Data Education Center: ETL vs. ELT: Understanding these Approaches to Data Integration

What is ETL (Extract, Transform, Load)?

ETL stands for Extract, Transform, Load, represents a traditional and widely adopted approach to data integration. Its core purpose is to gather data from varied sources, process it to fit analytical needs, and store it in a central repository such as a data warehouse or data lake.

This process not only streamlines data from multiple origins but also ensures its consistency and reliability, making it primed for insights and decision-making.

• Extraction is the initial phase where raw data is sourced from various origins. These can range from databases, CRM and ERP systems, to more modern sources like IoT sensors and online platforms. This step is critical for capturing a comprehensive data set ​​.

• Transformation follows, where the collected data undergoes cleaning, normalization, and preparation. This includes tasks like deduplication, validation, and sometimes complex operations like encryption or data summarization, ensuring the data adheres to the necessary quality and format requirements ​​.

• Loading marks the final stage, where the processed data is moved to the target storage system. This can be a batch process or a more continuous, incremental update, depending on the volume of data and the requirements of the system ​​.

ETL's evolution mirrors the advancements in database technology and the growing sophistication of data analysis tools.

Initially focused on simplifying the migration of data to relational databases for analytics, ETL processes have become more refined, supporting a broader range of data types and sources, including cloud-based systems.

This evolution enables organizations to harness vast data sets for comprehensive analysis, driving insights across various sectors​​.

What is ELT (Extract, Load, Transform)?

ELT reimagines the traditional ETL sequence by prioritizing the loading of raw data into a target system before transformation.

This method is particularly effective in handling large volumes of unstructured data, allowing for the flexibility of transforming data as needed after it has been loaded. ELT leverages the computational power of modern data warehouses, making it suitable for big data applications where immediate processing is not a prerequisite.

• Extract and Load in ELT happen almost simultaneously, with data directly transferred from the source to the storage system. This approach minimizes the delay in making raw data available for use, catering especially to environments that demand rapid data availability​​.

• Transform occurs within the data storage environment, leveraging its processing capabilities. This late-stage transformation allows for more dynamic and on-demand data manipulation, enabling organizations to adjust their analysis according to evolving business needs​​.

ETL vs. ELT

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two prominent data integration methodologies, each offering distinct advantages and considerations. Understanding the key differences between ETL and ELT is crucial for selecting the approach that best aligns with your specific data integration needs.

Key Differences Between ETL and ELT

Here's a breakdown of the core differences between ETL and ELT:

• Order of Operations: The primary distinction lies in the order of data transformation within the integration process.

  • ETL: ETL solutions prioritize data transformation. Extracted data undergoes a rigorous cleansing and standardization process before being loaded into the target system (data warehouse, data lake, etc.). This ensures high-quality, reliable information for analysis.

  • ELT: ELT tools, on the other hand, prioritize speed and flexibility. Data is extracted from various sources and loaded directly into the target system in its raw or semi-structured format. Transformations then occur within the target system itself.

• Target System: The choice of target system can also influence the preferred approach.

  • ETL: ETL software typically integrates with data warehouses, which are designed for structured data and complex analysis. The upfront transformation ensures the data aligns with the structured schema of the data warehouse.

  • ELT: ELT tools often integrate with data lakes, which can store all types of data (structured, semi-structured, and unstructured) in its native format. This flexibility allows for transformations to be tailored to specific analytical needs without modifying the raw data within the data lake.

• Data Quality: The approach you choose can impact the level of upfront effort required to ensure data quality.

  • ETL: ETL solutions address data quality issues during the transformation stage, providing a high degree of data cleanliness and consistency before loading into the target system. This minimizes the risk of errors or inconsistencies influencing analysis.

  • ELT: ELT tools load data into the target system in its raw or semi-structured format. While transformations can occur within the target system, ensuring data quality throughout the entire data lifecycle requires additional considerations, such as data governance practices and data validation techniques.

• Scalability: The ability to handle large and complex datasets is another factor to consider.

  • ETL: Transforming massive datasets before loading can be resource-intensive and time-consuming, potentially limiting scalability for big data environments.

  • ELT: ELT tools excel in big data scenarios. Loading raw data directly into the target system allows for faster processing and easier scaling as data volume increases.

Choosing Between ETL and ELT

The optimal data integration approach depends on your specific requirements. Here are some guiding principles to consider:

• Speed and Efficiency: ELT can often process data more quickly than ETL, as it reduces the steps involved in making data available for analysis. However, the complexity of the data and the transformations required can influence the overall efficiency of each approach​​.

• Data Transformation and Compliance: ETL provides granular control over data transformations, which can be crucial for meeting compliance requirements. In contrast, ELT transformations occur after loading, which might not be suitable for all regulatory environments​​.

• Data Size and Complexity: For smaller, well-defined datasets and simpler integration needs, ETL might be a suitable choice due to its emphasis on data quality and streamlined processing.

• Data Warehouse vs. Data Lake: If you're primarily focused on integrating data for complex analysis within a data warehouse environment, ETL ensures the data aligns with the structured schema of the data warehouse. If you're leveraging a data lake for big data analytics and require flexibility to store and analyze diverse data formats, ELT offers greater adaptability.

• Data Quality Requirements: If data quality is paramount and you require a high degree of data cleansing and standardization before analysis, ETL provides a controlled environment for ensuring data integrity. However, if you have robust data governance practices in place and can address data quality issues within the target system, ELT can offer faster processing times.

By carefully evaluating your specific data integration needs and considering these factors, you can make an informed decision between ETL and ELT. Both methodologies offer valuable functionalities, and the optimal choice lies in selecting the approach that empowers you to unlock the true potential of your data for insightful decision-making.
 

IRI ETL and ELT Solutions

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) represent two prominent data integration methodologies, each with its own strengths and considerations. IRI recognizes the value of both approaches and offers the tools and power businesses need to leverage the most appropriate approach for their needs.

IRI Voracity: Speed and Flexibility 

Unlike some data integration tool providers that support a single methodology, IRI software can be used in a wide range of data integration scenarios. For example, Here's how IRI solutions cater to ETL and ELT use cases:

• Streamlined ETL Processes: IRI offers proven high-performance ETL solutions within its data management platform, Voracity. Voracity can individually optimize, and at runtime combine, high volume Extract, Transform, and Load processes for various sources and targets. 

  • Fast Database Extraction: A key option is the IRI Fast Extract tool for large RDB tables called FACT. FACT boasts exceptional data extraction capabilities, efficiently handling data from diverse sources including relational databases, cloud applications, social media platforms, flat files, and sensor data. Its pre-built connectors simplify the extraction process, eliminating the need for complex coding.

  • Granular Data Transformation: Voracity uses the core data definition and manipulation program in the IRI CoSort utility called SortCL to speed and combine data transformation, reporting, masking and quality. The latter features for data validation, cleansing, standardization, and enrichment ensure the accuracy, consistency, and completeness of data before loading into the target system.

  • Optimized Data Loading: Voracity can also use the proven power of the CoSort sorting engine in SortCL to pre-sort data for bulk database loads. This bypasses less efficient sort functions in load utilities like SQL*Loader and BCP but also produces the specific loader file job syntax needed, ensuring seamless integration with your existing data infrastructure. 
     

• Enabling ELT Workflows: IRI recognizes the growing demand for big data analytics and the need for scalable data integration solutions. Voracity helps you leverage the ELT (Extract, Load, Transform) approach with:

  • Fast and Flexible Data Loading: Voracity enables the direct loading of raw or semi-structured data into data lakes, eliminating the upfront processing bottleneck associated with traditional ETL. This allows for faster data integration and facilitates big data exploration within the data lake environment.

  • In-Situ Data Transformation: Voracity provides powerful data transformation capabilities that can be applied directly within the data lake. This empowers analysts to explore and transform the data as needed for specific analyses, without modifying the raw data itself. Voracity integrates seamlessly with popular data lake platforms, allowing you to leverage their built-in processing power for data transformations.

  • Data Quality Management: While ELT offers faster processing, IRI understands the importance of data quality. Voracity provides functionalities and best practices to ensure data quality throughout the ELT process. This includes data profiling tools to identify potential issues within the raw data, data governance strategies to maintain data integrity, and data validation techniques to verify the accuracy of transformed data.

By including a comprehensive set of data integration and management capabilities, Voracity empowers you to choose the data integration approach that best aligns with your needs.

Whether you require a structured ETL process for ensuring fast, high quality data delivery, or a flexible ELT workflow for big data analytics, the data integration speed and flexibility of Voracity allow you to unlock the true potential of your data. 

For more information see: https://www.iri.com/solutions/data-integration/implement