What’s the Difference Between ETL and ELT?
What is ETL?
Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).
What is ELT?
Extract, load, and transform (ELT) is an extension of extract, transform, and load (ETL) that reverses the order of operations. You can load data directly into the target system before processing it. The intermediate staging area is not required because the target data warehouse has data mapping capabilities within it. ELT has become more popular with the adoption of cloud infrastructure, which gives target databases the processing power they need for transformations.
ETL process
ETL has three steps:
- You extract raw data from various sources
- You use a secondary processing server to transform that data
- You load that data into a target database
The transformation stage ensures compliance with the target
database’s structural requirements. You only move the data once it is transformed and ready.
ELT process
These are the three steps of ELT:
- You extract raw data from various sources
- You load it in its natural state into a data warehouse or data lake
- You transform it as needed while in the target system
With ELT, all data cleansing, transformation, and enrichment occur within the data warehouse. You can interact with and transform the raw data as many times as needed.
Differences
Extract, load, and transform (ELT) has improved extract, transform, and load (ETL) in several ways.
Transform and load location
Transformation and load occur in different locations and use distinct processes. The ETL process transforms data on a secondary processing server.
In contrast, the ELT process loads raw data directly into the target data warehouse. Once there, you can transform the data whenever you need it.
Data compatibility
ETL is best suited for structured data that you can represent in tables with rows and columns. It transforms one set of structured data into another structured format and then loads it.
In contrast, ELT handles all types of data, including unstructured data like images or documents that you can’t store in tabular format. With ELT, the process loads the various data formats into the target data warehouse. From there, you can transform it further into the format you require.
Speed
ELT is faster than ETL. ETL has an additional step before it loads data into the target that is difficult to scale and slows the system down as data size increases.
In contrast, ELT loads data directly into the destination system and transforms it in parallel. It uses the processing power and parallelization that cloud data warehouses offer to deliver real-time or near real-time data transformation for analytics.
Costs
The ETL process requires analytics involvement from the start. It needs analysts to plan ahead on the reports they want to generate and define data structures and formatting. The time required for setup increases, which adds to costs. Additional server infrastructure for transformations may also cost more.
ELT has fewer systems than ETL, as all transformations occur within the target data warehouse. With fewer systems, there is less to maintain, leading to a simpler data stack and lower setup costs.
Security
When you work with personal data, you must comply with data privacy regulations. Companies must protect personally identifiable information (PII) from unauthorized access.
In ETL, developers have to build custom solutions, like masking PII to monitor and protect data.
On the other hand, ELT solutions provide many security features—like granular access control and multifactor authentication—directly within the data warehouse. You can invest more time in analytics and less time meeting data regulation requirements.
When to use ETL vs. ELT
Extract, load, and transform (ELT) is the standard choice for modern analytics. However, you might consider extract, transform, and load (ETL) in the following scenarios.
Legacy databases
It is sometimes more beneficial to use ETL to integrate with legacy databases or third-party data sources with predetermined data formats. You only have to transform and load it once into your system. Once transformed, you can use it more efficiently for all future analytics.
Experimentation
In large organizations, data engineers conduct experiments—things like discovering hidden data sources for analytics and trying out new ideas to answer business queries. ETL is useful in data experiments to understand the database and its usefulness in a particular scenario.
Complex analytics
ETL and ELT may both be used together for complex analytics that use multiple data formats from varied sources. Data scientists may set up ETL pipelines from some of the sources and use ELT with the rest. This improves analytics efficiency and increases application performance in some cases.
IoT applications
Internet of Things (IoT) applications that use sensor data streams often benefit from ETL over ELT. For examples, here are some common use cases for ETL at the edge:
- You want to receive data from different protocols and convert it into standard data formats for use in cloud workloads
- You want to filter high-frequency data, perform averaging functions on large datasets, then load averaged or filtered values at a reduced rate
- You want to calculate values from disparate data sources on the local device, and send filtered values to the cloud backend
- You want to cleanse, deduplicate, or fill missing time series data elements
Tools used in both approaches
- AWS Glue is a serverless data integration service for event-driven ETL and no-code ETL jobs.
- Fivetran: An automated, cloud-based platform recognized for ELT, which also supports ETL and integrates with dbt.
- Airbyte: An open-source, flexible platform providing pre-built connectors for both approaches.
- Azure Data Factory - Cloud-based, serverless services designed for managing, moving, and transforming data.