ETL vs. ELT: Key Differences and Their Role in Data Warehousing

Understanding data warehousing involves two primary processes, ETL (Extract, Transform, Load) and ELT( Extract, Load and Transform) plays crucial role to manage/ process/ analyse change the shape of source data. ETL vs ELT Differences: Knowing this is very critical for businesses as they need to choose the right strategy suitable for their data integration requirements. In this blog, we will discuss what does data wrangling exactly means and differences between processing it in a traditional vs serverless manner with key benefits of both the processes.

What is ETL (Extract, Transform, Load)?

Time-based extract load this is the plank of traditional data processing called ETL — (Extract, Transform, Load). This process involves extracting data from different sources, modifying the data to match the target system and then storing it into a Data Warehouse or Date Mart.

• Extract - Data from various source like database, API, cloud storage is fetched in raw form.
  

• Transform -This is where the information is scrubbed, formatted and changed to be fit into your target system schema so that data can work compatibly together
  

• Load - Definition of transformed data is loaded into the DWH for analysis and reporting.

Key Benefits of ETL:

• Data Quality - ETL solutions make sure that the data is cleaned right from extraction to transformation before loading into the warehouse, which further ensures good quality of data source.
  

• Structured Data - When DWH requires the data to be standardized before reaching warehouse then it is appropriate for Structured data.
  

• Early Transformation - ETL is particularly useful when data needs significant transformation before analysis.

What is ELT (Extract, Load, Transform)?

ELT stands for Extract, Load , and Transform, as it is the reverse of transformation and loading steps in ETL. Unlike in ELT, where data is extracted and loaded directly to the date warehouse without any transformation done on it.

• Extract - Data is extracted from its original sources as raw data
  

• Load -After extraction, the raw data is loaded immediately to target warehouse at load stage without using ETL process.
  

• Transform- Data transformation done within the data warehouse with SQL, Python or other rebels. This procedure utilises the processing power of modern data warehouses.

Key Benefits of ELT:

• Scalability - ELT uses the scalability function of cloud solutions (like AWS) or new-era data warehouses, which makes it more scalable to large sets.
  

• Faster Data Loading - As raw data is stored directly in the warehouse, this shortens how long it takes to get new data into the system.
  

• Flexibility - ELT processing allows it to deliver results faster, and in business cases they need fast on the ground insights.

ETL vs. ELT: Key Differences

Role of ETL and ELT in Data Warehousing

ETL in Data Warehousing

Meanwhile, ETL is a classic data warehousing approach that works well with on-premises systems or when processing structured data (e.g., relational databases) since it requires transforming the input before loading the destination. ETL: (Extract, Transform and Load) makes it possible for a Data Warehouse to ensure that the data coming in is clean and consistent ready to be analysed immediately.

ELT in Data Warehousing

ELT, however, is designed for cloud-based data warehouses and big data environments where a large volume of unstructured information requires fast loading. This takes place within the warehouse, exploiting a new world of modern cloud-based systems that have solved many scaling and processing problems.

When to Use ETL vs. ELT

Use ETL When:

• You want to use super high-quality data, only with a lot of heavy processing at load time.
  

• Your data warehouse is in your private cloud/on-premises, and/or it does not have a very efficient way to perform transformations.
  

• You have small datasets or structured data that needs some preprocessing.

Use ELT When:

• You have an enormous amount of data or a cloud-based warehouse.
  

• You need faster data ingestion & real-time data processing.
  

• Transactions are simple or can be adequately enforced in the target data warehouse.

Common Tools for ETL and ELT

• Common ETL Tools: TROCCO, Informatica, Talend, Microsoft SQL Server Integration Services (SSIS), Apache Nifi
  

• Common ELT Tools: TROCCO, Snowflake, Google BigQuery, Amazon Redshift, Fivetran

Conclusion

Both ETL and ELT are a part of the data warehousing ecosystem; which one you should go for is based on various factors like volume of raw data, how it needs to be processed as well infrastructure costs. While ETL is suited for structured data and on-premises systems, ELT is better for cloud-based environments and large datasets. This is where businesses have to assess their data requirements and process the same.

Platforms such as TROCCO provide a complete fully managed solution that works for both ETL and ELT projects but also serves few use-cases of general data integration.

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