What is extract, load, transform (ELT)?

ELT is commonly known as a counterpart to ETL (or extract, transform, load) data integration. However, unlike ETL, ELT pipelines put data transformation at the end of the data integration process, rather than the middle. This approach enables faster data movement from source to target, with data transformation then occurring within the target system.

Organizations are relying on ELT as they handle growing data estates within cloud environments. ELT allows enterprises to leverage the computing power and elasticity of cloud platforms to transform their data on an as-needed basis, at scale. Such flexibility empowers enterprises to ingest and transform data from diverse sources and formats and retain access to raw data for future use cases. It also enables them to build pipelines with low upfront costs.

While ELT was initially a batch-based process, it can support the integration of streaming data by using real-time data streaming platforms such as Apache Kafka. Other key ELT tools include data integration and data transformation solutions that integrate with major data repositories.

ELT enables enterprises to store massive volumes of data quickly and leverage the benefits of cloud-native data architecture.

ELT became an integral integration process starting around 2010 for at least two key reasons:

• surges in data volumes
• the availability of cloud computing

As organizations sought to manage unprecedentedly large data estates, they turned to cloud environments. Cloud environments can provide less expensive, more scalable storage than on-premises servers. In other words, they are often considered an ideal store for growing and diverse datasets.

Just as importantly, cloud platforms and cloud-based data warehouses enabled data transformation at scale. This model meant it was no longer necessary for data to be transformed in the middle of a data pipeline, as in traditional ETL pipelines. Instead, data can be transformed efficiently at the target system—the ELT approach.

Data engineers often prefer ELT because it is better at handling massive amounts of data and can accommodate more diverse data, including semi‑structured and unstructured data, than traditional ETL. There are also advantages to maintaining raw data availability: before executing transformations, different teams and data scientists can tailor transformation logic to meet their specific needs while retaining the raw data for future needs, ranging from analytics to machine learning.

“It’s about your use case and what you’re trying to achieve,” said Jamil Spain, a Senior Technical Advisor at IBM. “It can make sense to do loading first and then get into a sandbox and figure out what data you have. From there, you can work on transforming it to the format that you want.”

Historically, ELT was a batch-oriented process. However, advancements in data engineering now make it possible to deploy ELT with event-streaming platforms such as Apache Kafka to integrate streaming data and support real-time analytics.

“When raw data comes in, you get it into certain Kafka topics and it’s more like an evolving stream,” Spain explained. “Then I can write programming logic to summarize or make computations…I can analyze the data as it grows.”

While Kafka is an open source solution, enterprise-grade solutions such as Confluent, an IBM company, can enhance Kafka implementations with enhanced data management capabilities, secure governance, pre-built connectors and customer support.

ELT consists of three primary stages: Extract, load and transform. Each of these stages is detailed in the following sections.

During data extraction, data is copied or exported from source locations. The data can consist of many data types and come from virtually any structured or unstructured data source, including but not limited to:

• SQL and NoSQL servers
• Customer relationship management (CRM) systems
• Enterprise resource planning (ERP) systems
• APIs
• Email, text and document files
• JSON and CSV files
• Web pages

The extracted data is moved to a data storage area, such as a data warehouse or data lake. A nominal amount of data processing can take place during this stage, including some data cleansing, such as addressing missing values.

Typically, loading takes place during business hours when consumers seek to use the data for time-sensitive analysis or insights and don’t have the time to wait for traditional ETL processes.

It is possible to confuse ELT with a related process known by a nearly identical abbreviation. However, there are several distinct differences between ELT (extract, load, transform) and ETL (extract, transform, load).

The main difference between the two is the order of operations. Unlike ELT, ETL performs the transform function before the load function. The most significant differences between the two processes include:

ELT provides several advantages for users who integrate the process into their workflows.

• Faster data movement: Without in-transit transformations slowing down data movement, data arrives faster for time-sensitive use cases ranging from inventory stockout alerts to equities trading.

• Higher pipeline resilience: Decoupling of the transformation and load stages ensures that a coding error or other error in the transformation stage doesn’t break the pipeline.

• More diverse data: ELT can extract and load data from diverse sources and in different formats.

• Data reusability: Because data is stored in the raw form, different stakeholders with different needs can access the same data and apply their own transformation logic to it.

• Greater scalability: ELT leverages the power and size of cloud-based repositories to enable transformation at scale.

• Cost efficiency: Cost efficiency can be achieved through lower upfront costs and leveraging flexible cloud compute and storage resources on an as‑needed basis.

For all its benefits, implementing ELT can come with its share of challenges.

Without in-transit transformations to clean and organize data, enterprises might encounter data quality, compliance and security concerns in the raw data delivered to target systems. Data governance programs can help address these concerns by enforcing data quality and compliance standards, as well as security practices such as data encryption and role-based access controls.

While using elastic cloud resources can be cost-efficient, costs can still increase over time as data volumes grow. Using cloud cost optimization, a practice consisting of strategies and tools to help reduce cloud expenses can enable enterprises to avoid higher-than-expected bills.

Because ELT was designed to function with modern cloud infrastructure, enterprises that use on-premises infrastructure and legacy systems typically find traditional pipeline architecture—namely, ETL pipelines—a better fit for their data integration needs.

Implementing ELT workflows requires data integration and transformation tools.

Leading data integration solutions often offer the flexibility to support both ELT and traditional ETL pipelines. Other key features might include:

• A broad set of connectors to ingest data from disparate data sources
• Integration with transformation tools and data warehouses
• Integration with data streaming solutions
• No-code and low-code options and AI-assisted pipeline design, enabling more users to build pipelines faster
• Integrated observability, quality, lineage and governance for more reliable pipelines

Data integration tools that support ELT range from commercial solutions such as IBM DataStage® and Fivetran to open source solutions such as Airbyte.

The industry standard tool for data transformation is the Data Build Tool (dbt). Key data-related tasks enabled by dbt include:

• Managing complex workflows for data transformation
• Preparing data for reporting and analysis by transforming raw data into a structured format
• Creating layered, reusable models that represent different stages of data transformation
• Ensuring data transformations run in the correct sequence and can integrate with larger data workflow.

Enterprises often use dbt to transform data into data warehouses such as Amazon Redshift and Google BigQuery, data management platforms such as Snowflake and data lakehouses such as watsonx.data®.

ELT is typically used in high-volume or real-time data use environments. Examples include:

ELT enables the rapid ingestion of diverse data often critical for artificial intelligence and machine learning workloads. Because data is loaded in the raw form, it is reusable for building and training different AI models. According to a report by MIT’s Sloan School of Management, data reusability improves the performance of AI-driven business initiatives.

ELT allows enterprises to consolidate customer data from different sources to inform segmentation and personalization initiatives. For instance, ingesting and analyzing information on customer purchase histories, browsing behaviors and demographic characteristics can help e-commerce retailers design tailored offers and discounts.

Enterprises can use ELT to efficiently aggregate data across systems and then apply transformations to meet specific regulatory requirements, all while maintaining audit trails and access to the original raw data. One study found that hybrid cloud ELT systems can reduce latency in risk, finance and regulatory reporting by 40%.

ELT supports the ingestion of data from sensors, Internet of Things (IoT) devices and inventory management systems. This approach can inform analytics that improve delivery routing and inventory management. For example, analyzing ingested data on sales and order fulfillment can help retailers optimize inventory levels to avoid stockouts.