What is Extract, Load & Transform (ELT)?

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The Extract, Load, & Transform (ELT) process is one of the most common ways of handling data. It’s a data integration process that takes the data from your sources, puts it into a single storage system, and then modifies it into a useful format.

With this method, there’s no need for a separate transformation step since the changes are done after the data gets into the storage system. In this article, we’ll give an overview of what the ELT process is as well as its benefits, uses, and types.

What is ELT?

Many companies use ELT to manage their data more efficiently. It involves getting data, transferring it to the destination, and changing it into a usable format.

You must learn about different ways to handle information, including ELT and ETL. This way, you can apply the most suitable data integration process for your needs. ELT is the  cloud-based process that is particularly important when getting your data ready for use in business intelligence and data analytics.

ELT process

Here are the different parts of the ELT process:

Data extraction

The first step in Extract, Load, and Transform involves knowing where your data comes from. These sources can be databases, cloud platforms, or even streaming services. They're the starting point of how you'll handle your data.

Once you know your sources, you’ll need to pull out the data from its source system. How you do this depends on factors such as how much data there is and in what format it exists. It sets the stage for everything that follows.

Data loading

After extraction, the data is loaded into storage systems, such as a data warehouse. It's like moving your data from its original place to a central hub with all the other information, where your team can access and use it easily.

Once your data is in the system, you will need to do data validation. This means checking that all your data made it to the target system and is in the right format.

Data transformation

Now comes the transformation part, where you take the raw data and turn it into a format that you use for analysis and reporting. Depending on your business needs and the type, this step might involve cleaning, organizing, or summarizing the data.

Modern data warehousing systems offer powerful tools if you’re wondering how to transform your data. You can use its computational strength to make these changes efficiently.

Data storage and organization

After transformation comes data modeling, where you decide how to arrange your data in the warehouse. You can choose from structures like star schema or snowflake schema, which entails creating relationships between tables and organizing data in a way that makes queries and analysis easier.

This step is important since the way you store your data can affect how quickly you can access it. You want to get information quickly and efficiently so that you can make data-driven decisions in real-time if you need to.

Data integration, quality, and cleansing

Throughout the ELT process, make sure to watch out for errors or inconsistencies and fix them as you go. You want to uphold the quality of the information you’ll be storing in your data center, so you should cleanse the data by removing duplicates, correcting errors, or filling in missing information. Also known as data integration, the goal is to provide a cohesive view of all your data, regardless of the source that is consistent and trustworthy. 

Data security and compliance

Security and compliance is a crucial part of every data system. When protecting your information during the ELT process, you can implement encryption, access controls, and monitoring systems.

Additionally, part of the ELT process is making sure you're following data privacy regulations to make sure you’re handling data legally and responsibly.

Monitoring and maintenance

Since you’ll be receiving data regularly, continue to monitor and maintain your ELT process. It involves checking for problems and keeping the system updated in terms of data sources and transformation techniques.

Reporting and analytics

The ELT process allows you to analyze data easily by making it accessible. You also have the option for advanced analytics through machine learning, AI, and data visualization tools.

ELT Benefits

ELT offers optimized data processing, flexibility, and scalability capabilities. Here’s an in-depth look at the advantages of using the ELT data integration process:

Faster data availability

Loading data into your target system before transforming it speeds up how quickly data becomes available for analysis through parallel processing. This means the raw data is available in the target system without waiting for the transformation step to be completed first.

While the raw data is being loaded, transformation processes can run at the same time, taking full advantage of the increased computational power provided by these systems. Parallel processing significantly reduces the time lag between data extraction and availability for analysis.

Streamlined workflows

Parallel processing also simplifies your data workflows. It cuts out the need for intermediate staging areas that are common in other data processing methods like ETL.


ELT allows you to handle large volumes of data through distributed processing and parallelization. This means that as your data grows, you can scale your infrastructure to make way for the increase in data volume.


Most organizations typically receive different data types from various sources. ELT offers the flexibility to integrate all information, whether from databases or cloud platforms, into a central system through data integration.


With ELT, there's less need for expensive on-premise hardware, as most of the  data processing happens in the cloud. This shift lowers initial investment costs and reduces ongoing maintenance expenses. It also uses resources more efficiently.

Better data transformation capabilities

When transformations occur within a data warehouse or a data lake’s powerful environment, you get higher quality and more accurate data outputs. As such, you get more comprehensive insights for better decision-making.

Continuous data refresh

ELT systems can update data models continuously through incremental loading. As new data is added or existing data is updated, the ELT process automatically refreshes the data models without the need for manual intervention. With this, you always have access to the latest information.

ELT Uses

From improving customer experiences to supporting real-time decision-making, ELT has become a crucial part of modern data strategies. Here are the varied uses of ELT:

Data consolidation and warehouse population

With data coming from CRMs, ERP systems, social media, and more, you can use ELT to bring all of it into your data warehouse or data lake. Once the data is in your storage system, ELT keeps it updated through incremental loading and consolidation.

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Real-time business intelligence and analytics

ELT also helps feed accurate data into dashboards and reporting tools to see what's happening in your business at this very moment. As such, it aids in forecasting models that predict trends, customer behavior, and market changes more accurately.

Big data processing

Whether you have a global organization or a small business, you are likely dealing with large-scale data sets. With ELT, you can manage and extract valuable insights from a huge volume of data effectively.

Moreover, big data often requires complex transformations to make it useful. The ELT process can handle any type of transformation you need, from simple sorting and filtering to more complex calculations and aggregations.

Production and supply chain management

ELT can give you a good idea of your production processes and supply chain operations through data from inventory systems, sales records, and supplier databases. This allows you to see if there are bottlenecks and find areas to improve your workflows.

In production and manufacturing, ELT helps monitor and optimize processes by providing access to real-time data. This way, you can develop better products, happier customers, and more streamlined operations.

Types of Data Sources for ELT

A key aspect of ELT is the variety of data sources it can work with. From traditional databases to cloud platforms, ELT's flexibility in data integration can prove very useful for businesses of all sizes.

Relational databases

Relational databases like MySQL, Oracle, and SQL Server are the backbone of many business data systems. Their reliability and structured framework make them a favorite of many organizations.

Legacy systems

Some companies still use legacy systems with custom or proprietary implementations. As such, integrating data, often with outdated formats, into modern workflows is a major challenge. ELT’s data integration capabilities make it easier to bring data from legacy systems into modern environments.

Cloud data warehouses

Cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake are becoming increasingly popular in ELT pipelines. In ELT, these warehouses offer powerful computation for the transformation stage.

Real-time data streams

ELT processes incorporate these streams, which usually come from IoT devices, sensors, and online transactional systems, to provide up-to-the-minute data for analysis. As such you can monitor system performance, track user interactions, and manage supply chains as data flows.

Event streaming platforms

Platforms like Apache Kafka and Amazon Kinesis facilitate event streaming, which requires high-velocity data flows. The ELT process can handle this type of data source as well, which allows you to maintain a competitive edge in rapidly changing market conditions.

SaaS platforms

Software as a Service (SaaS) platforms like Salesforce, HubSpot, and others are rich sources of operational and customer data. ELT processes extract data from these platforms via APIs. As such, businesses can integrate customer information into their central data systems easily.

Structured file formats

Structured file formats such as CSV, JSON, and XML are common in data exchange and storage. ELT processes frequently use these formats to load data into the data warehouse due to their simplicity and compatibility.

Unstructured and semi-structured data

Logs, text files, and multimedia content contain unstructured and semi-structured data types. With the ability to gather data from these sources, ELT lets you harness a more diverse range of information.

Social media channels

Social media platforms are a goldmine of user engagement data, trends, and sentiment. ELT processes tap into these platforms to extract valuable insights through natural language processing and sentiment analysis.

Open data sources

Public data sets from governments, schools, and research institutions provide information for benchmarking, market analysis, and enhancing data models. You can easily get data from these sources through ELT.

Enterprise applications

ELT can also gather critical company data from your Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems. As a result, you can get a holistic view of your business operations and customer interactions.

What’s the Difference Between ELT and ETL?

Companies can choose between two processes for handling data: ELT and ETL (Extract, Transform, Load). Knowing the differences can help companies choose the right approach for their data needs. 

Order of operations

The main difference between ELT and ETL is in their sequence of operations. As mentioned, ELT extracts the data in raw format, loads it to storage, and then transforms it right in the data warehouse. On the other hand, ETL retrieves data from the source and changes it into a suitable format before loading it into storage.

This difference impacts the speed and efficiency of data processing. ELT does the transformations within the data warehouse, taking advantage of its computational power. Meanwhile, ETL does it before loading the data, which can lead to longer processing times.

System complexity

ETL systems tend to be more complex due to the need for a separate transformation engine and data staging areas. This complexity can make ETL processes harder to set up and manage. ELT simplifies the data flow and reduces the need for additional transformation hardware or software.

Infrastructure requirements

ETL needs robust intermediate servers for data transformation, while ELT relies on the power of the target database. While the ETL system may be more complex, the ELT infrastructure's capabilities are based largely on the power of the data warehouse, which can be a problem if the storage can’t handle the volume or complexity of the data.

Suitability for different data types

ETL is traditionally used for structured data processing, where data fits neatly into tables and rows. ELT has flexible transformation capabilities and is best for organizations that use structured, unstructured, and semi-structured data.

Data storage and accessibility

In the ETL transformation process, you need temporary storage for data, which can cause management and cost challenges. ELT uses the target database’s storage capabilities, which, again, streamlines the process and allows you to access the data quickly.

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Final Thoughts

If you want a versatile and streamlined approach to data management, you can opt for the cloud-based ELT method. It can retrieve any type of information from various sources and transform the data into a format that you can load to your warehouse easily.