Machine learning model training
What is Behavioral Analytics?
What is Diagnostic Analytics?
The Difference Between Data Analytics and Statistics
Data Analytics vs. Business Analytics
What is Data Analytics?
The Difference Between Data Analytics and Data Visualization
Data Analytics vs. Data Science
Quantitative vs. Qualitative Data
Data Analytics Processes
Data Analytics vs. Data Analysis
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What Is Google Analytics 4 and Why Should You Migrate?
Google Analytics 4 and eCommerce Tracking
GA4 Migration Guide
Understanding Data Streams in Google Analytics 4
GA4 vs. Universal Analytics
Understanding Google Analytics 4 Organization Hierarchy
Benefits and Limitations of Google Analytics 4 (GA4)
What are the New Features of Google Analytics 4 (GA4)?
What Is Customer Data?
Collecting Customer Data
Types of Customer Data
The Importance of First-Party Customer Data After iOS Updates
CDPs vs. DMPs
What is an Identity Graph?
Customer Data Analytics
Customer Data Management
A complete guide to first-party customer data
What Is a Customer Data Platform?
Customer Data Protection
Difference Between Big Data and Data Warehouses
Data Warehouses versus Data Lakes
A top-level guide to data lakes
Data Warehouses versus Data Marts
Best Practices for Accessing Your Data Warehouse
What are the Benefits of a Data Warehouse?
Data Warehouse Architecture
What Is a Data Warehouse?
How to Move Data in Data Warehouses
Data Warehouse Best Practices — preparing your data for peak performance
Key Concepts of a Data Warehouse
Data Warehouses versus Databases: What’s the Difference?
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
How to Create and Use Business Intelligence with a Data Warehouse
How do Data Warehouses Enhance Data Mining?
Data Security Strategies
How To Handle Your Company’s Sensitive Data
How to Manage Data Retention
Data Access Control
Data Security Technologies
What is Persistent Data?
Data Sharing and Third Parties
What is Consent Management?
What is PII Masking and How Can You Use It?
Data Protection Security Controls
Data Security Best Practices For Companies
We'll send you updates from the blog and monthly release notes.
ETL vs ELT
In the world of data analytics and data science, data pipelines are the backbone of the process. Two of the most popular pipelines are ETL and ELT. ETL stands for Extract, Transform, Load, while ELT stands for Extract, Load, Transform. Both processes involve extracting, cleaning, enriching, and transforming data for use in data analytics and data science. However, the order in which these stages are performed differs between the two processes.
ETL pipelines first extract data from various sources, then transform and enrich it, before finally loading it into a target system, such as a data warehouse. On the other hand, ELT pipelines extract data from sources and load it directly into a target system, before transforming and enriching the data as needed. Each process has its own unique characteristics and benefits, depending on the business goals and the nature of the data sources. In this article, we will delve deeper into the differences between ETL and ELT.
Before we move forward, let’s get ourselves acquainted with common keywords and concepts you will encounter in this article.
About data pipeline architecture
Data engineers need to create data pipeline architecture to collect, process, and consolidate data from various sources, such as data lakes, cloud-based data warehouses, and on-premise systems. The data pipeline architecture typically consists of several key components or processes, such as:
- Data Sources: These are the raw data inputs that businesses receive from various sources, including structured, semi-structured, and unstructured data types. Data sources can include CRM systems, website traffic data, social media feeds, and log files.
- Staging Area: This is the temporary storage area where extracted data from various sources is copied and transformed before being loaded into a target system. This area helps to ensure data quality and consistency, and it is where sensitive data can be removed or masked to comply with regulations like GDPR and HIPAA.
- Data Integration: This is the process of combining data from different sources into a single, unified view. ETL and ELT are two popular data integration methods used to transform and integrate data from multiple sources into a target database.
- Transformation Process: This process converts raw data into a meaningful format that can be analyzed and used to make data-driven decisions. ETL and ELT can be used to transform data, but there are key differences between the two. ETL tools are best suited for structured data, while ELT tools are ideal for processing unstructured data, such as social media feeds, log files, and sensor data.
- Loading Process: The process of loading the data into a target system, such as a data warehouse, for analysis and reporting. Cloud-based data warehouses like Snowflake offer scalability and processing power, making them ideal for handling large data sets.
- Business Intelligence: ETL and ELT play a critical role in building comprehensive business intelligence systems. By integrating and transforming data from different sources, businesses can derive insights and make data-driven decisions.
- Automation: One of the benefits of ETL and ELT is their ability to automate the data integration, transformation, and loading processes. This saves time and reduces errors, making data management more efficient.
Data pipeline architecture is a critical part of data management because it enables businesses to process, integrate, and analyze large data sets from multiple sources. ETL and ELT are popular data integration methods that can help businesses derive insights and make data-driven decisions. Understanding the key differences between ETL and ELT is essential for selecting the best method for specific use cases.
What is ETL?
ETL stands for Extract, Transform, and Load, which is a data pipeline used to move and transform data from various sources into a target system, such as a data warehouse. In the extract stage, data is extracted from multiple sources, including databases, flat files, websites, mobile apps, SaaS applications, or APIs. The transform stage involves cleaning, validating, and enriching the data to make it useful for analysis. Finally, in the load stage, the transformed data is loaded into a target system.
While these three stages form the basis of the ETL process, there is more to it than meets the eye. For more details on the ETL process, including architecture, design elements, and data integration methods, please refer to the ‘Three stages of the ETL pipeline’ which provides a comprehensive overview of the ETL process and will help you gain a deeper understanding of how this data pipeline works.
What is ELT?
ELT stands for Extract, Load, and Transform, which is another popular data pipeline used to transfer data from various sources into a target system, such as a data warehouse. Unlike ETL, ELT does not require data transformation before the load process. Instead, raw data is loaded directly into the data warehouse first, and data transformation takes place in the data warehouse.
In the ELT process, the raw data is loaded into a staging area, and then it is processed by the compute resources in the target data warehouse. This enables data transformation, including cleaning, enrichment, auditing, formatting, and more, to occur inside the data warehouse. The advantage of this approach is that raw data can be stored indefinitely inside the data warehouse, allowing multiple and continuous transformations to occur.
ELT is a newer form of the ETL data pipeline, and it has been made more accessible due to cloud infrastructure. Cloud-based data warehouses, such as Snowflake, offer processing power and scalability that can handle large volumes of data and support real-time data ingestion. This makes ELT a popular choice for organizations dealing with big data and complex data sets.
Differences between ETL and ELT
Businesses should be aware of the differences between ETL and ELT before choosing a data pipeline. Here are the main differences:
- ETL has been around for over 20 years and is widely used by businesses such as banks, insurance companies, and retail companies.
- ELT is a newer process that has become more accessible due to cloud-based infrastructure, and it is gaining popularity among businesses such as media companies and e-commerce platforms.
- With ETL, users must select the data they need before the transformation stage. For example, a marketing team might only need customer data for a specific period, so they will select and transform that data only.
- With ELT, all data is loaded into the data warehouse, allowing users to decide later which information they want to transform and analyze. For example, a business might want to analyze sales data for a specific product across different regions but only need to transform data from a particular period.
- ETL is more suitable for dealing with small data sets, as complex transformations on large amounts of data can cause performance issues.
- ELT is designed to handle large volumes of data, both structured and unstructured.
- ETL generally has longer waiting times as every piece of data must be transformed before it can be loaded into the data warehouse.
- ELT has faster loading times because all data is loaded straight away and users can choose which information to transform and analyze later.
- Onsite ETL processes require regular maintenance to ensure efficient performance.
- Cloud-based ETL and ELT pipelines incorporate automated solutions, making maintenance less of a concern.
- ETL allows users to audit and remove sensitive information before placing it into the data warehouse, making it easier to comply with data laws. For example, businesses that deal with personal information such as banks, healthcare providers, and government agencies.
- ELT loads data first, which could violate data protection laws, making it more suitable for businesses that handle less sensitive information or have alternative means of complying with data laws.
ETL vs ELT: Which is best?
Based on the different characteristics of ETL and ELT mentioned earlier, there is no objective winner as to which data pipeline is best. The choice ultimately depends on the specific needs and goals of your business.
For example, ETL may be more suited for businesses that require complex data transformations and advanced support. On the other hand, ELT may be more beneficial for businesses looking for faster processing times and a cloud-based data pipeline.
Regardless of the chosen data pipeline, both ETL and ELT have the advantage of consolidating all data into a single source, making it easier to clean, analyze, and apply data science techniques. By having better control of their data, businesses can make informed decisions that drive growth and improvement.
Here are different considerations to help you choose one process over another:
- Data Size: If you're dealing with a large amount of data, ELT may be the better option since it allows you to load all data into the warehouse and transform only the data you need. ETL, on the other hand, may take longer to load and transform large data sets.
- Transformation Complexity: If you need to apply complex transformations to your data, ETL may be the better option. By transforming the data before loading it into the target system, ETL can reduce the processing load on the target system and improve performance. ELT, on the other hand, involves loading the data into the target system before performing the transformations, which can result in a heavier processing load on the target system and affect scalability of the systems.
- Real-time Processing: If you need to process data in real-time or near real-time, ELT may be the better option since it allows you to load data into the warehouse quickly and apply transformations as needed. ETL, on the other hand, may take longer to load and transform data, which may not be suitable for real-time processing.
- Cloud vs. On-premises: If you're looking for a cloud-based data pipeline, ELT may be the better option since it is easier to set up and maintain in the cloud. ETL, on the other hand, may require more maintenance and resources if done on-premises.
- Compliance Requirements: If you need to comply with data protection laws and regulations, ETL may be the better option since it allows you to audit and remove sensitive information before placing it into the data warehouse. ELT, on the other hand, requires you to load the data first, which could increase the risk of sensitive information being exposed.
Here are some examples:
- A financial institution that needs to comply with data protection laws prefers ETL since it allows them to audit and remove sensitive information before placing it into the data warehouse.
- A marketing agency that deals with large amounts of data from various sources prefers ELT since it allows them to load all data into the warehouse and transform only the data they need.
If you would like to explore the topic of ETL and ELT further, we encourage you to check out other relevant sections of the ETL Learning Center.
The ‘What is an ETL pipeline’ page provides a detailed explanation of what an ETL pipeline is and how it is used in data management. This page provides a great starting point for those who are new to the topic.
The ‘Three stages of the ETL process’ page delves deeper into the specific stages that make up the ETL pipeline, including extraction, transformation, and loading. Understanding these stages can help you to better understand the overall process of ETL.
Lastly, the ‘ETL: Everything you need to know’ guide covers everything related to ETL, from its definition and benefits, to its various types and use cases. This page is an excellent resource for those who want to gain a more in-depth understanding of ETL.
We hope you find these resources helpful in your quest to learn more about ETL and data management.