ETL Vs ELT
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two common data integration processes used to extract data from various sources, transform it to the desired format, and load it into a target data warehouse or data lake. While both ETL and ELT perform similar functions, there are some key differences between them that are worth understanding. In this blog, we’ll explore ETL vs ELT and compare them with examples and a comparison table.
ETL (Extract, Transform, Load)
ETL is a data integration process where data is first extracted from various sources, transformed to fit the desired format, and then loaded into a target system. In this process, data is first extracted from multiple sources such as databases, flat files, or APIs. Once the data is extracted, it is transformed according to a predefined set of rules to make it fit the target schema. Finally, the transformed data is loaded into the target system.
For example, let’s say a company wants to extract customer data from a CRM system, transform it to fit the format of their data warehouse, and load it into the data warehouse. In an ETL process, the data would be extracted from the CRM system, transformed by applying various business rules and data quality checks, and then loaded into the data warehouse.
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ELT (Extract, Load, Transform)
ELT is a data integration process where data is first extracted from various sources and loaded into a target system. Once the data is loaded, it is transformed within the target system according to a predefined set of rules. In this process, data is first extracted from multiple sources and loaded into a target system such as a data lake or a cloud-based data warehouse. Once the data is loaded, it is transformed using various tools and technologies such as SQL, Python, or Spark.
For example, let’s say a company wants to extract customer data from a CRM system, load it into a data lake, and transform it using Spark. In an ELT process, the data would be extracted from the CRM system, loaded into the data lake, and then transformed using Spark.
ETL vs ELT: Comparison Table
To better understand the differences between ETL and ELT, let’s compare them using a comparison table:
| ETL | ELT |
| Data is Extracted first | Data is Loaded first |
| Transformation is performed outside | Transformation is performed inside |
| Suitable for structured data | Suitable for both structured and unstructured data |
| More suited for small to medium-sized datasets | More suited for large datasets |
| More prone to errors due to complex transformations | Less prone to errors as transformations are performed on the target system |
| Data processing time is higher due to transformation | Data processing time is lower due to direct loading |
| Requires significant ETL development expertise | Requires significant ELT transformation expertise |
In summary, both ETL and ELT are data integration processes used to extract, transform, and load data from various sources into a target system. The main difference between them is the order in which the data is processed, with ETL transforming data before loading it into a target system, while ELT loads data into a target system before transforming it. While ETL is more suited for small to medium-sized datasets, ELT is more suited for large datasets and unstructured data. It is essential to understand the differences between ETL and ELT to choose the right data integration process for your organization.