ETL vs. ELT: The Future of Data Processing in Modern Data Warehouses

With big data and advanced analytics ruling the technological world today, handling data properly and moving and preparing it correctly is critical.

This is the work for ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). Both types of process are designed to put various pieces of information into a usable form for querying and analysis, but they do so in different ways.

The market is shifting from traditional ETL processing to a more ELT-focused model, driven by the widespread adoption of cloud solution providers and their modern, powerful, and scalable data warehousing platforms. In this article, we’ll explore the differences between ETL and ELT, why ELT is increasingly preferred in cloud-based environments, and how to determine which approach best suits your specific needs.

What Is ETL?

ETL is the traditional approach to data integration. In this process, data is extracted from databases, files, and other storage systems. It is then transformed on a separate server, where it is cleaned, mapped, aggregated, and merged, and finally loaded into the target data warehouse.

This method has become the norm for large company data engineering because it allows for high quality and governance. Pre-processing data before it hits the warehouse means that organizations can have consistency and compliance.

On the downside, as data volumes increase, this method can become slow and costly. Running an ETL server around the clock adds to operational expenses, and these servers often struggle to process large datasets efficiently in time-sensitive scenarios.

What Is ELT?

ELT flips the order of transformation. Instead of cleaning the data before processing, it first extracts the data from its source systems, loads it in raw form into the destination as quickly as possible, and then performs the transformation within the destination.

Modern cloud data warehouses like BigQuery, Snowflake, Redshift, and Azure Synapse can support this because they provide a lot of elasticity in their computational power. ELT workloads leverage this computing power to process data at scale more quickly and with greater flexibility.

Why Cloud-Based Warehouses Favor ELT

Cloud data warehouses have transformed data management by offering features such as elastic scaling, cost efficiency, and the separation of storage and compute. This allows businesses to scale computing resources as needed to handle large volumes of data, without the limitations and complexities of managing physical servers themselves.

The ability to pay-as-you-go eliminates the cost limitations businesses face when trying to scale physical infrastructure. Additionally, because organizations can securely store data in a cloud data warehouse at a low cost, they’re able to retain all data in a single repository and perform processing only when needed. This approach strongly supports the use of ELT in cloud environments, especially as companies need to load data quickly and process it only when necessary.

When to Use ETL vs. ELT

ETL is often preferred in cases requiring strict data governance or when an organization uses on-premises systems or has other reasons to stage data before transforming it. For example, a bank may need to redact customer information before loading it into a warehouse to comply with regulations like the European Union’s GDPR. ETL is also well-suited for smaller, simpler datasets that don’t require distributed, cloud-scale infrastructure.

ELT works well with cloud-native data systems designed to handle large, disparate data sources, such as IoT devices and clickstream logs. By loading data directly into a warehouse, companies can support multiple departments and use cases through flexible, on-demand ELT processes. For example, an online retailer can land all its clickstream data in BigQuery, then transform it differently for marketing analysis, personalization, and inventory optimization.

Implications for Scalability and Performance

ETL processes can face scalability challenges due to the fact that transformation servers are not built to process volumes of data. Scaling ETL typically requires additional hardware, which increases the need for managing physical infrastructure.

ELT, on the other hand, leverages the computational scale of a cloud data warehouse, transforming data in place. Organizations can leverage numerous machines to handle the transformation at scale, improve performance, and achieve greater operational simplicity for large and complex queries.