ETL vs. ELT: Pros & Cons Explained - (NEW) 2023 Update

Wondering about the difference between ETL and ELT?

The two terms are commonly used interchangeably, but they refer to different types of data pipelines.

Whether you're a data analyst or a business owner, understanding the nuances between ETL and ELT can enable you to make better and more informed decisions about your data management strategy.

This article will cover the main differences between ETL and ELT, the pros and cons, use cases, and how to pick the best approach for your organization's data needs.

ETL vs. ELT: What's the Difference?

• ETL (Extract, Transform, and Load) is a data integration method that involves extracting data from various sources, transforming it, and loading it into a destination.

• ELT (Extract, Load, Transform) is a method where the transformation happens after the data is loaded. Most commonly, it leverages the processing power of your data warehouse itself.

• ETL (Extract, Transform, Load) is like a store that sells ready-made products. It first orders all raw materials (extract) and uses them to create products (transform). It then ships the products to consumers (load).

• ELT (Extract, Load, Transform) is like a DIY warehouse. It buys raw materials (extract) and sells them to customers (load). Customers then use those materials to build products in their homes (transform).

There are two primary ways that ETL and ELT differ:

• ETL transforms data in a separate server, whereas ELT transforms data in the destination warehouse.

• ETL does not transfer raw data into the destination warehouse, whereas ELT loads raw data directly into it.

History of ELT and ELT

• ETL became popular in the 1970s when enterprise databases emerged. Early ETL systems mainly extracted data from various data sources, but there wasn't much transformation.

• In the mid-1990s, many large organizations started using on-site ETL tools to process and store large volumes of transactions.

• ELT developed with the emergence of cloud computing in the 2000s and as a response to the growing volume and the complexity of integrating data.

• ELT gained momentum with cloud-native data storage platforms like Microsoft Azure, Amazon Redshift, and Snowflake.

• Today, ELT is considered a vital tool in the modern data stack.

Is ELT Replacing ETL?

• ETL tools are great at moving data from different sources into a relational data warehouse. So if that works for you, there's no need to replace it.

• However, if you have a large volume and less-common data sources, then using ELT can improve accessibility to the data.

• If you've already invested in big data and cloud storage that needs to scale as you grow, then using ELT would be preferable to using ETL.

Traditional ELT Explained (Extract, Transform, Load)

Overview of the ELT process

Here's how the ELT process works.

Extract: In this step, data is extracted from a source system, whether that be a database, file, web service, or any platform that has data.

After the data is read from this system, it's copied to a staging area---an interim storage region used for data processing.

Transform: The data in the staging area is transformed into a format suitable for the target system.

Data teams perform this through operations like filtering, sorting, aggregating, joining, and calculating, making the data consistent and standardized for reporting and analysis.

Load: After transforming the data, it's loaded into the target system. The target system could be a data warehouse, data lake, an operational database, or any system that requires data.

Pros of ETL

• Efficient use of system resources: Since ETL pipelines transform data before loading it into a target system, it requires less storage space.

• Easier and faster data analysis: The data becomes more structured and easier to analyze after ETL.

• Compliance: Some companies deal with massive amounts of sensitive data. Adhering to GDPR, HIPAA, and CCPA in these cases becomes easier with ETL as it can omit sensitive data before loading it into the target system.

• More mature technology: ETL technology is more mature than ELT, which is comparatively new on the block.

Cons of ETL

• Requires additional hardware and software: ETL requires additional software and hardware to transform and process the data on local systems instead of in the cloud.

• Takes more time: Since all the data goes through a transformation process before storing, the ETL data ingestion process takes longer. Data is only transformed on an as-needed basis.

• Limited scalability: ETL systems have trouble handling data if it comes from multiple sources and the volume is large. This can lead to delays and performance bottlenecks.

Cloud-Based ELT Explained (Extract, Load, Transform)

Overview of the ELT process

1. Extract -- Just like ETL, data is extracted from source systems. This data can be structured, semi-structured, or unstructured. It typically involves API integration services to connect such source systems.

2. Load -- Instead of getting transformed, the extracted data is directly loaded into the target system.

3. Transform -- Data engineers can transform the data immediately after loading. Or, they can be transformed before analysis for business intelligence or other use cases.

Pros

• Flexibility of data formats: ELT can handle a wide range of data formats---allowing any format or schema type to be loaded into a target system. ELT also makes it easy to adjust and adapt to changing data requirements over time.

• Greater accessibility: While ETL generally requires an organization's IT department, ELT can run through managed processes in the cloud that are easier to oversee.

• Real-time processing: ELT enables real-time processing of data, which is useful when data needs to be analyzed immediately.

• Cost-effective: ELT can be more cost-effective than an ETL workflow since it does not require specialized ETL tools or a separate transformation layer. Also, ELT can easily automate the process of onboarding data.

Cons

• Compliance: Many regulations prohibit companies from storing sensitive data, even if they're removed in a later transformation. Additionally, some regulations prohibit storing information on the cloud, especially if the servers are outside a specific region or country's borders.

• Less mature: ELT is less mature than ETL, and the technology is still evolving.

• Increased storage requirements: Large amounts of data can require more storage space, which increases costs.

ETL vs. ELT: Detailed comparison

Function	ETL	ELT
Source Data	Mainly supports structured data from input sources.	Supports structured, unstructured, and semi-structured data types.
Data Size	Ideal for smaller amounts of data.	Can handle larger amounts of data.
History	Has been used for 30+ years and is well-developed, with experts readily available.	Is a newer technology, with fewer experts skilled in using it.
Loading time	Takes more time as data is first loaded into a staging area and then into a target system.	Since data is loaded directly into a target system, it’s faster.
Implementation difficulty	Slightly more challenging to implement since transformations need to happen separately.	Can be easier since transformations happen on an existing cloud platform.
Costs	Higher upfront cost due to setup required for local transformation hardware. Ongoing cost is lower.	Lower upfront cost but much higher ongoing costs for cloud-based transformations.
Privacy	Removal of sensitive data before loading it into the target systems keeps data private.	Loading data directly into destinations can increase risk.
Speed	ETL is slower as data is transformed before loading it into the target system.	ELT is faster as data is directly loaded and transformed in the target system.
Storage type	Can be used for on-premise or cloud-based data storage.	Optimized for cloud data warehouses.
Maintenance	Requires high maintenance as systems are the responsibility of the organization.	Relatively low maintenance as managed platforms handle updates and resolve issues.
Expertise needed	Requires experience in performing data sourcing, exportation, and transformation. Done by ETL specialists, engineers, and analysts, which are easier to find.	Requires deep knowledge of existing tools as well as strong niche skills. Done by ELT specialists, which can be harder to find.

ETL vs. ELT: Which Should You Use?

Which is better, ETL or ELT?

Whether ETL or ELT is better depends on the specific needs and goals of the organization.

You might prefer ETL if:

• You have small data sets that require less complex transformations.
• You aren't using real-time data.
• You want to ensure the data is sanitized to comply with various regulations.
• You don't want your target system to have unstructured data.
• Your target system has limited processing capabilities.

You might prefer an ELT solution if:

• You want to capture data in real-time.
• You have structured and unstructured data and want more flexibility in data transformations.
• You have the resources to manage data lakes.
• You can hire ELT experts.
• You want to load data faster.

Modern ELT: Best for Long-Term Use Cases

• Large volume of data: The ELT loading process can send large amounts of data to the target system and only transform the necessary data.

• Real-time data processing: ELT excels at real-time processing, which is ideal in certain situations, such as stock market results. Enables organizations to respond more quickly to changes in data.

• Data lake management: Data is loaded directly into a data lake, making it readily available. It doesn't require data to be structured, which is a prerequisite of traditional ETL processes.

• Cost efficiency: You don't need extra transformation tools with ELT---it uses the power of the target system to make transformations.

Traditional ETL: Best for a Few Circumstances

• Security and compliance: During the transformation process, ETL can mask or omit data before loading it into the target system--making it better than ELT for compliance with privacy and security standards like HIPAA, GDPR, CCPA and compliance with DSAR requests.

• Integration with legacy systems: ETL connectors can transfer data from legacy systems to more modern ones. They can also change the data models and schemas of the old systems to match the new ones.

• Data governance: ETL gives you more granular control over the data. You can use it to implement security measures and mask and encrypt data.

It also gives you better control over the data quality and lineage, making it easier to track changes and better understand the data.

Integrate Your Data With The Right Tools

ETL and ELT are two effective approaches for integrating data, each with strengths, weaknesses, and ideal use cases. Understanding the differences between ETL and Reverse ETL is also a good idea --- there's a need for both.

Whatever the methodology, if you're looking to pull data from long-tail applications, Portable is indispensable.

• As one of the best ETL tools, Portable offers 300+ ETL connectors for easy data extraction from various SaaS programs.

• And if the application isn't on the list, we can also build a custom connector for you.

• Once that's done, you can load data from these programs into your analytics environment--easily and quickly.

• It only takes five minutes to get started. Try Portable for free!