ELT process: Everything you need to know

DataOps teams are always looking for ways to streamline their workflows and improve efficiency. The extract, load, transform (ELT) process is one of many tools that make raw data actionable faster. When you need to move data quickly and regularly from many sources to your cloud data warehouse—and you can handle data transformation at the destination—ELT does the job.

Many data-driven organizations choose the ELT process to move the large amounts of data required for their data analysis and DataOps needs. ELT is a variation of the traditional ETL process.

The key difference between ELT and ETL is that with ELT, the data transformation step takes place after data has been loaded into the target system. When your cloud data warehouse is set up to handle data transformation, ELT tools can move more data faster and simplify your data workflows.

Here’s an overview of what ELT is, how it’s different from traditional ETL, and when it’s best to load data before you transform it.

What is ELT?

ELT (extract, load, transform) is a sequence of activities that move data between many sources and destinations. Unlike the traditional ETL process, in ELT pipelines, the raw data is loaded to a destination first and then transformed inside the cloud data warehouse, data lake, or other database.

This makes ELT fast and flexible—it easily integrates with multiple sources and formats. This added flexibility is one of the ELT process’s main advantages over the classic ETL model.

Here's how ELT works:

1. Extract data from sources (using automation, APIs, etc.)

2. Load data directly to the destination (e.g., data lake) without pre-processing

3. Transform data in a cloud data warehouse. This may involve automated processes at the destination, or you can transform data manually as needed.

Because the load step happens second, data moves from source to destination very fast. That makes ELT processes ideal for anything where the priority is to quickly move a diverse array of datasets from many sources and then combine all the data transformations in your data warehouse.

What are the benefits and drawbacks of using ELT?

One of the main benefits of ELT is the speed of data delivery. Since the data doesn't need to be transformed during the process, it can be quickly extracted from the source and loaded into the desired destination.

Because data isn't transformed prior to delivery, ELT pipelines can be more resource-efficient. On the other hand, the main disadvantage of ELT is that raw data is delivered to the destination—meaning that more expertise is needed to work with it afterward.

You’ll need a data team experienced in working with unstructured data and engineers who can build automation systems to make the most out of ELT. With a solid DataOps team, you’ll get some important benefits from this data process:

• Increased speed of data delivery: Data moves to destinations faster because it doesn't have to be transformed en route.
• Reduced manual effort: An ELT process enables the automation of data extraction and loading processes, eliminating manual labor and allowing for more efficient data movement.
• Increased scalability: ELT's ability to move data from multiple sources to a single repository makes it much easier to scale up the data storage and processing capacity as needed.
• Improved accuracy: Automating data extraction and loading processes can help ensure accuracy and reduce the risk of data corruption or errors.
• Reduced load times and costs: ELT processes are more resource-efficient in some cases because they don't require transforming all data prior to delivery and sometimes eliminate the need for expensive integration projects.
• Flexible data transformation: ELT processes enable more options for the transformation of data into various formats, making it easier to use data in different ways.

If you’ve heard of ELT, chances are you already know about ETL and want to know the difference. Here are the key differences.

What are the key differences between ELT and ETL?

ELT and ETL processes both move data from many sources to a destination of your choice. They both transform that data, too. The main difference between ETL and ELT is the location where data transformation happens. In ELT processes, the data is first loaded into the data warehouse and then transformed.

In ETL processes, the data is sent to a staging area for transformation first and then loaded into the cloud data warehouse. Although this can take longer, it might be necessary for some data pipelines.

ELT loads into cloud data warehouses as soon as it’s extracted. From there, it can be transformed quickly without needing any extra specialized hardware to run the staging area. It is  instantly loaded into the warehouse’s internal storage.

This difference means the ELT approach can take advantage of parallel computing more effectively as transformations need not all be applied serially as they may with an ETL approach. Additionally, with ELT, the time-consuming data extraction step can occur simultaneously with transformations. This way, overall process execution time may be better than an ETL implementation.

You can also achieve substantial cost savings and operational efficiency if your organization switches from a traditional ETL implementation to an ELT methodology. Ultimately, you must decide if ELT tools are a better fit for your data warehousing and data transformation needs.

Here are some reasons you‘ll need to use ETL and others that make ELT a better choice.

When should you use an ETL pipeline?

Data teams often use ETL pipelines when low latency is not a necessity for the end-use case. Loading data from multiple sources into a data warehouse for regular analysis is a typical example, as most data analysis does not require real-time data.

Suppose you're putting together a weekly or monthly report. In that case, you just need data between specific start and end points—not the most recent information logged in the seconds before the query was executed.

Additionally, ETL pipelines are advantageous when ease of use is a priority. Despite being complex to build without the right data orchestration tools, once your DataOps team constructs them, they make working with the delivered data quite simple. That’s because the data has already been cleaned and transformed to the destination data warehouse's specifications.

ETL pipelines are also beneficial in cases involving sensitive data or when there are rules about what data should be stored in the cloud or on-premises. These pipelines allow you to create rigid schemas that dictate precisely what data is stored where and in what format. Any data that is not needed or cannot be stored can be removed prior to delivery through the transformation process. For instance, GDPR and HIPAA can require that certain data be kept in precise locations.

While there are many cases where an ETL process makes sense, sometimes you need data faster—and that’s where ELT excels.

When should you use an ELT pipeline?

ELT pipelines are an ideal solution for scenarios that prioritize speed. For instance, if your product relies on a machine learning (ML) recommendation engine, an ELT pipeline is the best way to ensure customer data is delivered quickly to power consistent, timely conversions.

ELT pipelines are also advantageous when you need flexibility. With ETL pipelines, data integration and cleansing occurs prior to delivery and may result in dropped data points, removed rows that contain incomplete data, or converted data types that may cause data loss (e.g., DECIMAL or FLOAT data to INT in SQL).

ELT pipelines, however, make it easy to move all your data into a data lake and then apply the necessary transformations after the fact, all while preserving the raw data. Additionally, ELT pipelines are often the best choice for applications that involve large amounts of data.

Instead of transforming all of it during the load process, you can pay to transform only the data you need, making it a more cost-effective option.

Whether you choose to work with ETL or ELT tools, you’ll need the right data infrastructure to simplify the transformation of your big data into useful insights.

ELT process tools

Depending on your business intelligence needs, you might need an ELT pipeline tool like Stitch or a whole data orchestration platform like Shipyard. Every DataOps team has different goals and you’ll need a different data stack than others to accomplish them. Our favorite SaaS ELT tools handle everything from pipelines to transformation in your warehouse.

dbt Cloud data transformation

dbt enables data teams to work directly within data warehouses to produce accurate and trusted data sets for reporting, machine learning (ML) modeling, and operational workflows. It’s a developmental framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and easy.

Stitch

Stitch delivers simple, extensible ELT built specifically for data teams. It delivers analysis-ready data into your data science pipeline. With Stitch, extract data from the sources that matter, load it into leading data platforms, and analyze it with effective data analysis tools. From there, your machine learning algorithms take over and find the patterns you need to solve business problems.

Fivetran data pipelines

When you collect data from many sources to feed your data science pipeline, Fivetran helps you securely access and send all data to one location. This tool allows data engineers to effortlessly centralize data so that machine learning algorithms can then cleanse, transform, and model the data.

Shipyard data orchestration

Shipyard integrates with dbt, Snowflake, Fivetran, Stitch, and many more tools to build error-proof ELT data workflows in minutes without relying on DevOps. It allows your data engineers to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds. This makes it easy to build a web of data workflows to feed your data science pipeline with many data sets.

Get started with ELT

One of these ELT tools could move more of your data faster—it just depends on your current data infrastructure. We built Shipyard’s data automation tools and integrations to work with your existing data stack or modernize your legacy systems.

If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business needs.