ETL vs ELT: what's the difference and when to use them?

Are you a data leader looking to modernize your organization's data infrastructure? An important step to consider is the transition from the Extract, Transform, Load (ETL) process in favor of a newer approach that has a number of advantages for data-driven businesses: Extract, Load, Transform (ELT).

Compared to the traditional ETL process, the novel ELT approach offers a compelling alternative that is better suited to the demands of the modern data landscape. From faster time-to-insight and increased agility to improved scalability and lower infrastructure costs, the ELT approach is becoming a game-changer for CDOs and data leaders.

In this blog post, I’m exploring the key advantages of ELT and why many businesses are already transitioning to it when modernizing their data initiatives. Whether you're a CDO responsible for data infrastructure, a data engineer tasked with building data pipelines, or a business leader seeking to drive data-driven transformation, this blog post will provide you with a comprehensive understanding of the advantages of ELT and how it can benefit your business.

Let's dive straight in and explore why ELT is the future of data integration in the age of modern data platforms.

If you come across any new terms in this post, consult our exhaustive data glossary for definitions.

What is ETL (Extract, Transform, Load)?

ETL, or Extract, Transform, Load, is a data integration approach used in business intelligence and data analytics. It’s a set of steps that organizations use to integrate and prepare data from various sources into a format that can be easily analyzed. This is crucial for gaining a unified, 360-degree view of the business and driving data-driven decision-making.

ETL has been the dominant data integration approach for decades, with its origins dating back to the 1970s and 1980s when companies started investing in data warehousing and business intelligence. The ETL process, with its focus on data transformation before loading, was well-suited for the data architectures and technologies at the time.

The three main steps in the ETL process are:

1. Extract: the first step is to extract data from its original source systems. This could include databases, spreadsheets, APIs, and a variety of other data sources. The goal is to pull all the relevant data that will be needed for analysis and reporting.
2. Transform: once the data has been extracted, the next step is to transform it into a standardized format that can be loaded into the target data store. This may involve cleaning the data, performing data type conversions, applying business rules, and merging data from multiple sources.
3. Load: the final step is to load the transformed data into the target data warehouse, data lake, or other analytical data store. This allows the data to be accessed and analyzed by business users, data analysts, and data scientists.

Use cases for ETL

Here are some of the key use cases where the ETL approach was particularly beneficial:

• Data warehousing and business intelligence: ETL remains a foundational component of traditional data warehouse and BI architectures. The ability to extract data from various sources, transform it into a consistent format, and load into a centralized data warehouse enables organizations to create a single source of truth for their business intelligence needs.
• Data governance: the ETL process, with its focus on data transformation and cleansing before loading, can be particularly valuable for organizations that prioritize data quality and governance. ETL applies data validation and standardization rules upfront, helping to ensure the high quality of the data.
• Legacy system integration: many businesses still rely on legacy systems that may not be easily integrated with modern, cloud-based data platforms. The ETL approach is usually instrumental in those cases.
• Regulatory compliance: in highly regulated industries, such as finance, healthcare, and government, the ability to maintain a clear audit trail and comply with data-related regulations is crucial. The ETL process, with its well-defined data lineage and transformation steps, can support these requirements more effectively than a pure ELT approach.
• Batch-based data processing: ETL is generally better suited for handling large volumes of data that are processed on a scheduled or periodic basis, such as monthly financial reports or quarterly sales data.
• Specialized transformations: some data transformation tasks may require the advanced capabilities and specialized tooling provided by dedicated ETL tools. This can be particularly true for complex business logic, data enrichment, or advanced data masking and anonymization requirements that may be more challenging to implement directly within the target data platform.

What is ELT (Extract, Load, Transform)?

ELT, or Extract, Load, Transform, is an alternative approach to data integration that has gained popularity in recent years as businesses began to build more flexible and agile data platforms in response to greater data processing needs.

Advancements in cloud data platforms, increasing data volumes and variety, greater demand for agility, flexibility and advancements in data transformation capabilities caused many businesses to rethink their data strategies.

Similar to ETL, the ELT process also involves three main steps:

1. Extract: the first step is to extract data from its original source systems, just as in the ETL process.
2. Load: rather than transforming the data upfront, the ELT approach loads the raw, unprocessed data directly into the target data store, such as a data warehouse or data lake.
3. Transform: after the data has been loaded, the transformation step is performed within the target data environment. This allows for more flexibility and scalability in how the data is processed and prepared for analysis.

Use cases for ELT

Here are some of the common use cases where the ELT approach can be particularly beneficial:

• Cloud data warehousing and data lakes: one of the primary use cases for ELT. The scalability and processing power of these cloud-native data platforms make them well-suited for handling the "load first, transform later" approach of ELT. This allows for faster data ingestion and subsequent data transformation directly within the target environment.
• Real-time and streaming data: ELT can be advantageous for handling real-time or streaming data, where the ability to quickly load data and perform transformations on-the-fly is crucial. You load raw data without prior transformation, so it can be ingested and processed more quickly, so it’s perfect for supporting event-driven analytics and IoT data processing.
• Agile data pipelines: the ELT process is flexible and iterative by nature, which makes it well-suited for supporting agile data initiatives, where requirements and data sources can change rapidly. The ability to transform data directly in the target environment means businesses can adapt their data pipelines to changing business needs faster.
• Data science and machine learning: ELT is a great advantage when you need to be able to quickly access and experiment with raw data. Loading the data first and then transforming it as needed for specific modeling and analysis tasks allows data scientists to more easily explore and prepare data for their machine learning models.
• Data warehouse modernization: businesses looking to modernize their legacy data warehouses can rely on ELT to facilitate the migration to cloud-based platforms. The ELT approach allows for a more gradual and iterative transition, where raw data can be loaded into the new cloud environment and transformations can be gradually migrated and optimized over time.
• Regulatory compliance and auditing: In some regulated industries, the ability to maintain a clear data lineage and audit trail is crucial. The ELT approach, with its separation of loading and transforming data, can provide better visibility and control over the data integration process, supporting compliance and auditing requirements.

While ELT may not be the optimal choice for all data integration scenarios, it has emerged as a valuable approach for businesses looking to adapt their data strategies to build more agile, scalable, and cost-effective data platforms that can keep pace with rapidly evolving business needs and data requirements.

Advantages of ETL over ELT

While the ELT approach has gained significant traction in recent years, there are still certain advantages to the traditional ETL process that make it a preferred choice in some business and technical scenarios.

Business advantages of ETL

• Reduced workload on target systems: performing the heavy lifting of data transformation outside of the target data environment makes ETL helpful at minimizing the processing load on the data warehouse or data lake. That improves overall system performance and stability.
• Improved data quality: in the ETL process, the transformation step occurs before the data is loaded into the target system. This allows organizations to apply rigorous data cleansing, validation, and standardization rules upfront, ensuring higher-quality data is available for analysis and decision-making.
• Better data governance: ETL provides more control and visibility over the data transformation process, making it easier to maintain data lineage, auditability, and compliance with regulatory requirements.

Technical advantages of ETL

• Specialized transformation capabilities: ETL tools often provide a rich set of data transformation capabilities, including complex business logic, data masking, and data enrichment. This can be more challenging to implement directly within the target data platform.
• Easier maintenance and troubleshooting: the clear separation of concerns in ETL (extract, transform, load) can make it easier to maintain and troubleshoot the data integration process, especially as the complexity of the data landscape grows.
• Specialized optimization and tuning: ETL tools are often designed with advanced optimization and tuning capabilities to ensure efficient data processing, which can be more challenging to achieve on a general-purpose data platform.

While ELT has its own set of advantages, particularly in terms of flexibility and scalability, the ETL approach remains a valuable option for organizations that prioritize data quality, governance, and performance over pure agility. The choice between ETL and ELT ultimately depends on the specific requirements, constraints, and data landscape of the organization.

Advantages of ELT over ETL

As organizations continue to modernize their data architectures and embrace more flexible, cloud-based approaches, the Extract, Load, Transform process has emerged as a compelling alternative to the traditional Extract, Transform, Load model. Here are some of the key advantages of the ELT approach:

Business advantages of ELT

• Faster time-to-insight: loading the raw, unprocessed data into the target data store first and then transforming it allows organizations to get their data into the hands of analysts and decision-makers more quickly. This can be particularly beneficial in fast-paced, rapidly evolving business environments. ELT can be advantageous for other data-related metrics as well.
• Lower infrastructure costs: ELT can reduce the need for expensive ETL tools and servers, as the transformation logic is executed within the target data platform, which may already be part of the organization's cloud-based infrastructure.
• Easier scalability: ELT can handle large data volumes and more complex transformations, as the heavy lifting is done within the powerful, scalable data warehouse or data lake environment, rather than in a separate ETL tool or server.
• Increased agility and flexibility: the ELT approach enables more agile and iterative data transformation, as changes can be made directly in the target data environment without having to re-engineer the entire data integration pipeline. This supports the ability to quickly adapt to changing business requirements.

Technical advantages of ELT

• Leveraging target platform capabilities: by performing the transformation step within the target data environment, ELT allows organizations to take advantage of the advanced processing power, storage, and analytical capabilities of modern data platforms, such as cloud-based data warehouses and data lakes.
• Greater simplicity: the ELT approach simplifies the data integration process by eliminating the need for a separate ETL tool or server, which can reduce the overall complexity of the data architecture.
• Improved testability and debugging: performing transformations directly in the target data environment can make it easier to test, debug, and troubleshoot the data integration process, as the data and transformation logic are all contained within a single platform.
• Broader connectivity: many modern data platforms offer a wide range of native connectors and integrations, allowing organizations to extract data from a diverse set of sources directly into the target environment, without the need for a separate ETL tool.

ETL vs ELT: key differences

The choice between ETL and ELT depends on the specific requirements, constraints, and data landscape of the organization. Both approaches have their advantages, and many data-driven businesses use a combination of the two to meet their data integration and processing needs.

The key differences between ETL vs ELT are:

• Timing of transformation: in ETL, the transformation step happens before the data is loaded into the target storage. In ELT, the transformation step happens immediately after data loading.
• Location of transformation: in ETL, the transformation logic is typically executed in a separate ETL tool or server. In ELT, the transformation logic is executed within the target data environment, such as a data warehouse, data lake, data mart or others.
• Flexibility and scalability: ELT is generally more flexible and scalable, as the transformation can be adapted and optimized directly within the target data environment, which often has more processing power and storage capabilities. ETL can be more rigid, as changes to the transformation logic may require re-engineering the entire ETL pipeline.
• Time-to-insight: ELT can often provide faster time-to-insight, as the raw data can be loaded more quickly, and the transformation can be performed as needed. ETL may take longer, as the transformation step needs to be completed before the data can be loaded and used for analysis.

When to switch from ETL to ELT?

While ETL still has its advantages, particularly in terms of data quality, governance, and performance, the ELT approach has become a compelling option for organizations looking to build more agile, scalable, and cost-effective data architectures that can keep pace with rapidly evolving business requirements.

Businesses can unlock the following advantages by switching from a traditional Extract, Transform, Load approach to Extract, Load, Transform:

When you want to modernize your existing data platform

Businesses working with data platforms that rely on ETL processes are likely noting a number of performance bottlenecks already. Forward-thinking data-driven organizations are increasingly investing in cloud-based, modern data platforms. We specialize in building such solutions for transportation and other industries and rely specifically on Snowflake Data Cloud for these purposes.

When you want to accelerate time-to-insight

Loading the raw, unprocessed data into the target data storage first and then transforming it means analysts and decision-makers get access to data faster. This can be particularly beneficial in fast-paced, rapidly evolving business environments where timely access to data is crucial.

When you seek greater agility and flexibility

The ELT approach enables more agile and iterative data transformation, as changes can be made directly in the target data environment without having to re-engineer the entire data integration pipeline. This supports the ability to quickly adapt to changing business requirements and experiment with new data sources or transformation logic.

When you seek more scalability

ELT can handle large data volumes and more complex transformations, as the heavy lifting is done within the powerful, scalable data warehouse or data lake environment, rather than in a separate ETL tool or server. This helps organizations keep pace with growing data needs and support more advanced analytics and machine learning use cases.

When you want to cut infrastructure costs

ELT can reduce the need for expensive ETL tools and servers, as the transformation logic is executed within the target data platform, which may already be part of the organization's cloud-based infrastructure. This can lead to significant cost savings, especially for organizations that are transitioning to cloud-based data architectures.

When you seek to leverage target platform capabilities

By performing the transformation step within the target data environment, ELT allows businesses to take advantage of the advanced processing power, storage, and analytical capabilities of data clouds, cloud-based data warehouses and data lakes. This can result in more efficient and optimized data transformations.

When you want to simplify your processes

The ELT approach simplifies the data integration process by eliminating the need for a separate ETL tool or server, which can reduce the overall complexity of the data architecture. This can make it easier to manage, maintain, and scale the data integration processes over time.

Is Snowflake an ETL tool?

Snowflake is not a traditional Extract, Transform, Load tool, but rather a cloud-based data warehousing and analytics platform. However, Snowflake does provide features and capabilities that can support and simplify the traditional ETL process:

• Integration with ETL tools: Snowflake can be easily integrated with various ETL tools such as Informatica, Talend, Fivetran, Matillion, and others, which can be used to extract data from different sources, transform it as needed, and then load the data into Snowflake.
• Built-in transformation capabilities: Snowflake does provide SQL-based tools for data transformation within its platform, including SQL queries as well as functions for data manipulation, data cleansing, data normalization, and more.
• Automated data loading: Snowflake offers features like Snowpipe, which enables automated and near real-time loading of data into Snowflake from various data sources, such as cloud storage (e.g., Amazon S3, Google Cloud Storage, Azure Blob Storage).
• Data pipelining: Snowflake can be integrated with data pipeline tools like Apache Airflow, AWS Glue, or Azure Data Factory to orchestrate and automate end-to-end ETL pipelines.

• External stages: Snowflake supports the use of external stages, which are cloud storage locations (e.g., Amazon S3, Google Cloud Storage, Azure Blob Storage) that can be used as sources or destinations for ETL processes.

So, while Snowflake is not an ETL tool itself, it provides a robust platform and features that enable and support ETL processes as part of a comprehensive data management and analytics solution.

Embrace the future of data integration with ELT

The ELT approach offers a compelling alternative to the traditional ETL model, particularly in the context of modern, cloud-based data platforms. By loading raw data first and then transforming it within the target environment, ELT enables faster time-to-insight, increased agility, improved scalability, and lower infrastructure costs – all of which are critical for organizations looking to stay ahead in today's rapidly evolving data landscape.

While ETL still has its place in certain scenarios, the advantages of ELT are hard to ignore. If you don’t want to get left behind, consider incorporating ELT processes into your existing data landscape. Our team at RST Data has the necessary expertise and experience to guide you through the transition from ETL to ELT.

If you’re ready to unlock the full potential of your data, just write to us through this contact form to schedule a free data strategy session and take the first step towards a more agile, scalable, and cost-effective data future.