ETL and ELT are common data integration processes that are extensively used in data science and business intelligence. While leading enterprises use both practices, you might be wondering what is the difference between ETL and ELT?
Find out which data integration practice is right for you in this head-to-head ETL vs ELT comparison.
What is ETL?
ETL, which stands for Extract, Transform, and Load, is the traditional data integration and pipelining process. In ETL, unstructured data is first extracted from its source, moved to a staging area where it is transformed into structured data, and then loaded into the target system.
Once data is cleaned, validated, and made compatible with the new system, it is loaded into the target environment to gain analytical insights. In most open source ETL tools, the extracted data is only moved to the data warehouse from the processing server after it has been transformed. For instance, consider the example of Online Analytical Processing (OLAP) data warehouses, in which only relational SQL-based structures are accepted.
In most cases, the ETL process is followed to integrate data from small data sets that require complex transformation.
Key benefits of ETL
ETL is known for being a stable and fast solution that can cater to pre-defined use cases. Since data is already structured and transformed before being loaded, it leads to stable operations even in open source ETL tools.
Additionally, the ETL process makes it easier to comply with standards like GDPR, HIPPA, and CCPA. This is due to the flexibility provided by ETL as users can easily omit any sensitive data before loading it to a target system.
What is ELT?
Extract, Load, and Transform, which is commonly known as ELT, first pulls unstructured data from its source, loads it to the new system as is, and then performs transformative operations on it.
In ELT, vital operations like data cleansing, transformation, and enrichment take place in the data warehouse, making it easier for users to make multiple transformations to the raw data. Unstructured data is loaded to business intelligence systems as is, eliminating the need for performing data staging, and leading to faster time to insights.
ELT is a newer concept that has evolved due to scalable cloud-native warehouse support. For example, cloud-based data warehouses like Snowflake, Azure, and Redshift have provided the needed digital infrastructure for storing and transforming data faster.
Key benefits of ELT
ELT provides the flexibility to explore the entire data set (including real-time data) without overheads. This is because users can get all kinds of data analysis without loading additional data sets.
Low cost and easy maintenance
Due to its robust appeal, ELT processes are easier to maintain and require lower fulfillment cost. Modern ELT tools undertake automated cloud-based transformation, which further lowers the overall cost.
What is the difference between ETL and ELT?
The key differences mainly lie in the sequence in which tasks are performed. Besides that, ETL and ELT processes also differ regarding the size and type of data they undertake.
With ELT, the process of loading data has been streamlined because there’s no staging area involved, which means that the data loads faster. In ETL, the data loads slower because it is first staged and then transformed, but it also makes the output cleaner and more organized.
Here’s a quick ETL vs ELT comparison to help you decide which process is right for you.
|What is it?||Data is first extracted, then transformed into a processing server before loading to the target system||Once data is extracted from the source, it is loaded to the target system and then transformed in the target environment|
|Maturity||It has been around for 20+ years||It is a newer form of integration|
|Code-based transformation||In a secondary server (for heavy transformations)||In the database (simultaneous load and transformation)|
|Privacy||Better suited to meet security and compliance standards||Direct loading of data needs more security restrictions|
|Costing||Heavy servers can result in higher costs||Lower cost|
|Maintenance||Secondary servers require timely maintenance||Minimum maintenance|
|Data output||Structured (in most cases)||Can be structured, unstructured, or semi-structured|
|Data volume||Good for small data sets with heavy transformation needs||Good for large data sets that need speedy analysis|
|Support for DW||Yes||Yes|
|Support for data lake||No||Yes|
ETL vs ELT: Bottom Line
In a nutshell, your analytical goals and tech stack can be crucial factors for picking the right data integration practice. Most of the above factors make ELT a more scalable and efficient approach – particularly for processing complex data sets (unstructured and structured). ELT can also create a diverse BI archive and makes it easier for users to re-query raw data to caret to new use cases. On the other hand, ETL doesn’t generate complex data or support endless queries.
It also boils down to how you process data. For systems with legacy infrastructure or heavy transformations, ETL is an ideal solution. It is also recommended to comply with standards so that no sensitive data is transferred to the target system. Though, when it comes to data processing, flexibility, and time-to-insights, ELT gets the upper hand. By integrating cloud-native capabilities, ELT can help users leverage their data into true business insights.
Transform with Gathr!
Legacy ETL and ELT platforms can suffer from various challenges. For instance, most legacy tools are costly, non-flexible, and non-scalable. They also have limited transformation potential.
Don’t restrict your business goals with these limitations. Move over legacy ETL/ELT platforms with Gathr, which is a truly cloud-native, fully managed, no-code data pipeline platform. This means that you can easily create batch and streaming ETL pipelines using drag-and-drop actions by leveraging Gathr’s next-gen visual UI.
Apart from unmatched speed, high performance, and flexibility, Gathr also provides support for ETL, ELT, and even reverse ETL for cleaning, enriching, and transforming data from various sources.
Gathr is an end-to-end, unified data platform that handles ingestion, integration/ETL (extract, transform, load), streaming analytics, and machine learning. It offers strengths in usability, data connectors, tools, and extensibilty.
Gathr helped us build “in-the-moment” actionable insights from massive volumes of complex operational data to effectively solve multiple use cases and improve the customer experience.
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