What Is ETL?

What is ETL? Extract, Transform, Load. And if you’re an o rganization looking to create a data warehouse you heavily rely on the ETL process.

If you’re like most companies, you have an inordinate amount of data siloed across the organization. You know it’s a problem, and you’re looking to solve it as quickly as possible, so you’ve prioritized shattering those silos, bringing all your data to one place, and creating a single source of truth.

To do that, you’ll need to create a cloud data warehouse that centralizes all of your organization’s data, making it accessible for everyone.

Organizations looking to create a data warehouse rely on the ETL process to populate it, which means they typically end up following these three steps:

1. Extracting  data from multiple sources, like CRM systems, SaaS apps, data storage platforms, and legacy systems
2. Transforming  it into the appropriate format by cleaning it, standardizing it, and organizing it while deleting duplicate data
3. Loading  it into the data warehouse either in one fell swoop or bit by bit

The ETL process has been around for ages. It emerged as early as the ’70s. It gained popularity with the advent of data warehouses and the promises of having all your data in one place to achieve the so-called 360-degree view of your business and enable reliable data-driven business insights.

While the time-consuming and unwieldy process might have worked well enough in the 1990s and 2000s, it leaves much to be desired in the age of big data.

The downsides of traditional ETL processes

The average enterprise manages  several hundreds of terabytes of data , and the data volumes are expected to more than double every year with no end in sight. As a result, the traditional ETL process has proven itself too clunky and cumbersome to keep pace with today’s fast-paced, data-fueled agile landscape. 

Not only does ETL struggle with the volume, variety, and velocity of data, the process is also exhaustively time-consuming, labor-intensive with projects often stretching out for weeks or even months. What’s more, engineers need to complete ETL tasks on their time because of the process’s nature. When a business user wants to find an answer  right now , they’ll have to get in line with everyone else and all their requests, making it that much harder to operate with agility. 

Also, traditional ETL processes require analysts to work within rigid schemas, which prevents them from exploring the data on a profound level. And once a project is done, it isn’t easy to reuse the logic created because it’s static and inflexible.

Modern ETL tools have emerged in recent years to streamline the ETL process considerably while building reusable analytics models and empowering all business users to leverage all of the organization’s data at their own pace, without the help of IT.

The new age of ETL solutions

As companies increasingly look to create a single source of truth and get a 360-degree view of their business. They are increasingly turning to a new breed of ETL solutions that bring the speed, agility, and exploration capabilities needed to transform into truly data-driven, cloud-native organizations. 

With the right tools in place, it’s possible to give your team the ability to respond to quickly changing circumstances with agility, with the peace of mind that comes with knowing the decisions they make are rooted in data. At the same time, it enables them to dig deeper into data and analyze it from every which way to discover new insights during periods of exploration and tinkering.

Using  Datameer Spectrum , for example, analysts can model data, explore it, model it some more, study it again, and repeat them ad infinitum. This way, they can rapidly iterate on their work, tweaking it continuously until they find what they’re looking for.

It’s a proven way to accelerate time to insight considerably. Datameer customers report a  98 percent reduction in analytics cycles . All of a sudden, what used to take weeks or even months now takes hours. Up next, we’ll look at why that is.

How new ETL tools empower users and accelerate analytics

In the past, typical business users needed to engage IT when they wanted to build data pipelines. Once they submitted their requests, IT would get around to it when they could, and then they’d have to do all the prep and modeling work before analytics could commence. By the time everything was ready, the data would be old. And because the request was specific, analysts couldn’t explore outside the bounds of the models they were given.

Modern ETL tools overcome these challenges and empower teams by removing IT from the equation altogether, which democratizes analytics across the organization. In turn, this enables line of business workers to transform, blend, and enrich complex data sets at their leisure and uncover real-time insights faster.

Further, the new breed of ETL tools is versatile by design. Whereas in the past, data needed to be transformed using fixed schemas, modern tools automatically create schemas based on analyst input, further speeding up analytics workflows. 

Beyond that, leading ETL solutions also use automation to operationalize data pipelines in the cloud, bringing powerful analytics capabilities to everyone. 

To learn more about how modern ETL tools like  Datameer, Sign up for your free trial today!