo
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Census is a popular Operational Analytics platform that syncs data across numerous third-party applications with your Data Warehouse. Census automatically publishes all SQL & dbt models from your Data Warehouse in your operational tools allowing you to ensure customer success and that your Marketing and Sales teams are on the same page without the need for any engineering bandwidth.

Census works on top of your existing infrastructure. This means that you do not need to spend additional resources in setting up new hardware. This tool empowers everyone in your business to make data-driven decisions by leveraging live metrics in every application leading to happier users & more revenue.

Census Pricing

Reverse ETL - Census Pricing
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Census offers a Free and Business Plan with a 14-day free trial. The Free Plan only supports integration with Google Sheets and allows creating only two workflows. However, the Business Plan supports integration with unlimited sources and allows the creation of unlimited workflows. This plan does not follow a transparent pricing model. The final price depends on the number of sources you wish to integrate and can be finalized after a discussion with the Census team.

More information about Census can be found here, and its pricing can be found here.

4) Grouparoo

Reverse ETL - Grouparoo Logo
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Grouparoo is another popular Reverse ETL solution that gives businesses the ability to move data between their Data Warehouses or Databases and Cloud-based operational tools in real-time. One key characteristic that differentiates Grouparoo from other tools is that its free Community Edition is Open-source. This means that businesses can make changes to the code base to fit their unique use case and business requirements.

Grouparoo Pricing

Reverse ETL - Grouparoo Pricing
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Grouparoo offers two paid tiers, i.e., Standard Cloud and Enterprise Cloud, along with its free Open-Source Community Edition. The Standard Cloud Tier allows users to sync 100k records at $150/month, and the Enterprise Cloud Tier is a custom plan for Enterprises with larger data requirements. The pricing for this tier can be discussed with the Sales team at Grouparoo. An in-depth comparison of the features offered in each tier is as follows:

Reverse ETL - Grouparoo Tiers Comparison
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More information about Grouparoo can be found here, and its pricing can be found here.

5) Polytomic

Reverse ETL - Polytomic Logo
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Polytomic is another Reverse ETL tool that allows businesses to create a live view of all customer data they need in Marketo, Salesforce, HubSpot, and other business systems within minutes. Users have the ability to sync data from various data sources such as Data Warehouses, Databases, Spreadsheets, APIs, etc., and choose the fields they wish to sync from these sources. Polytomic also houses a functionality that allows users to sync data in real-time or schedule it or invite other users to perform the necessary operations.

Polytomic Pricing

Reverse ETL - Polytomic Pricing
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Polytomic offers three tiers, i.e., Basic, Standard, and Enterprise. However, it does not follow a transparent pricing model for any of the tiers, and the price can be finalized by contacting the Polytomic team.

More information about Polytomic can be found here, and its pricing can be found here.

Alternatives to reverse ETL tools

There are a few alternatives that can be used if you are not sure to use reverse ETL tools. You can build your own integrations using the programming languages like python. However, this might reduce the overall productivity of your team. Another good option is to use bundles capabilities from companies that offer reverse ETL functionality.

Final Words on ETL vs ELT

In this article, you have learned the difference between the two key Data Integration processes and also when to use one process over the other. 

All of this can get very complicated. It requires a lot of expertise at every level of the process. But Hevo Data can guarantee you smooth storage and processing.

Visit our Website to Explore Hevo

Hevo is a No-code data pipeline. It has pre-built integrations with 100+ 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.

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