ETL vs ELT: What is the Best Approach for your Data Warehouse?
Business organizations today are handling large volumes of data which are scattered across different formats. Whether your data warehouse is using a structured SQL database or an unstructured format, most information sources mostly use compatible formats. Therefore, before integrating your data into an analyzable format, you will have to clean, enrich, and transform all your data sources.
To accomplish the above\, most data warehouses use either of the following methods:
- ELT (extract, transform, load)
- ETL (extract, load, transform)
While ETL is the traditional method of data warehousing, ELT is also used commonly these days,
Regardless of whether it is ETL or ELT method, the data integration process has these three essential steps:
- Extract – refers to the process of retrieving raw data from an unstructured data pool. In the ETL method, this raw data is extracted into a temporary staging data repository and in the ELT method, it is extracted into the storage system of the data lake.
- Transform – refers to the process of restructuring and enriching of raw data so that it can be integrated with the target source data.
- Load – refers to the process of loading the structured data into a data storage system to be used by BI tools, such as Tableau, Looker, and Zoho Analytics.
The main difference between the two methods is that ETL includes a staging area for implementing data transformation.
So, to understand which approach is better for your data warehouse, It;s important that we understand each method in detail.
What is ETL?
In ETL, the data is first extracted from homogeneous or heterogeneous data sources, and then deposited into a staging area – after which it gets cleaned, enriched and transformed to the required data format. And finally, it is uploaded to the data warehouse.
ETL is an essential component of Online Analytical Processing (OLAP) based data warehouses such as IBM Cognos, SAP NetWeaver, Microsoft Analysis Server, Jedox OLAP Server. As OLAP only accepts structured data, your data must be transformed before the loading part.
Traditional ETL methods were time-consuming with a waiting period for the data to go through each stage. However, modern cloud-based ETL solutions are much easier and faster.
What is ELT?
In the ELT method of data extraction, after the data is extracted, you can directly start the loading process and move the data into the repository. There is no need to move the data into a temporary staging area. Data transformation then happens within the target database.
The ELT process works for data lakes that unlike OLAP data warehouses accept both structured and unstructured data. This means there is no mandate of transforming the data before loading it.
ETL and ELT – An example using Microsoft Azure
Now that we know what ETL and ELT mean, let us see an example of how a typical ELT and ETL workload can be implemented using Microsoft Azure.
Example 1 of an ELT pipeline- A business organization with OLTP dataset stored in SQL Server database using Microsoft Azure Synapse tool to perform data analysis and visualization using Power BI.
Here are the various stages of the data pipeline using ELT:
- Exporting the source data from SQL Server to flat files using bcp (or bulk copy) utility.
- Copying the flat files into Azure Blob Storage that serves as the temporary staging area.
- Loading the data into Azure Synapse.
- Transforming the data is transformed into a star schema – suitable for semantic modelling.
- Loading the semantic model into SQL Server Data Tools.
Example 2 of an ETL pipeline – that acquires data from various sources and orchestrates the ETL jobs using MS Azure HDInsight tool.
Orchestration is necessary to ensure that the required job runs at the appropriate time. Here are the various stages of the data pipeline using ETL:
- Ingest the file and result storage using Azure Storage or Azure Data Lake Storage repository.
- Extract and load the existing data in Azure using services such as Apache Sqoop or Apache Flume.
- Transform the data for preparation using HDInsight-supported tools like Apache Hive, Apache Pig, or Spark SQL.
ETL vs ELT – A comparison chart
|Data availability in system||Transform and load only the necessary data||Load all data immediately for transformation and analysis|
|Data lake compatibility||Not prefered||Compatible|
|Compliance||Easy to maintain GDPR, HIPAA, and CCPA compliance standards.||Requires data upload that could violate GDPR, HIPAA, and CCPA standards.|
|Data size||Suited for smaller data size||Suited for large volumes of data|
|Data warehousing support||Works with both cloud-based and on-premise data warehouses.||Works with cloud-based data warehousing solutions.|
|Price||Costlier than ELT and is best for small-to-medium sized organizations.||Comparatively cheaper and scalable – thus affordable for all business sizes.|
|Loading time||Slower loading time due to staging process||Faster loading time|
ETL or ELT: Which is right for data warehousing?
How does ELT score over ETL – when it comes to data warehousing? Here are few of its advantages:
- Flexibility and ease of storing raw and unstructured data.
- Immediate access to all the information without the mandate of transforming and structuring it first.
- High speed of data ingestion, without any heavy lifting by the data pipeline.
- Faster loading time and low-maintenance , making ELT ideal for companies regardless of the volume of their data.
So, in which cases you should consider using ELT instead of ETL? Here are some of the prototype cases:
USE CASE #1: Companies with massive amounts of data
ELT is a better solution for managing massive amounts of structured and unstructured data. With cloud-based ELT solutions, you can process large volumes of of data quickly
USE CASE #2: Companies that need immediate data access
ELT is also preferred when you need instant access to data. As transformation is the last step, ELT prioritizes fast loading of its data to the data repository.
How DataChannel can help in data warehousing
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