What is Extract, Load & Transform (ELT)?
Summary: ELT (Extract, Load, Transform) modernises data integration by loading raw data into cloud warehouses first, then transforming it using powerful computational resources. This approach offers faster data availability, better scalability, and flexibility for handling diverse data types, making it ideal for cloud-native analytics and real-time business intelligence.
The Extract, Load, & Transform (ELT) process is one of the most common approaches to modern data integration. It's a data integration process that takes data from your sources, loads it into a single storage system, and then transforms it into a useful format.
With this method, there's no need for a separate transformation infrastructure since the modifications are performed within the target data warehouse or data lake. In this article, we'll provide 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, particularly in cloud environments. It involves extracting data, transferring it to the destination, and then transforming it into a usable format using the computational power of modern data warehouses.
You should understand different approaches to data handling, including ELT and Extract, Transform & Load (ETL), so you can apply the most suitable data integration process for your needs. ELT is particularly important in cloud-native environments and is increasingly enhanced with AI capabilities for optimised data processing.
ELT process
Here are the different components of the ELT process:
Data extraction
The first step in Extract, Load, and Transform involves identifying where your data originates. These sources can include databases, cloud platforms, streaming services, APIs, or even IoT devices. They serve as the starting point for how you'll handle your data integration workflow.
Once you've identified your sources, you need to extract the data from its source system. The extraction method depends on factors such as data volume, velocity, format, and whether you need batch or streaming ingestion. This step sets the foundation for everything that follows in your data pipeline.
Data loading
After extraction, data is loaded into storage systems, such as a cloud data warehouse, data lake, or hybrid storage solution. This is like moving your data from its original location to a centralised hub where all your information resides, and where your team can access and use it easily.
Once your data is in the target system, you need to perform data validation. This means verifying that all your data successfully transferred to the target system, maintained its integrity, and is in the expected format for subsequent processing.
Data transformation
Now comes the transformation phase, where you take the raw data and convert it into a format suitable for analysis and reporting. Depending on your business needs and data types, this step might involve cleaning, organising, aggregating, or summarising the data.
Modern cloud data warehouses offer powerful computational capabilities for data transformation. You can leverage their distributed processing power to perform these changes efficiently, often handling much larger datasets than traditional transformation approaches.
Data storage and organization
After transformation comes data modelling, where you decide how to structure your data within the warehouse. You can choose from architectures like star schema, snowflake schema, or data vault, which involves creating relationships between tables and organising data to optimise queries and analysis performance
This step is crucial since your data storage design directly affects query performance and accessibility. You want to retrieve information quickly and efficiently to support data-driven decision-making, whether for real-time dashboards or complex analytical workloads.
Data integration, quality, and cleansing
Throughout the ELT process, monitor for errors, inconsistencies, and data quality issues, addressing them systematically. You want to maintain high-quality information in your data repository, so you should cleanse data by removing duplicates, correcting errors, standardising formats, or enriching missing information.
This data integration process aims to provide a cohesive, consistent view of all your data, regardless of source, ensuring the information is trustworthy and suitable for business intelligence and analytics applications.
Data security and compliance
Security and compliance are crucial components of every data system. When protecting your information during the ELT process, implement encryption (both at rest and in transit), robust access controls, audit logging, and comprehensive monitoring systems.
Additionally, the ELT process must ensure compliance with data privacy regulations such as GDPR, PIPEDA, or industry-specific requirements, ensuring you handle data legally and responsibly throughout the entire pipeline.
Monitoring and maintenance
Since you'll be receiving data regularly, continuously monitor and maintain your ELT process. This involves checking for processing issues, performance bottlenecks, data quality problems, and keeping the system updated with new data sources and evolving transformation requirements.
Modern ELT platforms provide automated monitoring, alerting, and self-healing capabilities to minimise manual intervention and ensure reliable data processing.
Reporting and analytics
The ELT process makes data easily accessible for analysis by storing it in optimised formats within powerful computing environments. You also have options for advanced analytics through machine learning, artificial intelligence, and sophisticated data visualisation tools that can operate directly on your transformed datasets.
ELT Benefits
ELT offers optimised data processing, flexibility, and scalability capabilities, particularly in cloud environments. 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 can speed up how quickly raw data becomes available for initial analysis. This means unprocessed data is accessible in the target system without waiting for transformation processes to complete first.
However, it's important to note that while raw data is quickly available, meaningful business insights still require transformation. Modern streaming ELT approaches can provide near real-time data processing, though truly real-time processing requires specialised streaming architectures.
Streamlined workflows
ELT simplifies data workflows by eliminating the need for intermediate staging areas and separate transformation infrastructure that are common in traditional ETL approaches. This reduces infrastructure complexity and operational overhead.
Scalability
ELT enables you to handle large volumes of diverse data through the distributed processing capabilities of modern cloud data warehouses. As your data grows, you can scale your infrastructure elastically to accommodate increasing data volumes and processing demands.
Flexibility
Most organisations receive different data types from various sources. ELT offers flexibility to integrate structured, semi-structured, and unstructured information from databases, cloud platforms, and streaming sources into a central system through versatile data integration capabilities.
Cost-effectiveness
With cloud-native ELT, there's less need for expensive on-premises transformation infrastructure, as most data processing leverages the elastic compute capabilities of cloud data warehouses. This shift can lower initial investment costs and reduce ongoing maintenance expenses through more efficient resource utilisation.
Better data transformation capabilities
When transformations occur within powerful cloud data warehouse environments, you can leverage advanced processing capabilities, including SQL engines, machine learning functions, and distributed computing. This can result in higher-quality outputs and more sophisticated analytical transformations.
Continuous data refresh
ELT systems can update data models continuously through incremental loading and change data capture (CDC) mechanisms. As new data arrives or existing data changes, the ELT process can automatically refresh data models without manual intervention, ensuring you always have access to current information.
ELT Uses
From improving customer experiences to supporting data-driven decision-making, ELT has become a crucial component of modern data strategies. Here are varied applications of ELT:
Data consolidation and warehouse population
With data originating from CRMs, ERP systems, social media, IoT devices, and more, you can use ELT to consolidate all information into your data warehouse or data lake. Once data resides in your storage system, ELT maintains it through incremental loading and real-time synchronisation.
Business intelligence and analytics
ELT helps feed accurate, up-to-date data into dashboards and reporting tools, enabling you to understand current business performance. It supports forecasting models that predict trends, customer behaviour, and market changes with greater accuracy and timeliness.
Big data processing
Whether you operate a global organisation or a smaller business, you likely handle large-scale datasets. ELT enables you to manage and extract valuable insights from massive data volumes effectively, leveraging the processing power of modern cloud platforms.
Moreover, big data often requires complex transformations to become useful. The ELT process can handle various transformation types, from simple sorting and filtering to sophisticated calculations, aggregations, and machine learning model applications.
Production and supply chain management
ELT provides comprehensive visibility into production processes and supply chain operations through data from inventory systems, sales records, supplier databases, and IoT sensors. This enables you to identify bottlenecks, optimise workflows, and improve operational efficiency.
In production and manufacturing, ELT helps monitor and optimise processes by providing access to real-time operational data. This enables you to develop better products, enhance customer satisfaction, and create more streamlined operations.
Types of Data Sources for ELT
A key aspect of ELT is the variety of data sources it can accommodate. From traditional databases to modern streaming platforms, ELT's flexibility in data integration proves valuable for businesses of all sizes.
Relational databases
Relational databases like MySQL, PostgreSQL, Oracle, and SQL Server remain the backbone of many business data systems. Their reliability and structured framework make them favourites among organisations requiring transactional consistency and data integrity.
Legacy systems
Some companies still operate legacy systems with custom or proprietary implementations. Integrating data from these systems, often with outdated formats, into modern workflows presents challenges. ELT's flexible data integration capabilities facilitate bringing data from legacy systems into contemporary cloud environments.
Cloud data warehouses
Cloud data warehouses like Amazon Redshift, Google BigQuery, Snowflake, and Azure Synapse are increasingly central to ELT pipelines. These platforms provide the computational power necessary for the transformation stage and offer scalable storage and processing capabilities.
Real-time data streams
ELT processes can incorporate streaming data, typically from IoT devices, sensors, web applications, and transactional systems, to provide up-to-the-minute data for analysis. This enables you to monitor system performance, track user interactions, and manage supply chains as data flows continuously.
Event streaming platforms
Platforms like Apache Kafka and Amazon Kinesis facilitate event streaming, enabling high-velocity data flows that support real-time analytics and decision-making. ELT processes can handle these streaming data sources, allowing you to maintain competitive advantages in rapidly changing market conditions.
SaaS platforms
Software as a Service (SaaS) platforms like Salesforce, HubSpot, and others provide rich sources of operational and customer data. ELT processes extract data from these platforms via APIs, enabling businesses to integrate customer information into their central data systems seamlessly.
Structured file formats
Structured file formats such as CSV, JSON, XML, and Parquet are common in data exchange and storage. ELT processes frequently utilise these formats to load data into data warehouses due to their simplicity, compatibility, and efficient processing characteristics.
Unstructured and semi-structured data
Logs, text files, multimedia content, and social media data contain unstructured and semi-structured information. With the ability to ingest data from these sources, ELT enables you to harness a more diverse range of information for comprehensive analytics.
Social media channels
Social media platforms provide valuable user engagement data, trends, and sentiment information. ELT processes can tap into these platforms to extract insights through natural language processing and sentiment analysis capabilities.
Open data sources
Public datasets from governments, academic institutions, and research organisations provide information for benchmarking, market analysis, and enhancing analytical models. You can easily incorporate data from these sources through ELT pipelines.
Enterprise applications
ELT can also gather critical company data from your Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems. This provides a holistic view of business operations and customer interactions for comprehensive business intelligence.
What’s the Difference Between ELT and ETL?
Companies can choose between ELT and ETL (Extract, Transform, Load) processes for handling data. Understanding these differences helps companies select the right approach for their data requirements.
Order of operations
The primary difference between ELT and ETL lies in their sequence of operations. ELT extracts data in raw format, loads it into storage, and then transforms it within the data warehouse. Conversely, ETL retrieves data from sources and transforms it into a suitable format before loading it into storage.
This difference impacts processing speed and resource utilisation. ELT performs transformations within the data warehouse, leveraging its computational power. ETL transforms data before loading, which may require additional processing time and infrastructure.
System complexity
ETL systems tend to be more complex due to requirements for separate transformation engines and data staging areas. This complexity can make ETL processes more challenging to set up and manage. ELT simplifies 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 processing power of the target data warehouse. While ETL systems may be more complex, ELT's effectiveness depends largely on the data warehouse's capabilities, which could be limiting if the storage system cannot handle the volume or complexity of required transformations.
Suitability for different data types
ETL is traditionally used for structured data processing, where data fits neatly into predefined schemas and tables. ELT offers more flexible transformation capabilities and is better suited for organisations working with structured, unstructured, and semi-structured data in diverse formats.
Data storage and accessibility
ETL requires temporary storage for data during transformation, which can create management and cost challenges. ELT utilises the target system's storage capabilities, streamlining the process and enabling faster access to raw data for immediate analysis needs.
Final Thoughts
If you want a versatile and streamlined approach to data management, the cloud-native ELT method offers significant advantages. It can retrieve various types of information from multiple sources and transform data into formats suitable for loading into your data warehouse efficiently.
By 2025, organisations leading in real-time analytics and data-driven decision-making are embracing modern integration models that combine the flexibility of ELT with advanced features like AI-powered transformations and streaming data processing.