ELT

ELT is essentially a more modern version of ETL. Where ETL is extract, transform, and load, ELT is extract, load, and transform. It’s a faster, cheaper, and more flexibly way to move and change data than ETL.

Key highlights:

• ELT, or Extract, Load, Transform, is a modern data integration process ideal for handling the large and diverse datasets common in today’s businesses.
• Unlike its predecessor ETL, ELT prioritizes speed by loading raw data into the target system before transformation.
• This approach leverages the power of cloud-based data warehouses and data lakes, allowing for efficient transformation and analysis within the storage environment.
• ELT offers significant benefits, including scalability, cost savings, and faster access to insights, making it increasingly popular across various industries.
• From marketing to finance to healthcare, ELT’s ability to handle real-time and big data analytics is changing how organizations utilize their data for strategic decision-making.

Why this matters:

Today we have a lot of data, and data is very valuable to understanding what’s happening, optimizing processes, and training AI. Because of this, good data management is very important. This is why the ELT process is becoming popular. The ELT process helps move raw data from different places to a main storage area, like a data warehouse or a data lake. Once the data is there, it can be changed and studied to find helpful business insights.

Think of a data pipeline like a smooth assembly line, moving data from where it is created to where we want to use it. ELT is essentially this pipeline.

It makes sure the raw data travels easily.

In the past and still somewhat today, data scientists transformed data to meet local needs before loading it into databases or data lakes. But with ELT, instead of changing the data before loading it into the target system, we load the raw data straight into the target system.  This method works very well for large datasets, because the transformation of data happens in these strong data systems, which are made for efficient processing.

You can think of it like moving all your ingredients into a nice kitchen (the data warehouse) before you start cooking (transforming the data). This makes everything easier and often quicker.

It’s also more flexible, because you don’t always know now what you’ll use data for in the future, and if you transform it too early, you might lose the precise components you’ll need later.

With ELT, cloud-based solutions, especially data lakes, are transforming data management. These large systems can store an almost endless amount of data. They include many data types, such as structured, semi-structured, and unstructured data.

This ability makes data lakes very flexible. They offer a complete view of all the data an organization holds.

Here are the main parts of the ELT process:

1. Data Extraction
   First, we gather data from the source system. This can come from many places, like operational databases, APIs, CRM systems, cloud applications, and social media. We use a data extraction tool to make sure this process is efficient and works well.
2. Data Loading
   After extraction, we move the data to the target data store. The data is usually in its raw form now. The target can be a data warehouse, which is good for analyzing structured data, or a data lake, which can handle different types of data, including unstructured and semi-structured data.
3. Data Transformation
   In this last step, we change the loaded data into a format that is easy to analyze. This process includes cleaning, enhancing, and organizing the data based on what the business needs. The transformation takes place in the data store, using the capabilities of modern data platforms.

Often a company or service or tool will just do the first 2 steps. Data scientists or analysts might complete the third step at a later point, when necessary.

Extraction

Extraction is the first step in the ELT process: gathering raw data from different data sources. This data can be organized, like records in a database, or unorganized, like posts from social media or logs from sensors. How well data is extracted depends on using the right tools and methods for each data source. For example, to extract data from a relational database, you might use SQL queries. If you need data from cloud applications, you could use APIs.

Important: a good extraction process makes sure that data is collected accurately, completely, and consistently. This sets things up for easy loading and further transformation in the target data store.

Loading

The data loading process is where the ELT method is different from the old ETL method. In ELT, you directly move the data you have gathered into the target system, which is usually a data warehouse or data lake, without transforming it first. Sometimes, a staging area is used to hold the data temporarily before it goes to its final place in the target system. This area helps manage large amounts of data and helps to keep things running smoothly without causing interruptions. By loading raw data straight into the target system, you can use the strong processing abilities of today’s data platforms. This makes it easier and faster to transform the data when you need to.

Transform

The last step in the ELT process is data transformation. This step happens in the chosen target system, but it does not need to happen instantly. Here, raw data is shaped, organized, and improved to create useful insights. This can include cleaning data to fix errors, changing data types, and mixing data from different sources for a complete view. Transforming data in the target system is beneficial because it uses powerful local processing capabilities and it saves raw data with all its complexities and details, which might come in handy later.

Modern data platforms, especially those based in the cloud, provide scalability and efficiency. This makes the transformation process faster and more flexible than older methods. The transformed data is then important for gaining clear business intelligence. It helps teams make better decisions and leads to improved business results.

While ELT has emerged as a forerunner in data integration, it’s crucial to understand its strengths and limitations compared to ETL:

ELT shines when dealing with large, diverse datasets, offering scalability and cost-effectiveness. However, it requires specialized tools and expertise to manage transformation within the target system.

On the other hand, ETL, with its mature tools and established processes, might be suitable for smaller, well-structured datasets but can struggle with big data’s demands. The choice between ELT and ETL ultimately hinges on an organization’s specific data needs, infrastructure, and long-term goals.

The benefits of using ELT goes well beyond just data integration. It helps businesses access information faster. That helps them make smarter decisions — and quicker decisions — plus boosts how they work to stay ahead of competitors.

By improving workflows that depend on data and supporting real-time analytics, the benefits of using ELT are changing how companies use data for success in today’s world focused on information.

Singular offers ETL and ELT tools. You can choose which to use depending on your exact need or use case.

Our ETL is incomparably customized to advertisers’ needs, with thousands of integrations into ad network and marketing platform databases and API streams, significantly helping you stay up to date with everything you’re doing for growth. Our ELT tools are general purpose and significantly cheaper than anything else on the market.

You’ll be hearing more about this in the future.