Being part of a data integration company, People often ask me questions such as .” What does ETL or ELT mean and how are they different? When do I use ETL vs. ELT? Which one is a more modern technique?” The answers to all these questions bring me to this writing this blog where we would answer in whole what these techniques mean, how they are different and when you use them.
What is ETL(Extract, Transform, Load)?
ETL is a data integration process that involves extracting data from various sources, transforming it into the desired format, and loading it into a target system. The main objective of ETL is to convert data from one format to another for reporting, analytics, or any other purpose.
The ETL process involves three stages:
- Extraction: Data is extracted from various sources such as databases, APIs, flat files, or web services.
- Transformation: The extracted data is then transformed or manipulated to meet the requirements of the target system. This transformation could happen in the form of concatenation or mathematical operations. This includes cleaning, filtering, merging, or aggregating the data.
- Loading: The transformed data is loaded into the target system, such as a data warehouse, for analysis or reporting.
When to use ETL(Extract, Transform, Load)?
ETL is best suited for batch processing large volumes of data for on-premise environments with limited memory and processing power. It is ideal to be used in the following scenarios
- Combine data from multiple sources: ETL can combine data from various sources such as databases, flat files, and cloud-based systems, into a single destination system.
- Extract data from legacy systems: ETL can extract data from legacy systems that do not support modern integration standards.
- Automate data integration: ETL can automate the data integration process, reducing the time and effort required to integrate data manually.
- Ensure data quality: ETL can be used to ensure data quality by validating data against predefined rules and standards. This helps ensure the data is accurate and consistent across different sources.
Real-life use case: A retail company wants to analyze customer data from its point of sale (POS) systems and customer relationship management (CRM) software. The company uses ETL to extract data from these sources, transform it into a consistent format, and load it into a data warehouse for analysis.
What is ELT?
In an ELT process, data is first extracted from the source system and loaded into the target system. Once the data is loaded, the transformation step occurs within the target system, using tools such as SQL, MapReduce, or Hadoop. This approach allows for more flexibility in data processing and enables users to take advantage of the target system’s processing power and storage capabilities.
The ELT process involves three stages:
- Extract: The first stage in the ELT process is extracting data from various sources, such as databases, files, and APIs. The data is usually extracted using specialized tools and techniques, such as data connectors, APIs, and data replication software.
- Load: Once the data is extracted, it is loaded into the target system, such as a data warehouse or big data platform. The loading process can be performed using specialized tools and techniques, such as ETL tools or data-loading scripts.
- Transform: The transformation stage in the ELT process involves performing data transformations within the target system. This can include filtering, sorting, aggregating, and joining data to create new data sets or tables. The transformation step can be performed using specialized tools and techniques like SQL, Hadoop, or Spark.
- Validation: Once the data has been transformed, it is important to validate it to ensure it is accurate and consistent. This involves checking for errors, duplicates, and missing data and ensuring that the data meets any predefined quality standards.
- Analysis: The final stage in the ELT process is analyzing the data to gain insights and make data-driven decisions. This can involve using data visualization tools, statistical analysis, and machine learning algorithms to uncover patterns, trends, and insights in the data.
When to use ELT?
- When the target system is a data warehouse or big data platform: ELT can be a more efficient and cost-effective approach to integrate data when the target system is a data warehouse or big data platform that can handle large volumes of data and perform complex transformations.
- When data processing requirements are complex: ELT can be a more suitable approach when the data processing requirements are complex and require specialized tools and techniques such as Hadoop, Spark, or MapReduce. ELT enables data analysts and scientists to perform complex data transformations within the target system.
- When data sources are numerous and varied: ELT can be a more efficient approach to integrate data when the data sources are numerous and varied, as it can enable faster data loading and transformation without needing a separate ETL server or infrastructure.
- When real-time data integration is required: ELT can be a suitable approach when real-time data integration is required, as it can enable faster data loading and transformation within the target system.
- When the data transformation process is flexible: ELT can be a more suitable approach when it is flexible and can be performed within the target system itself. This can enable faster and more
Real-life usecase: A healthcare organization wants to analyze patient data from various sources such as electronic health records (EHRs), lab reports, and billing systems. The organization uses ELT to load this data into a cloud-based data warehouse and then transform it based on the needs of the business.
Key Differences between ETL and ELT
ETL and ELT vary in two key aspects: where data transformation occurs and how data is stored in data warehouses. ETL involves data transformation on a dedicated processing server, whereas ELT performs data transformation within the data warehouse. Additionally, ETL does not transfer raw data directly into the data warehouse, while ELT sends raw data directly to the data warehouse.
| Parameters | ETL | ELT |
| Data Volume | Suitable for small to medium volumes | Suitable for large volumes |
| Data Transformation | Performed on a separate server or processing engine | Performed directly within the target data warehouse |
| Raw Data Handling | Raw data is transformed before loading into the target warehouse | Raw data is directly loaded into the target warehouse and then transformed |
| Data Integration | Best suited for integrating data from multiple sources with complex transformation requirements | Best suited for data integration involving large data volumes and target data warehouses with built-in transformation tools |
| Flexibility | Limited flexibility, as transformations are done before loading data into the target system | More flexibility, as transformations can be performed within the target system and on demand |
| System Architecture | Relies on a separate ETL server, | Does not require a separate processing engine, |
| Cost | May require additional infrastructure and licensing costs for ETL tools | Can be more cost-effective, as it reduces the need for additional infrastructure and licensing costs for ETL tools |
| Performance | May have slower performance due to the need to transform data before loading | Can have faster performance, as data can be loaded and transformed in parallel within the target system |
In summary, ETL and ELT are approaches to integrating data from multiple sources into a target data warehouse. While ETL involves transforming data before loading it into the target warehouse, ELT loads raw data directly into the warehouse and performs transformations within the warehouse. Each approach has its strengths and weaknesses, and the choice between the two will depend on the project’s specific requirements, including the volume and type of data, the capabilities of the target warehouse, and the need for real-time processing. Ultimately, selecting the right data integration approach is critical for ensuring the data is transformed, loaded, and managed effectively. This can help organizations derive meaningful insights and improve decision-making.
