What Is ETL? Understand 'Extract, Transform, Load' in Depth

ETL: A Brief History 

Believe it or not, ETL stretches back to the 1970s, during which it rose to prominence following the emergence of enterprise databases. At that time, early ETL systems mainly extracted data trickling in from a variety of different sources. 

Over the next few decades, the technology became increasingly popular. In large part, the rise of data warehousing is to thank for that growth. Fast-forward to the mid-1990s, and many large organizations were using on-site ETL tools to process and store large volumes of diverse transactions. 

Today, ETL remains a critical component for data management. This is because companies are collecting more data than ever before from an ever-growing list of sources. Using modern ETL tools, it’s easier to refine, process, and integrate data across different teams and departments.

A robust ETL strategy can provide a direct data pipeline for engineering, marketing, sales, design, executive decision-makers, and research and development teams. It’s a win-win for departments across the organization.

What Does the ETL Process Look Like?

Here’s an overview of what happens along each stage of the ETL process:

1. Extract 

Data extraction involves pulling data from source locations and moving it into a staging environment, which you can think of as temporary storage.

A large business may have thousands of different data inputs. Examples may include applications, IoT devices, email systems, CRMs, and SQL and NoSQL servers. Data can be both structured and unstructured, and data volumes can vary significantly from source to source.

2. Transform 

After the data is collected, it then has to go through cleansing, organization, authentication, and validation processes.

The transformation process modifies and deletes incorrect and irrelevant data. It ensures information is accurate and compliant before moving it forward for analysis and visualization.

In addition to cleansing, data also goes through a formatting process during ETL. This involves putting data into tables and aligning it with the target data warehouse schema. 

3. Load

Following transformation, data goes through a consolidation process and winds up in a data warehouse. 

When loading data into a target warehouse, it’s important to proceed with caution in order to avoid overloading the host system and impacting other workflows. For the best results, this process should ideally occur during off-peak hours.

What Are the Benefits of ETL?

There are many reasons why companies still use ETL solutions after all these years. Here’s a look at some of the top benefits the technology offers:

Data Governance 

Companies today face rising pressure from government agencies and consumers about data privacy and security. Data governance is therefore critical when it comes to avoiding fines and penalties and maintaining consumer trust.

ETL helps with data governance by ensuring information is safe and in compliance before it goes into production.

Single Source of Truth

It’s critical to have an internal vetting system for data to prevent false information from leaking to the public and to ensure teams are working off the same data. When it boils down to it, sales, marketing, and executive teams need to know that the data they’re using is accurate and up to date. 

ETL creates a single source of truth (SSOT) — i.e., a system where everyone has access to the same information. This, in turn, reduces risk since everyone knows they’re working with the most current data. 

Data Context

A data point doesn’t mean much without context. Companies need to be able to trace data points and gain a deeper historical context to derive meaning from them. 

ETL solutions help businesses track where data originates and view modifications throughout the lifecycle. This ultimately provides greater insight for stakeholders — making it easier to rely on data for business intelligence initiatives. 

ETL vs. ELT

Many companies are now switching their process around and loading data before transforming it. This variation is called exchange, load, transfer (ELT). 

Companies typically use ELT to make data processing faster and improve flexibility. With ELT, you don’t have to wait as long to load data, meaning you can get up and running in less time. This approach is also helpful for businesses that have fluctuating data sources since ELT reduces formatting to save time and effort.

While ELT improves speed and flexibility, it’s also a bit riskier, causing businesses to have a harder time maintaining security and compliance.

Going back to the transportation example, if ETL is like an airport, then ELT is like an open train platform where anyone can jump on board at any time. 

What’s more, ELT can increase the price of data production and management. As such, companies with tight budgets may want to consider ETL to filter out unnecessary data and reduce storage costs.

At the end of the day, there’s no right or wrong answer when deciding between ETL and ELT. Ultimately, every company has different needs. Some businesses prefer the traditional approach to ETL, while others need to move faster and with greater flexibility.

What Are the Top Challenges for ETL?

Companies often run into unexpected performance issues that can derail their ETL systems and make it harder to process data. 

Here’s a breakdown of some of the main issues that companies face with ETL management:

Resource Control

It’s important to realize that running ETL at a high volume can require significant computational resources. Companies often need to choose between waiting for data or continuously churning out ETL at a high volume. 

Accessibility 

Businesses often lack the tools for efficiently moving data from warehouse environments to stakeholders. This makes it hard to gain relevant insights, which is particularly problematic for engineers who need access to fresh data when rolling out new features. 

Continuous delivery requires a steady flow of information. Without these insights, it’s like driving a car without headlights. You might make it to your destination, but it won’t be pretty. And you might run into problems along the way.

High Data Loads

Rising data volumes create many challenges for ETL. For example, companies often lack adequate memory and CPU, which leads to bottlenecks. 

Further, despite all the extra processing with ETL, companies still often wind up processing unnecessary data. This increases production and storage costs and makes it harder to scale. 

Ongoing Maintenance 

ETL isn’t something that you can set and forget. You have to continuously maintain your processes to keep up to date with changing inputs.

Of course, this can be very taxing for IT departments — especially those with staffing shortages.

How Plutora Enables and Extends ETL

In summation, ETL remains an important strategy for any organization that’s serious about using and integrating data. With a modern ETL solution in place, you can gain deep visibility and control over data following ingestion.

Plutora provides a one-stop-shop for data integration. The automated Plutora platform pulls data from across the entire software delivery value stream and funnels it directly into a data warehouse for processing and reporting. Additionally, Plutora eliminates manual data crunching, giving you actionable analytics with push-button ease.

With Plutora, your team will benefit from an SSOT for software delivery and smart dashboards that deliver contextual information to key stakeholders when they need it. These dashboards can seamlessly integrate into your existing workflows, increasing response time. 

When you add it all up, Plutora lowers risk, saves money, and helps turn raw information into actionable insights. Does it get better than that? For more information on Plutora, take a look at our approach to business intelligence.