Data is the fuel that powers your business.

You need data to drive product development, marketing campaigns, and customer service efforts. But collecting and analyzing this data can be time-consuming and expensive if you're not leveraging machine learning.

That's why companies need to ensure they can easily access all of their data in one place so they can set up a system for collecting, storing, and using it to improve their business.

What is ETL?

ETL, or Extract, Transform and Load, is a method for extracting data from one system and formatting it for use in another.

ETL is a process that occurs in many industries, including financial services, telecommunications, and healthcare. The data can be extracted from multiple sources and converted into the appropriate format for use by a target application.

After the extraction phase, you must transform the data into a form that the target application can utilize. Once this transformation has been completed, you can load it into the target system.

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What is ELT?

Extract, load, transform (ELT) is a data integration process that involves extracting data from one or more sources, loading the data into a staging area or data warehouse, and then transforming the data into a format that is suitable for querying and analysis.

The ELT process typically involves the following steps:

  1. Extract: Data is extracted from various sources, such as databases, flat files, or APIs.
  2. Load: The extracted data is loaded into a staging area or data warehouse.
  3. Transform: The data is transformed and cleaned to make it suitable for querying and analysis. It may involve merging data from multiple sources, applying quality rules, and aggregating data.
  4. Load: The transformed data is loaded into a data warehouse or other storage system for querying and analysis.

ELT is often contrasted with ETL (extract, transform, load), which follows a similar process but with the transformation step occurring before the data is loaded into a destination system. The choice between ELT and ETL depends on the system's specific needs and capabilities.

ETL vs. ELT: Side-by-Side Comparison

ETL

ELT

Definition

Data is extracted, transformed, and loaded into another system.

Data is removed, loaded, and changed within the destination system.

Extract

API connectors to extract raw data.

API connectors extract raw data.

Transform

Data is transformed on processing servers.

The data is converted inside the target system.

Load

Data is loaded into a destination system after being transformed.

Raw data is loaded directly into the target system for immediate analysis.

Speed

The process of extracting, transforming, and loading data into a destination system is time-consuming.

Unlike conventional ETL tools, ELT is much faster; data is loaded directly into a destination system and transformed in parallel.

Code-Based Transformations

The secondary server is best for computationally intensive transformations and data pre-cleansing.

In-database modifications provide speed and efficiency in a single step.

Maturity

Modern data-warehousing techniques have been in use for two decades; the best practices for these techniques are well-known, as are the protocols used to implement them.

ELLT is a newer form of data integration; it allows less documentation, and experience.

Privacy

Pre-load transformation can help you eliminate PII in your database, making it easier to comply with HIPPA regulations.

Direct loading of data should be used only in settings where extra precautions are taken to safeguard privacy.

Maintenance

Adding a secondary processing server will add to the maintenance burden.

Fewer systems mean less maintenance.

Costs

Creating separate servers can be an expensive undertaking.

Simplifying your data stack will save you money.

Re-queries

Data is transformed before entering the destination system, so you cannot query raw data.

Data is loaded directly into the destination system and can be requested repeatedly.

Data Lake Compatibility 

ETL is not compatible with data lakes.

ELT but is consistent with data lake technology.

Data Output

Structured

Structured, Semi-structured, Unstructured

Data Volume

Ideal for small data sets with complex transformations.

Ideal for large datasets, it provides speed and efficiency.

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Key Difference Between ETL and ELT

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two processes that involve moving data from one system to another. While they share similarities, there are also significant differences between the two.

ETL is a process in which data is collected from multiple sources, cleansed if necessary, and moved into a single location for analysis. 

You can use this process to extract data from several different systems, including transactional and non-transactional systems. The data is then transformed into a format that makes it easier to understand before being loaded into an analytics platform or data warehouse.

ELT is similar to ETL in that it involves moving data from one system to another. 

However, while ETL focuses on extracting data from multiple sources and transforming it into a format that allows for more straightforward analysis, ELT focuses on extracting data from only one source, usually an operational system, and loading it into an analytics platform or data warehouse.

Which is Better: ETL or ELT?

The choice between ETL (extract, transform, load) and ELT (extract, load, transform) depends on the system's specific needs and capabilities. Both approaches can effectively integrate data from various sources and make it available for querying and analysis.

ETL is a traditional approach that involves extracting data from multiple sources, transforming it into a format suitable for the destination system, and then loading it into a data warehouse or other storage system. This approach is well-suited for situations where the data needs to be transformed significantly before you can load it into the destination system.

ELT, on the other hand, involves extracting data from multiple sources and loading it into a staging area or data warehouse, where it can be transformed and cleaned before being loaded into the destination system. 

This approach is well-suited for situations where the destination system has the processing power and capabilities to handle the transformation step.

Ultimately, the choice between ETL and ELT will depend on your organization's specific needs and resources. Consider factors such as the complexity of the data transformation process, the volume and velocity of the data being processed, and the capabilities of the destination system.

ELT and ETL Use Cases

Both approaches have several potential use cases, including

  1. Data migration: ETL and ELT can be used to migrate data from one system to another, such as from an on-premises database to a cloud-based data warehouse.
  2. Data integration: ETL and ELT can integrate data from multiple sources, such as databases, flat files, and APIs, into a single repository for querying and analysis.
  3. Data cleansing: ETL and ELT can be used to clean and transform data to remove errors, inconsistencies, and duplicates and to ensure that the data is consistent and usable.
  4. Data warehousing: ETL and ELT can be used to populate and maintain a data warehouse, a central repository for data used for reporting and analysis.
  5. Business intelligence: ETL and ELT can extract data from various sources and transform it into a format that can be analyzed and visualized to support business decision-making.
  6. Machine learning: ETL and ELT can extract, transform, and load data from various sources to support machine learning models and applications.
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FAQs

1. Is ELT an alternative to ETL?

Yes, ELT is an alternative to ETL. It's a much more efficient alternative.

2. What is an advantage that ELT has against ETL?

ELT has an advantage over ETL because it is much faster. You can also use ELT to implement complex transformations that would be difficult or impossible with ETL.

3. Is Snowflake an ELT?

Yes, Snowflake is an ELT.

4. Is Powerbi ETL or ELT?

PowerBI is an ELT tool.

PowerBI is a business intelligence and analytics tool that helps you analyze your data. It's made to make it easy to visualize and understand the data you have in different parts of your company, so you can see what works and what doesn't.

What are 3 tiers of ETL?

Three tiers of ETL are:

  • Extracting data from the source system
  • Transforming it into a format that can be easily handled by the target system
  • Loading the transformed data into the target system

6. Is ETL outdated?

ETL is not obsolete. It is a vital tool in data analysis and will be for years.

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  • *According to Simplilearn survey conducted and subject to terms & conditions with Ernst & Young LLP (EY) as Process Advisors