SSIS ETL vs SSIS ELT: 4 Critical Aspects

on Data Automation, Data Extraction, Data Integration, Data Processing, Data Replication, ETL, SQL • September 16th, 2021 • Write for Hevo

SSIS ELT FEATURE IMAGE

Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT) are popular data integration approaches. While building a Data Warehouse, most of the time is spent in ETL/ELT development. The kind of data integration approach that you choose, whether ETL or ELT, can have a significant impact on the delivery of your data integration efforts.

SQL Server Integration Services (SSIS) is a popular tool for data integration. When performing data integration tasks using SSIS, the biggest dilemma in the minds of the decision-makers is which approach to choose, whether SSIS ETL or SSIS ELT. That’s why you should understand the differences between the two so that you can zero in on one of the approaches to help you meet your needs. In this article, we will be discussing SSIS ETL vs SSIS ELT.

Table of Contents

Introduction to Microsoft SQL Server Integration Services (SSIS)

SSIS ELT: SSIS Logo
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Microsoft SQL Server Integration Service is a Data Warehousing tool that can help perform a wide range of integration tasks such as Data Extraction, Transformation, and Data Loading Transformations such as Cleaning, Aggregating, Merging Data, etc. As the name implies it enables the user to load the data from one database to another such as SQL Server, Oracle, Excel file, etc. To ensure ease of use for the user, it also contains graphical tools and window wizard workflow functions such as sending email messages, FTP operations, data sources.

This tool can be used to perform two main functions:

  • Data integration from multiple sources to provide data in a central place to the users.
  • Workflow management depends on various parameters to help in performing analytical tasks.

Introduction to ETL

SSIS ELT: ETL
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The ETL process involves extracting data from its sources, transforming the data to meet operational needs (in most cases using staging tables), and then loading the transformed data into a Data Warehouse or the target database. This data transformation approach is reasonable provided many different databases are involved in your Data Warehouse landscape. 

ETL requires you to move data from one point to another, and the transformation is done using a separate specialized engine. SSIS is a powerful tool for this process, and it performs the entire ETL process using data flows. 

Introduction to ELT

SSIS ELT: ELT
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The SSIS ELT process involves extracting data from the source, loading it into a staging table in the database, performing any necessary transformations on the data, and then loading it into a Data Warehouse or target database. The data extracted from the source into the staging table is a raw copy. This means that the data is put into the staging table with the same columns as in the source without converting the data, calculating new fields, and more. However, you can filter any unneeded columns and rows when extracting data to avoid wasting resources on unneeded data. 

In SSIS ELT, data is moved from the source to the staging table using data flows. However, the process of moving the data from the staging table to the data warehouse can be done using  T-SQL for improved performance and the fact that it is easier to code compared to using SSIS transformations. 

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SSIS ETL vs SSIS ELT

Deciding to use ETL or ELT as your data integration approach can have a huge impact on your SSIS development. Although there are differences between ETL and ELT, each involves three steps namely Extract, Transform, and Load.

However, the two approaches perform these steps in different orders. So, the question to a Data Experts is:

“Should data be transformed before or after loading it into the data repository?”

Let’s discuss how SSIS ETL and SSIS ELT compare in different core areas.

Order of  Processes

SSIS ETL Order of Processes: Data transformations are done immediately after data is extracted and moved to the staging area. Thus, the transformations are done in the staging area and outside the Data Warehouse. The data is immediately loaded into the Data Warehouse after the transformations.

SSIS ELT Order of Processes: In SSIS ELT, the data is extracted and then loaded into the target system first. Some of the data is then transformed later on an “as-needed” basis to facilitate the analysis process. Thus, the transformations happen inside the Data Warehouse or target database and there is no need for a staging area.

Compliance

SSIS ETL Compliance: Companies under the regulation of HIPAA, CCPA, or GDPR will need to mask, remove, or encrypt particular data fields for the privacy of their clients. This can involve removing the last part of an IP Address or transforming Emails to just the domain. 

Since SSIS ETL transforms data before loading it into the Data Warehouse, it provides a more secure way of doing these transformations.

SSIS ELT Compliance: In contrast, SSIS ELT requires you to upload your sensitive data first. This will show up in logs that are accessible to system admins. Also, the use of ELT to transform data can violate the EU’s GDPR compliance standards in case non-compliant data leaves the EU when it’s being uploaded to a Data Lake.

Thus, with ELT, sensitive data will be more vulnerable to exposure and hacks.

Data Size and Transformations

SSIS ELT: Data Transformation
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SSIS ETL Data Size and Transformations: ETL is a data integration process that works well with smaller data sets that need complex transformations. If the data set is huge, ETL is not considered to be efficient. ETL also works well with onsite and cloud-based Data Warehouses but requires a structured or relational data format. This means that it doesn’t perform well with unstructured data. The aggregation operations also become more complicated as the size of the data increases. 

SSIS ELT Data Size and Transformations: ELT is considered to be a good data integration approach when working with huge data sets. SSIS ELT works well with both structured and unstructured datasets. ELT performs well with cloud-based Data Warehouses to support raw, unstructured, semi-structured, and structured data types. This means that ELT works well with all types of data. If you have a powerful cloud-based target data system, you can process massive volumes of data quickly.

Waiting Time to Load Data

SSIS ETL Waiting Time to Load Data: ETL is a multi-stage process, hence, it has a longer load time than ELT. The data has to be loaded into the staging area where transformations take place before it’s loaded into a Data Warehouse. But once the data is loaded into the Data Warehouse, analysis is done in no time. Thus, ETL takes longer to load data into the data warehouse, but once loaded, the data is analyzed faster.

SSIS ELT Waiting Time to Load Data: In SSIS ELT, data is loaded faster because you don’t have to wait for transformations to be completed. The data is directly loaded into the target database or Data Warehouse. However, analysis of the data takes quite some time in ELT. Thus, although ELT loads data faster, processing the same data is slower when compared to processing in ETL.

Conclusion

In this article, you were introduced to Microsoft SQL Server Integration Services [SSIS], related data replication approaches, and developed an understanding of SSIS data integration. This is what you’ve learned in this article:

  • You’ve learned more about ETL.
  • You’ve learned more about ELT.
  • You’ve learned the differences between SSIS ETL and SSIS ELT.

Automated integration with your Data Warehouses/multiple data sources and the Analytics database can make your choice much simpler as a lot of necessary features can be integrated readily.

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Do you use Microsoft SQL Server? Share your experience of working with SSIS ETL and SSIS ELT in the comments section below.

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