What is ELT? The Modern Approach to Data Integration

This is Part 2 of our 3-part Data Integration Guide series. Also read: What is ETL? & ETL vs. ELT.

ELT stands for "Extract, Load, Transform" – a modern approach to data integration that has revolutionized how businesses handle data in the cloud era. Unlike traditional ETL (Extract, Transform, Load), ELT changes the order of operations by loading raw data first and transforming it within the target system, typically a cloud data warehouse.

This approach has become the preferred method for cloud-first organizations dealing with large, diverse datasets, offering greater flexibility, scalability, and performance than traditional data integration methods.

What is ELT?

In the cloud, the proper order of the three traditional ETL steps also changes. There’s no need to clean up data on dedicated ETL hardware before loading it into your data warehouse. Instead, the cloud creates the perfect conditions for “push-down” ELT architecture:

This infographic, titled “What is ELT: A Simple Guide to Extract, Load, Transform,” explains the ELT (Extract, Load, Transform) process used in modern data integration and analytics. It visually breaks down the three core steps:

• Extract – Pulling raw data from diverse sources such as CRMs, ERPs, APIs, flat files, and IoT sensors while preserving data fidelity.
• Load – Transferring the extracted data into a centralized system, typically a data warehouse or data lake, making it ready for analytics and business intelligence.
• Transform – Cleaning, formatting, and enriching the data by filtering duplicates, standardizing formats, joining datasets, and adding calculated fields to ensure usability and trustworthiness.

The infographic emphasizes the goal of ELT: turning raw, disparate data into a structured and reliable asset that can drive analytics, BI, and AI applications.

ELT: How it Works, Benefits & When to Use: A Summary

How ELT Works

• Extract – Pull raw data from diverse sources such as databases, applications, APIs, or files while preserving data fidelity.
• Load – Move that raw data directly into a centralized data warehouse or data lake, ready for processing, without pre-transformation.
• Transform – Use the data warehouse’s compute power (often SQL-based) to clean, format, join, and enrich the data, making it ready for analytics, BI, and AI applications.

Key Benefits of ELT

• Scalability and Flexibility: Cloud-based data warehouses handle vast amounts of structured and unstructured data with ease.
• Speed: Raw data loads quickly, reducing time-to-insight and enabling near real-time analytics.
• Cost Efficiency: ELT eliminates the need for dedicated ETL servers and uses pay-as-you-go cloud resources.
• Cloud-Native Power: Modern platforms use Massively Parallel Processing (MPP) to execute transformations efficiently.
• Adaptability: Ideal for evolving data needs, from machine learning to ad-hoc analysis.

When to Use ELT

ELT is the preferred method for organizations that:

• Operate in cloud or hybrid data environments
• Use cloud data warehouses or data lakes (Snowflake, BigQuery, Redshift, Databricks)
• Need agile analytics and flexible data transformation workflows
• Manage large, diverse, or real-time data pipelines
• Want to reduce infrastructure and maintenance costs

A Brief History of ELT

2010s: The Rise of Cloud Computing & the Emergence of ELT

The introduction of cloud data platforms like Amazon Redshift, Google BigQuery, and Snowflake sparked the shift towards ELT. With cloud infrastructure offering vast scalability and on-demand computing power, organizations started loading raw data directly into data warehouses and transforming it inside the cloud.

Key Technologies: Cloud data warehouses, SQL-based transformations, cloud storage (e.g., Amazon S3).

Present Day: ELT Becomes the Standard for Cloud-First Organizations

As businesses continue to adopt cloud-first strategies, ELT has become the default method for modern data architectures. Data transformation is no longer a bottleneck, and businesses are increasingly able to handle large, complex datasets more efficiently using cloud-native tools.

Key Technologies: Modern cloud data warehouses (Snowflake, Google BigQuery), Databricks Delta Lake, MPP (Massively Parallel Processing) engines.

Why ELT Makes Sense in the Cloud

ELT is compute-intensive, but that compute-intensive activity occurs in a highly powerful and scalable environment – the cloud, rather than in an on-premises server that perhaps needs to trade-off between data transformation and other transaction handling.

Cloud-Native Advantages

Massively Parallel Processing: Cloud data warehouses can process transformations in parallel across multiple nodes, dramatically reducing processing time.

Infinite Scalability: Cloud infrastructure can scale up and down based on demand, accommodating virtually any amount of data.

Cost Efficiency: Pay-as-you-go cloud pricing models make ELT more cost-effective than maintaining dedicated ETL infrastructure.

Speed: Transformations happen where the data lives, eliminating the need to move large datasets between systems.

Modern ELT Use Cases

Big Data & Machine Learning

AI/ML workloads require vast amounts of raw data. ELT enables data scientists to load unstructured or semi-structured data (JSON, logs, IoT streams) into cloud data lakes and transform it dynamically for different use cases. The cloud is the only practical solution for large-scale machine learning and AI operations.

Cloud Migration

With the advent of cloud computing, businesses have been migrating data to the cloud to gain faster time to insight. Cloud-native ELT tools use the advantages of the cloud, including speed and scale, to load data directly to the cloud and transform it within the cloud infrastructure.

Internet of Things (IoT) Data Integration

One of the fastest growing sources of data for businesses is connected devices and systems that are part of the IoT. Whether we are talking about wearable devices or embedded sensors in places, vehicles, or equipment, the IoT is producing astronomical volumes of data. ELT technology, especially cloud-native tools, is essential to integration and transformation of data from IoT sources.

Ad-Hoc Analytics

Companies using cloud data warehouses (e.g., Snowflake, BigQuery) can leverage ELT to load data first and apply transformations as needed. This allows analysts to explore data more flexibly without predefined transformation rules, enabling agile analytics and faster insights.

Real-time Analytics

ELT enables near-real-time data processing and transformation, crucial for applications that require low-latency insights, such as financial transactions, real-time monitoring, and event-driven architectures.

Types of ELT Tools

Cloud-Native ELT Tools

Several cloud-native ELT applications have emerged that can extract and load data from sources directly into a cloud data warehouse. They can then transform data using the power and the scale of the cloud – a critical requirement when you're dealing with Big Data. These ELT tools can be deployed directly into your cloud infrastructure or hosted in the cloud as a SaaS.

Real-time Streaming Tools

Modern businesses often demand real-time access to data from different sources. Streaming ELT tools process data in real time, with distributed models and streaming capabilities, rather than in batches. This is particularly important for applications requiring immediate insights.

How Does the ELT Process Work?

ELT involves the same activities as traditional ETL, but the 'E' and 'L' portions of ELT are done in one move straight to the target data platform. The 'Transform' activity is fundamentally different – instead of transforming your data in an external ETL engine/server, you use the power of your cloud data warehouse to process the raw data you extracted and loaded.

The Three Steps of ELT

1. Extract

The process remains the same as ETL—data is pulled from various sources such as databases, APIs, files, and cloud applications. The extraction process must handle different formats, latency issues, and connectivity challenges.

2. Load

Instead of transforming data before loading, ELT moves raw data directly into a cloud data warehouse (e.g., Snowflake, BigQuery, Redshift, Databricks Delta Lake). This allows companies to store unprocessed data for future use and multiple transformation possibilities.

With ELT, you 'Extract' data from the source system and stream it to intermediate cloud storage, such as Amazon S3 or Google Cloud Storage before 'Loading' it into the target data platform. Extracted data passes through but is never persisted to disk.

3. Transform

Using SQL and cloud-native tools, ELT applies transformations after the data is loaded, utilizing the power of cloud-based massively parallel processing (MPP) databases. You facilitate transformations by generating and executing appropriate SQL on the target MPP database.

Cloud data warehouses like Snowflake, Redshift, Google BigQuery, and Azure Synapse are columnar databases, so index and record location operations are vastly quicker. They're also massively parallel databases, so the required transformations are carried out in parallel, not sequentially, with multiple nodes handling multiple transformations at the same time.The Rise of Cloud Computing and ELT

The emergence of ELT is directly tied to the rise of cloud computing in the 2010s. Traditional ETL was designed for on-premises data warehouses, where data had to be extracted, transformed, and loaded into a structured format. However, cloud platforms introduced new paradigms that changed everything.

The Shift to Cloud Data Architectures

Data Warehouses store structured data and have historically relied on ETL processes to clean and organize data before it could be analyzed. With cloud-based systems like Snowflake and Google BigQuery, scalability and performance improved, but the ETL approach still required pre-transformation, which could slow down processes.

Data Lakes, on the other hand, store raw, unstructured data and allow transformations to be done after the data is loaded. This flexibility makes ELT the better fit for cloud-first organizations, enabling data to be loaded in raw form and transformed on-demand within the cloud platform.

The shift to cloud data architectures, especially with data lakes and the power of Massively Parallel Processing (MPP), is why ELT has overtaken ETL as the preferred method for data integration in modern cloud environments.ELT Benefits and Advantages

Pros of ELT

• Scalable and cost-effective in the cloud
• Data is available immediately for multiple use cases
• Reduces infrastructure complexity—no need for a separate ETL engine
• Faster and more flexible transformations using SQL
• Handles diverse data types (structured, semi-structured, unstructured)
• Near-real-time processing capabilities
• Leverages cloud's parallel processing power

Cons of ELT

• Requires robust cloud infrastructure for optimal performance
• Compliance concerns for industries where raw data must be pre-processed
• Initial query performance may suffer if transformations aren't optimized
• Requires proper governance to manage raw data effectively

When to Choose ELT

ELT is the preferred choice for cloud-first organizations that require flexibility, scalability, and the ability to analyze vast amounts of raw data. Use cases include:

• Cloud Data Warehouses & Data Lakes, where organizations use platforms like Snowflake, Redshift, or BigQuery for analytics
• Big Data & Machine Learning, where raw data is stored and transformed on demand for AI/ML models
• Ad-Hoc & Agile Analytics, where business users need the ability to query raw data dynamically without predefined schemas
• Multiple Data Formats where semi-structured data (e.g., JSON, XML) needs to be transformed within the data warehouse
• Real-time Analytics requirements
• Cost optimization priorities, utilizing cloud-based processing over dedicated hardware

ELT Best Practices

When implementing ELT processes, consider these best practices:

• Leverage the processing power of modern cloud platforms for transformations
• Implement proper data governance to manage raw data effectively
• Use SQL-based transformations to streamline analytics workflows
• Monitor compute costs to optimize performance and expenses
• Design for scalability from the beginning
• Implement proper data quality checks within the transformation layer
• Document transformation logic for maintainability

The Future of Data Integration

The future is ELT. If you are still on-premises and your data is predictable, coming from only a few sources, then traditional ETL still works. However, that is becoming less and less the case as more businesses commit to a cloud or hybrid data architecture.

Emerging Trends

Exponential Data Growth: Data won't just continue to grow; the amount of data we see will mushroom in the next decade, requiring cloud-native solutions.

More Machine Learning and Artificial Intelligence: Preparing data for machine learning and artificial intelligence will become a more critical use case for ELT as next-best-action and digital assistant technologies continue to expand.

The Democratization of Data: In the future, data won't just be for the data professionals. Businesses want – and need – employees to make data-driven decisions, requiring flexible, self-service analytics capabilities that ELT provides.

Conclusion

ELT represents the modern approach to data integration, purpose-built for cloud environments and the demands of contemporary data analytics. By loading raw data first and transforming it using the power of cloud data warehouses, ELT offers the flexibility, scalability, and performance that modern businesses require.

As organizations continue to generate more diverse and voluminous data, ELT's ability to handle structured, semi-structured, and unstructured data in real-time makes it the preferred choice for cloud-first data strategies.

To understand how ELT compares to traditional ETL approaches and determine which is right for your organization, explore our detailed ETL vs ELT comparison guide.