What is ETL?
ETL (Extract, Transform, Load) has been the traditional approach for analytics for many years. This was originally designed to work with relational databases which have historically dominated the tech market.
ETL requires the transformations to happen before the loading process. Data is extracted from data sources and then deposited into a staging area. After that data is cleaned, enriched, transformed, and finally loaded into the data warehouse.
The data teams which use this approach have to predict all the use cases for the data before any analysis is even performed and then this creates transformations accordingly. This means the data teams are left waiting as they can’t access any information until the whole process is done.
In ETL the engineering team typically owns the extract and transform process, analysts have little visibility into the logic that has been used for the transformation process. Moreover, This makes it hard for analysts to understand exactly what the data represents, often leading to incorrectly drawn conclusions. Since the analysts’ team has little visibility they can’t help the engineering team in fixing the bugs with transformations code.
What is ELT?
ELT is a modern variation of ETL where data clearing, enrichment, and transformation happen after the loading process. This is enabled by the fact that modern cloud data warehouses are extremely scalable moreover they have completely separate storage for computer resources.
So the main difference between ETL and ELT is the order that these steps take place. With the transformation happening in the warehouse, it’s typically defined using SQL. This allows analysts to contribute to the need for transformation logic.
Benefits of transforming data after loading into the data warehouse
The warehouse stores the data and makes it readily available for use. You don’t have to think about how to structure the data before you load it into the warehouse. The data modeling to transform the raw data can be set up as when it is needed.
SQL language is generally preferred for the transformations in the data warehouse. Moreover, this allows the entire team to contribute to the transformation logic.
Self Service Analytics
If all of your raw data is within the warehouse, meanwhile one can use the BI tools to drill down from aggregated summary statistics to the raw data underlying them.
If you find errors in your transformation pipeline, then you can fix the bugs and can re-run the transformations to fix your data. Above all, with an ETL approach, the complete extract-load-transform process would need to be re-run.
Advantages of Using ETL Process:-
One of the biggest advantages of ETL over ELT relates to the pre-structured nature of the OLAP data warehouse. After transforming data, ETL allows for more efficient and stable analysis. Moreover, ETL is ideal when the task requires speedy analysis.
Another significant advantage for ETL over ELT relates to compliance. Often companies regulated by GDPR, HIPPA, or CCPA need to remove masks or encrypt their specific data fields to protect the privacy of their clients.
Finally, as a data integration process, ETL has existed for over two decades, this means that there are many well-developed ETL tools and platforms available to assist with data extraction, transformation, and loading needs.
Advantages of Using ELT Process:-
The primary advantage of ELT over ETL relates to the flexibility and ease of storing new, unstructured data. With ELT, one can save any type of information – even if one doesn’t have the time or ability to transform and structure it first. Furthermore, you don’t have to develop complex ETL processes before data ingest and saves developers from complex issues.
The ELT process has many other benefits. Let’s know them in brief:-
- ELT is comparatively higher in speed.
- This process requires low maintenance cost, therefore it is mostly preferred.
- Quicker loading is one of the most important benefits of the ELT process.
Most of us always get confused in knowing these 2 processes. There are lots of differences in both. Let’s know each of them in detail.
ETL vs ELT Comparison:-
|The staging server transforms the data and then transfers the same to data warehouse DB.||Here data remains in the DB of the data warehouse.|
|ETL server/staging area handles the whole transformation process.||The target system performs the transformation process.|
|Used for a small amount of data and is also used for compute-intensive transformations.||Used for a high amount of data.|
|The ETL process loads only the important data, as identified at design time.||This process involves development from the output-backward and loading only relevant data.|
|Time-Load||Data first loaded into staging and later loaded into a target system.||Data loaded into target system only once and is faster as compared to ETL.|
|Small and medium businesses have to pay a lot of money to install this.||Low entry cost using online software as a service platform.|
|Most tools have unique hardware requirements which are expensive too.||Being Saas hardware cost is not a major issue.|
Data Lake Support
|Does not supports.||Allows the use of data lake support with unstructured data.|
|Overwrites existing column and pushes the same to the targeted platform.||Easily add the calculated column to the existing table.|
|Complexity increases with the further addition of data in the dataset.||The power of the targeted platform can process a significant amount of data quickly.|
|It needs high maintenance as you need to select data to load and transform.||Low maintenance as data is always available.|
|The process is used for over two decades. It is well documented and best practices easily available.||Relatively new concept and complex to implement.|
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