ETL vs ELT: Best Use Cases

on ETL, Tutorials • October 19th, 2020 • Write for Hevo

Introduction

The explosion of big data has put a huge strain on the data warehouse architecture. Organizations handle huge volumes of different types of data including social media, customer behaviour, and big data. Organizations that have data warehouses use either extract, transform, load (ETL) or extract, load, transform (ELT) data integration method. 

ETL and ELT are the two most popular methods used by businesses to collect data from multiple sources and store it in a data warehouse that can be accessed by all users in an organization. ETL is the traditional approach to data warehousing and analytics, but the popularity of ELT has increased with technology advancements. 

In this article, we will be discussing the following:

An Overview of ETL and ELT Processes

The ETL and ELT processes are very necessary in data science because data sources, whether structured SQL databases or unstructured NoSQL databases, will rarely use similar or compatible formats. Therefore, you must clean, transform, and enrich your data sources before integrating them into one data source. 

It then becomes possible for business intelligence tools like Tableau, Looker and Data Studio to derive insights from the data. Regardless of whether an organization uses ETL or ELT, the data transformation/integration process takes the three steps given below:

  • Extract: Extraction is the process of pulling the source data from the original data source or database. For ETL, the data goes into a temporary staging area while for ELT, the data goes into a data lake storage system. 
  • Transform: Transformation is the process of changing the structure of information so that it can integrate with the target data system and the other data in the system. 
  • Load: Loading is the process of depositing information into a data storage system. 

ETL and ELT perform the above steps in a different order. So, should data transformation be done before or after loading the data into the data repository?

To answer the above question, you should understand ETL and ELT processes separately. 

Hevo, A Simpler Alternative to Perform ETL

Hevo offers a faster way to move data from databases or SaaS applications into your data warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.

Check out some of the cool features of Hevo:

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  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources that can help you scale your data infrastructure as required.
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  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
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You can try Hevo for free by signing up for a 14-day free trial.

The ETL Process

In this process, data is first extracted from the homogeneous/heterogeneous data sources and then deposited into a staging area. The data is then cleaned and transformed into the required format. Finally, the data is uploaded to the data warehouse. 

ETL is a good component of Online Analytical Processing (OLAP) based data warehouses like Microsoft Analysis Server, SAP NetWeaver, IBM Cognos, Jedox OLAP Server, etc. Since OLAP only accepts structured data, you must transform the data before loading it. The traditional ETL methods had a waiting period for the data to go through each phase.  This made them time-consuming. 

However, modern ETL solutions are much faster and easier. 

Advantages of ETL

One of the major advantages of ETL over ELT is the pre-structured nature of the OLAP data warehouse. Once the data has been transformed, ETL allows for a more efficient, speedier data analysis. 

On the other hand, ELT is not suitable when a speedy data analysis is required. Another advantage of ETL over ELT is about compliance. Companies regulated by HIPAA, GDPR, or CCPA are required to encrypt specific data fields for privacy purposes. 

ETL offers a more secure way of doing it because it transforms the data before moving it to the data warehouse. On the other hand, ELT requires users to upload sensitive data first. This data can show up in logs that SysAdmins can access. 

The ELT Process

In the ELT method, data can be loaded directly into the data warehouse after extracting it. The data doesn’t have to be moved to a temporary staging area. Data transformation is then done within the target database. 

The ELT process is used for data lakes that accept both structured and unstructured data. This means there is no need to transform the data before loading it. 

Advantages of ELT

The primary benefit of ELT over ETL is its flexibility and ease of storing new, unstructured data. ELT allows you to save any type of information, even if you haven’t transformed and structured it. It gives you immediate access to information whenever you want it. 

Other advantages of ELT include:

1. High speed

ELT allows all data to get into the data warehouse immediately. 

Users can then get the data they need for analysis and transformation. 

2. Low maintenance

ELT is cloud-based, hence, it relies on automated solutions for maintenance. 

3. Quicker loading

Since the transformation is done inside the warehouse, it reduces the time taken to load the data to its final destination. 

ETL vs ELT 

The following chart is a comparison of ETL and ELT features:

FeatureETLELT
Availability of data in the systemTransform and load only the necessary dataLoad all the data immediately for analysis and transformation
Data sizeGood for small volumes of dataGood for large volumes of data
PriceCostlier than ELT and is suitable for small-to-medium sized businessesCheaper than ETL and is scalable. It’s affordable to businesses of all sizes
Data warehousing supportCan work with both on-premise and cloud-based data warehousesWorks with cloud-based data warehousing solutions
Loading timeLoads data slowly due to the staging processLoads data faster

Use Cases

So, when should you consider using ELT over ETL? Here are examples of prototype cases:

Use case #1: Companies with huge volumes of data

ELT is good for managing huge volumes of both structured and unstructured data. Cloud-based ELT solutions help users process large volumes of data quickly. 

Use case #2: Companies that need instant data access

ELT is a suitable option for a company that needs immediate data access. Since transformation is done as the last step, ELT gives first priority to loading of data into the data repository. 

Limitations of ETL

Here are the limitations of the ETL process:

1. Difficult to debug and fix errors

Since ETL selects data early, it is difficult to debug and fix errors later in the process. 

2. Slow to implement process changes

ETL has become outdated. It is only efficient when using the rigid structure of old data formats. 

Limitations of ELT

Here are the limitations of the ELT process:

1. More resources are required

The storage of ELT data requires more resources. 

2. May be slower

The ETL process can run slowly due to persisting each step of the process. 

Conclusion

In this article, you have learnt the difference between ETL and ELT processes and also when to use ELT over ETL. 

All of this can get very complicated. It requires a lot of expertise at every level of the process. But Hevo Data can guarantee you smooth storage and processing.

Hevo is a No-code data pipeline. It has pre-built integrations with 100+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial.

Have any further queries? Get in touch with us in the comments section below.

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