What Is ELT, Pros/Cons & 7 Steps to Build Your Pipeline

What Is Extract, Load, Transform (ELT)?

Extract, Load, Transform (ELT) is a data integration pattern used to handle large data sets in modern data warehousing. Unlike ETL (Extract, Transform, Load), the ELT process involves first extracting raw data from various sources, loading it directly into a data lake or data warehouse, and then performing transformations within the data warehouse environment.

This approach leverages the processing power of modern data warehouses and modern data platforms capable of transforming data directly within a data lake, making it well-suited for massive and complex datasets. ELT supports scalability, allowing organizations to manage growing volumes of data without inhibiting performance.

ELT is particularly advantageous when handling unstructured and semi-structured data due to its flexibility and adaptability. It is increasingly popular in big data ecosystems and cloud computing environments, where computational resources are abundant and transformation processes can be optimized within the data warehouse or data lake.

ELT vs ETL

These two processes are closely related but have several important differences.

What Is the Difference?

The key difference between ELT and ETL lies in the order of operations, particularly where the transformation takes place. In ETL, data is extracted from source systems, transformed according to specific business rules outside the data warehouse, and then loaded into the target warehouse. ELT loads raw data directly into the data warehouse before transformations are applied within the warehouse itself.

This distinction makes ETL more suitable for traditional data systems that may have limited processing capabilities, as it offloads transformations to external engines. ELT leverages the powerful, scalable processing capabilities of data warehouses, enabling efficient handling of large datasets.

Another difference is flexibility: ELT offers more adaptability when dealing with unstructured or semi-structured data, as the transformations can be customized post-load. In ETL, transformations need to be well-defined upfront, which can limit flexibility in handling evolving data sources.

When to Use Each Technique

ETL is often the preferred approach in legacy systems or when working with smaller, well-structured datasets where the transformation logic is straightforward and can be handled outside the warehouse. It's also useful when compliance or regulatory requirements demand data cleaning and transformation before storage.

ELT is best suited for modern big data environments where the volume and variety of data are high. It works well with cloud-based data warehouses like Snowflake or Google BigQuery, which are optimized for in-database transformations. ELT is especially beneficial when dealing with unstructured data sources such as logs or social media data.

How ELT Works

Here’s an overview of the ELT process.

Extract

The extract phase involves pulling data from various heterogeneous sources. These can include transactional databases, CRM systems, sensors, cloud services, and more. During extraction, data is gathered in its raw form without alteration, ensuring that all required data is loaded into the data warehouse.

Data extraction must be carefully managed to ensure data integrity and consistency. The process often involves scheduling regular extracts to keep data current and using techniques like change data capture (CDC) to obtain only altered data. Ensuring data quality at this stage is essential, as errors or inconsistencies can propagate through the entire pipeline.

Load

Loading refers to the process of moving extracted data into a data warehouse or data lake environment. This step sets the foundation for subsequent data analysis and transformations. It allows raw data to reside in a centralized repository where it can be accessed and processed efficiently. The process must ensure that data loads correctly without causing discrepancies in data quality or reliability.

Efficient loading requires optimizing data transfer to handle data volume, velocity, and variability. Bulk load operations are typically used to manage large datasets, while incremental loads are essential to maintain up-to-date data without overwhelming system resources. Considerations such as indexing, error handling, and transactional integrity ensure that data is correctly loaded and prepared for transformation.

Transform

Transformation in ELT happens after data is loaded into the data lake or warehouse, offering flexibility in data processing. By performing this step within the data lake or data warehouse, larger datasets can be transformed quicker due to the scalable computational resources available. Transformations include sorting, aggregating, joining, and cleaning data to align it with business requirements.

This phase allows organizations to tailor data structures to meet analytical needs, utilizing the data warehouse’s built-in functions and features. It also supports complex transformation rules and logic that can be applied uniformly across whole datasets. By leveraging the data platform’s capabilities, ELT ensures transformation processes are efficient and scalable.

Key Features of ELT Tools

Source Connectivity

Source connectivity in ELT tools refers to the ability to connect to, and retrieve data from, diverse data sources across an organization's IT landscape. This allows organizations to collect rich data from databases, cloud services, ERP systems, and third-party APIs with minimal friction. ELT tools should provide a wide array of connectors to sync data across platforms.

A source connectivity framework in ELT tools enables scalable and adaptable integration strategies, accommodating changing business needs without extensive reconfigurations. It ensures that all necessary data is accessible for loading into the warehouse or data lake.

Data Loading Capabilities

The effectiveness of ELT processes heavily relies on data loading capabilities, which ensure that data is transferred efficiently into the data warehouse or data lake. This involves optimizing data transfer speeds to handle high-volume data loads without overwhelming the network or system resources.

Effective ELT tools use batch loading, incremental loading, and real-time streaming capabilities. Advanced data loading features enable organizations to handle variations in data velocity and volume, accommodating spikes in data generation without bottlenecks. Some tools also offer support for managing data schema evolution, error handling mechanisms, and checkpoint capabilities to ensure data integrity during transfers.

In-Database Transformations

In-database transformations enable ELT tools to leverage the processing capacity of the data warehouse by executing transformations directly within the database environment. This capability reduces data movement and minimizes latency, allowing for quicker turnaround times on data processing tasks.

ELT tools that support in-database transformations enable complex operations such as joins, aggregations, and conditional transformations. By utilizing the native computing capacity of modern data warehouses, organizations can scale their transformation processes efficiently without needing additional middleware.

Data Governance and Security

Data governance and security in ELT tools requires maintaining data integrity, confidentiality, and availability throughout the data lifecycle. This is typically achieved through features such as role-based access controls (RABC), encryption during data transfer, and audit logging for all data activities.

ELT tools must comply with data protection regulations by providing mechanisms to manage data privacy and maintain accurate records of data movements and transformations. Effective data governance frameworks integrated into ELT tools ensure that data usage policies are enforced across the organization, preventing unauthorized access while supporting compliance.

Self-Service Interface

A self-service interface empowers users to independently manage and configure data processes without requiring deep technical knowledge. It supports non-technical staff in accessing, retrieving, and transforming data as needed.

Such interfaces typically include drag-and-drop capabilities, intuitive dashboards, and step-by-step guidance for managing ELT tasks. This democratization of data handling allows for faster decision-making and iterative analysis.

Pros and Cons of ELT Pipelines

Advantages of ELT pipelines include:

Scalability

As organizations accumulate more data, ELT can scale by leveraging cloud storage and processing capabilities, bypassing the bottlenecks typically encountered in data transformation operations external to the warehouse. This scalability is essential for companies experiencing rapid data growth and diverse data streams.

Faster Data Loading

The ELT process enables faster data loading by immediately transferring raw data into the data warehouse, bypassing the transformation stage at the outset. This significantly reduces bottlenecks traditionally caused by data transformation processes. By allowing raw data to reside in the warehouse, organizations can expedite data availability.

Data Warehouse Capabilities

ELT leverages the capabilities of modern data warehouses, which offer high-efficiency, in-database processing. This integration allows organizations to utilize warehouse features for data transformation processes. As data warehouses are optimized for handling large-scale operations with minimal infrastructure constraints, ELT maximizes their capabilities for faster transformations.

Support for Unstructured and Semi-Structured Data

Traditional ETL processes often struggle with varying data formats, but ELT excels by using the data warehouse as a repository for raw, unstructured data. This makes ELT suitable for integrating data from sources like social media, IoT devices, and web logs that do not conform to conventional tabular formats.

Disadvantages of ELT pipelines include:

Data Quality and Consistency

In ELT pipelines, raw data is loaded into the warehouse before transformations occur. This can lead to issues with data quality if errors, duplicates, or inconsistencies are present in the source data. Since transformations happen later, these issues can propagate throughout the warehouse, potentially affecting analytics and reporting accuracy.

Handling Large Amounts of Data

While ELT is designed to handle large data volumes, managing these datasets efficiently is complex. Loading massive amounts of raw data can place a significant strain on network bandwidth, storage, and computational resources. Dataset may also be duplicated or unnecessarily accrued over time without proper data management and governance. Additionally, the transformation stage, which takes place within the warehouse, requires substantial processing power.

Security and Compliance Gaps

A significant concern with ELT pipelines is the security and compliance risks associated with loading raw, untransformed data into the data warehouse. Sensitive information, such as PII, may be exposed before necessary protections like masking or encryption are applied. This can result in compliance violations, especially in industries subject to regulations like GDPR or HIPAA.

Resource Bloating

ELT pipelines can lead to resource inefficiencies, particularly when storing large amounts of raw data in the warehouse. Since unprocessed data is loaded first, storage costs can quickly escalate, especially in cloud environments where charges are based on usage. Additionally, in-warehouse transformations require significant computational resources, which can strain system performance and increase operational expenses if not managed properly.

Managing Dependencies and Orchestration

Managing dependencies in an ELT pipeline is more complex than in ETL processes, because transformations occur after loading. Coordinating extraction, loading, and transformation stages is essential to ensuring timely data availability. Without effective orchestration, delays in one stage can cause bottlenecks or inconsistencies in downstream transformations.

Dealing with the Cons of ELT

Here are some of the ways that organizations can ensure effective ELT processes.

Ensuring Data Quality: Validation, Testing, and Data Lineage

To mitigate the data quality issues in ELT pipelines, organizations should implement validation and testing frameworks during the loading and transformation phases. Initial validation can be introduced by checking for anomalies, missing values, or duplicates as data is ingested. This helps catch obvious errors early and ensures that flawed data doesn’t propagate throughout the warehouse.

Data lineage tools can also be used to track data sources, transformations, and usage across the pipeline. This transparency allows teams to pinpoint where issues arise and maintain consistency. Automated testing suites, such as unit tests for transformation scripts, can further enhance data quality by verifying that each transformation yields the expected outcomes.

Supporting Volume: Optimizing Extraction and Loading, Integrating with Distributed Computing Frameworks

Managing large volumes of data in an ELT pipeline requires strategies to optimize both extraction and loading processes. To minimize bottlenecks, organizations can implement parallelized data extraction, ensuring data is pulled from multiple sources simultaneously. Batch loading can further enhance performance, reducing the strain on network bandwidth.

In addition, utilizing the scalable infrastructure of modern data warehouses or integrating distributed computing frameworks like Apache Spark or Hadoop with the ELT process allows for more efficient handling of large datasets during the transformation stage. These frameworks provide scalable computation power, enabling faster processing times and reducing the risk of performance slowdowns.

Security: Access Controls, Encryption, Integration with Cloud Providers

Implementing strong access controls ensures that only authorized users can access raw data, reducing the risk of unauthorized exposure of sensitive information like personally identifiable information (PII). Role-based access control (RBAC) models can be used to restrict access to specific data sets, ensuring compliance with regulations such as GDPR and HIPAA.

Encryption is another essential element of security. Data should be encrypted both in transit and at rest, ensuring that it remains protected throughout the pipeline. Most modern cloud providers offer built-in encryption services, which can be integrated into the ELT workflow. These cloud platforms also often provide compliance certifications, making it easier to meet regulations.

Managing Dependencies and Orchestration

To effectively manage dependencies and orchestrate ELT pipelines, teams should leverage automated orchestration tools such as Dagster, Apache Airflow, or Prefect. These tools provide scheduling capabilities, ensuring that each step of the pipeline—extraction, loading, and transformation—occurs in the correct sequence.

These tools also enable monitoring and alerting for pipeline failures or delays, making it easier to troubleshoot issues. Orchestration platforms help break down complex pipelines into modular components, simplifying maintenance.

How to Build an ELT Pipeline

Here’s an overview of how to establish an ELT process.

1. Defining the Data Flow

The first step in building an ELT pipeline is to define a clear data flow that outlines how data will move through the pipeline. This includes identifying the sources of data, such as databases, APIs, or cloud services, and determining how the data will be extracted, loaded into the warehouse, and transformed.

Mapping out the data flow in advance helps ensure that all necessary data points are captured. Consideration must also be given to the structure of the data, including how unstructured, semi-structured, and structured data will be handled. Establishing the flow early enables the organization to create efficient extraction and transformation strategies.

2. Incorporate Data Governance and Quality from the Start

Integrating data governance and quality control measures early in the planning process ensures that data entering the pipeline is secure, compliant, and reliable. This includes implementing role-based access controls, encryption, and comprehensive logging to meet security and compliance standards, such as GDPR or HIPAA.

For data quality, ensure that data validation and cleaning mechanisms are in place. Using data quality frameworks that can automatically detect and flag anomalies ensures that only high-quality data is transformed and analyzed in the warehouse.

3. Choosing the Right Tools and Technologies

Selecting the appropriate tools for each stage of the ELT pipeline is crucial for its efficiency and scalability. Modern cloud-based data warehouses like Snowflake, Amazon Redshift, and Google BigQuery are well-suited for ELT processes due to their in-database transformation capabilities and flexible storage options.

Transformations can be handled using SQL-based tools like dbt (data build tool) that enable in-database transformations. Orchestration tools such as Dagster can automate and monitor the scheduling of extraction, loading, and transformation tasks.

4. Modularizing the Pipeline

Breaking down the ELT pipeline into modular components allows for easier development, testing, and troubleshooting. Modular pipelines also make it possible to reuse components across different data processes, improving scalability and maintainability.

Each phase—extraction, loading, and transformation—should be treated as a separate module with clear input and output definitions. This modularity also supports incremental updates, where only new or changed data is processed, reducing resource usage and improving performance.

5. Optimizing Data Loading

Efficient data loading is essential for keeping the ELT pipeline responsive and cost-effective. Batch processing can be used for large datasets, while real-time or incremental loads are better suited for systems requiring continuous data updates. Employing bulk load operations where possible reduces latency and network congestion, improving overall load times.

Using tools that support automatic schema evolution can also ensure that new fields or data types are handled seamlessly without manual intervention, minimizing downtime and errors.

6. Using Staging Areas for Raw Data

Establishing a staging area within the data warehouse allows for more flexible and efficient data transformations. Raw data is first loaded into staging tables, where it can be validated, cleaned, and transformed as needed. This reduces the risk of affecting production data and provides a safe environment for testing transformations.

Staging areas enable the normalization and standardization of data, ensuring that data enters the warehouse in a well-prepared state. This organization is particularly beneficial for handling unstructured data, enabling systematic parsing and formatting.

7. Implementing Version Control for Transformations

Version control systems keep track of changes in transformation scripts and configurations, allowing teams to monitor and rollback changes when necessary. This capability is invaluable for debugging, collaborating on new changes, and auditing data handling practices.

Version control supports systematic management and improvement processes, encouraging a disciplined approach to data transformations. It supports collaborative efforts among data engineers by offering a clear history of changes and promoting accountability. Consistent versioning practices also enhance compliance with data governance policies.

Orchestrating ELT Pipelines with Dagster

As a data-centric data orchestrator, Dagster allows data teams to execute their ELT pipelines as a cohesive part of their wider data platform, enabling tighter integration between data ingestion and downstream products that depend on this data.

With a built-in toolkit for data ingestion thanks to our Embedded ELT feature and integrations with leading providers like Fivetran, Dagster allows you to orchestrate your ELT workloads on your platform of choice.

Other features like Dagster Pipes and Asset Checks also help streamline some of the most challenging aspects of running ELT pipelines at scale, like setting up parallel processing on remote, distributed infrastructure and enforcing data quality checks at the point of ingestion.

To learn more about building ELT pipelines with Dagster, see this rundown of ELT Options in Dagster and this demo of an example project using Embedded ELT.