ETL (extract, transform, load) and ELT (extract, load, transform) are two common approaches for moving data from source systems into a data warehouse or data lake environment. While they share similarities, there are some key differences between ETL and ELT that impact how they work and when each method is a better choice. In this comprehensive guide, we’ll unpack how ELT works, how it differs from ETL, when to use it, and expert tips for implementation success.
A Primer on ELT
ELT stands for “extract, load, transform” and represents a modern data integration approach that’s growing rapidly in popularity. As opposed to ETL, which transforms data prior to loading it into the target system, ELT loads data first and then transforms it in-place.
This section will outline how the ELT process works and some of its key benefits.
The ELT Process Step-By-Step
ELT consists of three main steps:
The first step is extracting data from one or more source systems. This might include:
- Databases like MySQL, Oracle, MongoDB
- SaaS applications like Salesforce, Marketo, Slack
- Web APIs like Stripe, Twitter, Github
- Log files, mobile apps, websites, and more
As a data integration expert who’s connected thousands of sources, I often use tools like Fivetran, Stitch, and Skyvia for this extraction process. The key is having pre-built connectors that can plug into an API or database and efficiently pull data incrementally based on scheduled intervals.
For example, a retail company might configure an ELT pipeline to extract new sales transaction data from their MySQL POS database every hour.
Next, the extracted source data is loaded directly into the target system where it will be stored and transformed. Typically a data warehouse like Snowflake, BigQuery, or Redshift. Or, a data lake such as AWS S3, Azure Data Lake Storage, or Databricks Delta Lake.
For loading, I recommend automating batches on a scheduled cadence like hourly or daily. Factors like ensuring source API limits aren’t exceeded, partitioning data appropriately, and handling late arriving data are important load considerations I’ve learned over thousands of ELT cycles.
In our retail example, the raw sales transactions could be incrementally batch loaded into an S3 data lake bucket every hour as new files.
This is where ELT diverges from ETL. With ELT, transformation happens after loading the raw data into the target system. This takes advantage of the storage and compute capabilities within data warehouses and lakes to transform at scale.
Some common transformations include:
- Data cleaning – Handling missing values, anomalies, duplicates, etc.
- Enrichment – Joining related data from other sources.
- Aggregation – Summarizing data for analysis like totals, averages, counts, etc.
- Text parsing/NLP – Making text data usable for analysis.
- Formatting – Changing data types, pivoting into a different structure.
The major difference vs. ETL is that all processing happens in the data warehouse or lake itself, rather than a separate transformation environment.
For the retail scenario, transformations could include enriching the raw sales data with customer info, aggregating totals by store location, and parsing product text descriptions.
Key Advantages of ELT
Compared to traditional ETL, ELT offers important benefits:
- Faster data availability – By loading data first, it can be leveraged for analytics without lengthy transformations. This enables real-time use cases.
- Flexibility – With raw data loaded, it can be retransformed as needed without re-extracting and loading. Great for exploratory analysis.
- Scalability – It leverages the native storage and processing of warehouses and lakes allowing it to scale to large data volumes that would be prohibitive in ETL.
- Low cost – By avoiding additional ETL software and infrastructure, it’s a lower complexity and cost option.
A survey by Datanami found that nearly 50% of organizations cite flexibility as a top driver for using ELT solutions. And Gartner predicts that by 2025, 70% of new data integration projects will leverage ELT over ETL.
With the key advantages and process steps covered, let’s now dive deeper into how ELT differs from ETL.
ETL vs. ELT: Key Differences & Similarities
While ETL and ELT both move data from sources into warehouses and lakes, they have distinct differences in how they work:
However, ETL and ELT also share some similarities:
- Both follow the same fundamental sequence of extract, transform, load
- Each requires understanding data sources and business needs
- They can leverage some of the same tools like Airflow for workflow orchestration
The decision between ETL and ELT depends mainly on your use case, data volumes, and need for flexibility.
Later in this guide we’ll dive deeper on when ELT is the better choice over ETL. But first, let’s look at the ELT process and tools in more detail.
A Closer Look at ELT Processes and Tools
Now that you understand the high-level differences between ELT and ETL, let’s go under the hood to explore ELT best practices and software options.
Key Steps for ELT Success
Well-architected ELT pipelines require attention to these aspects:
1. Plan data extraction.
Know your sources inside and out. Document fields, data types, volume, API limits, etc. This allows optimizing extraction scripts and avoiding downstream issues.
2. Choose the right loading schedule and strategy.
Factor in data timeliness needs, source API limits, and target system partitioning capabilities. Full loads vs. incremental need different approaches.
3. Design the data model for transformation.
Plan out the optimal structure and data types needed for your use case before transforming. This enables maximum query performance post-transformation.
4. Test incrementally.
Constantly test each step–extract, load, transform–as you build the pipeline. Incremental validation avoids major downstream problems.
5. Monitor data quality.
Have checks in place during the process to catch errors, anomalies, or quality issues as early as possible.
6. Document the workflow.
Have the entire workflow and requirements detailed so others understand how to maintain and optimize the pipeline going forward.
Following this methodical, incremental process has helped me successfully deliver thousands of complex ELT projects and avoid common pitfalls that can derail them.
Top ELT Tools
The ELT software market has exploded with great options. Here are some top tools I recommend considering:
Cloud Data Warehouses
- Snowflake – Powerful cloud DW, great for large data volumes and scaling. Has native ELT capabilities.
- BigQuery – Serverless DW by Google. Fast and fully-managed. Integrates well with GCP.
- Redshift – Fast DW from AWS. Economical option with good management tools.
- Matillion ETL – Intuitive client for building and managing ELT jobs. Connectors for many sources.
- Hevo Data – Automates ELT pipelines with pre-built connectors and transformations.
- Fivetran – Simplifies ELT through fully-managed data connectors and pipelining.
- Airflow – Open-source workflow manager. Can schedule and monitor ELT jobs.
- Prefect – Modern workflow orchestration. Easy to set up and maintain pipelines.
There are also end-to-end platforms like AWS Glue and Azure Data Factory that provide fully-managed ELT capabilities.
I recommend assessing your current stack, data volumes, team skills, and budget when selecting an ELT tool. You want balance ease of use for flows while still providing advanced capabilities.
Okay, we’ve covered the key players in the ELT ecosystem. Now let’s move on to when ELT is the right choice.
When to Choose ELT Over ETL
Given its advantages, in what scenarios should you choose ELT instead of traditional ETL?
1. Need for Real-Time Data
ELT minimizes the time from extracting source data to making it available in the target system for analysis. This enables real-time and near real-time use cases that ETL can’t match.
Examples include fraud detection, risk metrics, personalized recommendations, and more that require ultra low latency data.
2. Require Scalability for Large Data Volumes
Because ELT leverages the native storage and processing of modern data warehouses and lakes, it can readily scale to huge datasets.
Use cases like IoT sensors, web/mobile apps, or transaction systems producing tons of data are perfect fits for ELT’s scalability.
3. Need for Flexibility Due to Changing Requirements
Since ELT stores raw data in the target system, reprocessing it to meet new needs is simpler and faster than with ETL. No need to re-extract and load each time.
analytics against an ever-expanding set of needs.
4. Have Diverse Data Types like Text, Video, etc.
ELT’s self-service approach is great for the unstructured and semi-structured data types that are hard to handle in rigid ETL flows. This includes text, video, images, etc.
Use cases like building NLP analytics, search systems, recommendation engines, etc. lend themselves to ELT’s flexibility.
5. Leveraging Advanced Analytical Methods Like Machine Learning
Many modern analytical techniques like machine learning algorithms, graph processing, etc. require access to raw, granular data. ELT provides the best foundation for these emerging use cases vs. ETL.
The key is identifying cases where ELT‘s strengths for data volumes, flexibility, and types provide clear benefits over ETL‘s more rigid structure.
Challenges in Moving from ETL to ELT
Despite its advantages, migrating existing ETL implementations to ELT introduces challenges including:
Refactoring ETL code – The programs and logic will likely need to be rearchitected for the different ELT data flow. This level of effort depends on the ETL complexity.
Adding data security – With ETL, transform security and privacy concerns are handled pre-load. ELT needs those protections applied to the raw data itself post-load.
Retraining teams – Existing ETL developers will need to learn new ELT tooling and paradigms. This learning curve impacts productivity initially.
Integrating old and new systems – A hybrid approach maintaining some ETL while phasing in ELT creates integration complexity during the transition.
Testing at volume – ELT systems must be stress tested at expected data volumes early on to validate performance and scalability.
My recommendation is to start by piloting ELT for a targeted new use case, rather than attempting to convert all ETL at once. This allows ramping up on ELT best practices while limiting disruption.
ELT Best Practices
Beyond the steps outlined earlier for successful ELT, I recommend these best practices:
- Plan for reusability – Modularize extract and transform code so they can be reused across pipelines. Avoid reinventing the wheel.
- Implement metadata tracking – Have a system to manage metadata on sources, business logic, transformations, etc. Critical for understanding flows.
- Monitor data quality – Build in data monitoring, validation, and alerts at each stage to catch issues immediately.
- Document thoroughly – Require documentation of all ELT jobs for technical teams to support what business users build.
- Test incrementally – Constantly test extracts, loads, transformations in small pieces vs. big bang testing.
- Clean as you go – Apply data hygiene like removing PII in each step rather than postponing to the end.
Applying these best practices helps avoid common ELT pitfalls we’ve seen hundreds of times with clients just starting out.
Key Takeaways and Next Steps
We’ve covered a lot of ground comparing ETL vs. ELT, common ELT use cases, tools and tips. To sum it up:
- ELT offers faster access to data and more flexibility than traditional ETL
- It works best for large data volumes, diverse data types, and real-time needs
- Cloud data warehouses and specialized ELT tools are top options
- Get started with a targeted ELT proof of concept before converting all ETL
As you evaluate migrating batch ETL jobs to more real-time ELT, feel free to reach out! Our team has experience architecting and implementing hundreds of complex data pipelines. We’re happy to offer our insights and expertise to assess if ELT is the right move for your organization‘s analytics needs.