Or how the evolution of data warehouses and technology is shifting ETL.

Data-driven organizations are often on the lookout for populating their data warehouse from various data sources. ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the usual methods for transferring huge volumes of data from different sources to a single data warehouse to enable Business Intelligence (BI) applications to create reports and present analytical results.

For the past two decades, ETL has been the approach of choice; however, with the workloads shifting increasingly to the cloud, ELT is emerging as the alternative approach for populating data warehouses such as Amazon Redshift, BigQuery or Snowflake and performing analytics.

Let’s examine why is it happening, what it means ETL vs ELT, and what you can anticipate tomorrow.

How ETL and ELT are Different?

What seemingly looks like a harmless swapping of “T” and “L” actually indicates an entirely different approach towards handling raw data and presents entirely new possibilities for gaining data insights.

What is ETL?

ETL (Extract, Transform, Load) is a three-step process in which raw data is first extracted from a data source. In the second step, it is transformed into a clean, structured format that is better suited for operational needs. This transformation process includes pulling relevant data, cleaning and removing duplicates, and accordingly, sorting it in particular categories. The final step is loading the data into a target data warehouse where it is ready to be used by BI applications for reporting and analytics. If you are looking for the Wikipedia description here it is.

What is ELT?

ELT (Extract, Load, Transform), on the other hand, is a comparatively new variant of ETL in which the data, after extraction, is first loaded into the data warehouse in raw and unadulterated form. The transformation as required is pushed to be the last step and is carried out at the target system. While the original data source has little to do except delivering raw data which considerably reduces the load time, the target system (a data warehouse) must be powerful enough to handle transformations.

Is ETL difficult and outdated?

ETL emerged to solve the problem of providing companies with clean and ready-to-analyze data.

ETL is meant for IT and data experts. They along with subject matter experts with knowledge of marketing or sales, for example, will make sure that the raw data is appropriately cleaned and structured before reaching the relevant people.

For a company to get started, it requires at least several months lead time, to have the initial transformation jobs performed. As your company grows or the data sources change, modifications, updates, and maintenance are required.

For a company to get started, it requires at least several months lead time.

On the transformation level, the more data you produce, the higher the transformation time is. Even getting the first batch of clean data could take days and users still will not have current data.

The same goes with changes in requirements of data needed for analysis. If you require different data from the source, then you will have to fix the transformation process and reload your data again.

Is ELT a better approach?

Decision makers need to have access to data and insights to optimize any strategy at any time. ELT is meant for business managers, product, growth or the marketing teams; and those who actually make decisions based on data insights.

While ETL presents them with readable, structured data from the get-go, it takes away the flexibility and freedom from decision-makers to decide what they need. ELT, on the other hand, gives them access to raw data where they can pick and choose what they really need, and transformations are carried out accordingly.

ETL vs ELT: The Pros and Cons

Since ELT is all about loading before any transformations, the load time is significantly less as compared to ETL which uses a staging table to make transformations before finally loading the data.

As the data size grows, the transformation, and consequently the load time, increases in ETL approach while ELT is independent of the data size.

With ELT, all of the data is always available. It’s there for everyone to view- from developers to the C-suite. It adds flexibility and gives freedom to the decision makers to keep what they need and discard what they don’t. Insights cannot wait days for changes to be implemented. Business, product and growth teams today need flexibility and current data to drive your business forward.

However, perhaps, the most significant benefit of using ELT is that it is designed to leverage the scalability that comes with cloud infrastructures and supports volumes of data.
The low maintenance, scalability, and availability of the ELT approach make it a perfect choice for all business sizes including SMEs and ‘born-in-the-cloud’ companies.

On the downside, ELT needs a powerful target system that can handle data transformations. However, the increasing pervasiveness of cloud infrastructures, and fast data warehouses that can carry out the required transformations, effectively overcomes this issue. In fact, the ELT naturally fits in with the cloud infrastructures and databases like Amazon Redshift, Google BigQuery, and Snowflake.

Essentially, this makes ELT perfect for the modern, hybrid and multi-cloud environments as companies gradually shift from legacy systems and adopt the cloud.

However, before deciding on an approach, it is advisable first to understand the needs of your organization and make use of the right tools.

Finding the Right Partner is the Key

Populating a data warehouse and analyzing it to get useful insights is a significant concern for most of the data-driven organizations today. Forward-planning companies are breaking data silos, and technologies like Amazon Redshift, Google BigQuery, Snowflake, and Microsoft SQL Server can help organizations in running a data warehouse seamlessly.

Populating such data warehouses with data from all customer touchpoints requires a platform, that can fill the data warehouse with analytics-ready data, with proficiency and ease, regardless of the service provider. Here is where Blendo comes in.

Fully manage your ELT with Blendo to collect and sync data from multiple data sources into any data warehouse with a 90% reduction in the time it takes from extracting raw data to gaining data-driven insights.

Connect your data sources with as little as a few clicks and load it to a single destination which makes managing the information flow from your data sources a breeze. Blendo effectively reduces the time required for extracting and loading the data regardless of data size.

What reaches the data warehouse is structured analytics-ready data from multiple sources such as Salesforce, Pardot, Zendesk, and more.

Most importantly, everyone within your organization is on the same page with a consolidated view of the data.

As a result, all your organization’s departments, including finance, sales, marketing, and support, can access data, freely decide what they need, and drive actionable insights.

Most importantly, everyone within your organization is on the same page with a consolidated view of the data.

Blendo is a modern ETL platform (ELT) designed to offer self-serve analytics-ready data in modern data warehouses. Don’t hesitate to contact us and try Blendo for free.