Snowflake ETL vs. ELT
By Mike Sargo
Chief Data and Analytics Officer and Co-Founder of Data Ideology
There are two main data movement processes for the Snowflake data warehouse technology platform.
Extract, Transform, and Load (ETL) vs. Extract, Load, and Transform (ELT).
The Cloud data integration approach has been a popular topic with our customers as they look to modernize and achieve data transformation. Once you've determined which data warehouse technology platform will best meet your organization's needs, we will work with you to determine which data movement process will be a good fit for your organization. First, let's explore the differences between Snowflake ETL vs. ELT.
Traditionally, ETL has been the data processing method. ELT is quickly becoming the preferred processing method for data warehousing and analytics. Organizations are looking to modernize their data platforms to deliver real-time insights with technologies such as Snowflake. In conjunction with those efforts, it is also in their best interest to consider leveraging a modern data integration approach.
Extract, Transform, and Load (ETL) enables:
The ETL data integration process has clear benefits.
Data is staged into a central shared storage area used for data processing. The data warehouse can then take care of simple transformations into defined styles and formats as needed.
Ease in Processing
The data is quickly processed as it moves from the remote source, to the staging and eventually to the data warehouse.
ETLs combines the data from multiple systems into one consolidated data warehouse.
Clean and Secure Data
The ETL process supports compliance requirements by facilitating data cleansing and security.
The ETL process requires hands-on data management and processing. There is a time lag with the ETL data warehousing method since the data is not accessible until the data processing is complete.
Extract, Load, and Transform (ELT) enables:
The ELT process has become more popular as many organizations are modernizing their legacy data warehouse environments for a few reasons.
Your organization can immediately access and analyze the data without waiting for it to be processed or transformed into a particular format or style.
In general, the newest technology and data integration methods are the best, particularly with real-time data requirements and leveraging cloud bases technologies.
You can process only the data you need to perform an analysis.
Ease of Use and Storage
You can quickly process and store relevant data without waiting to complete the entire pool of raw data.
The ELT process is a fast emerging method, primarily because of its flexibility and time-savings features. ELT offers a level of immediacy and accessibility that is impossible with ETL, but the new cutting-edge performance means that there are growing pains for organizations as they reengineer their data processes. As the ELT approach becomes more of a standard for developers and organizations, the growing pains will rapidly decrease. You do not have to create complex processes as you would for the ETL, but data reliability could be affected.
ETL vs. ELT: Which Process Will Work for Your Company?
The ELT process is the right solution if your company needs to quickly access and store specific data without the bottlenecks. However, it is not as well-established. Therefore, there is an evolving list of the best practices and other detailed information to process your data the most effectively and efficiently possible. For use with the cloud-based Snowflake data warehouse, ELT is often the preferred choice.
With the ELT's time and money-saving features, it is challenging to compete, particularly with how critical immediate and flexible analytics are to the fast-paced company culture. You might be familiar with or prefer the ETL data processing, which is why companies still use both the ELT and ETL processes.
At Data Ideology, we will work with you to determine which data integration tools will be best for your organization and data-driven decisions.
Written by Mike Sargo
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.
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