Understanding the ETL process: the complete guide

On average, a company uses 112 SaaS applications (BetterCloud). A figure that exceeds 150 for organisations with more than 5,000 employees, a 40% increase since 2020.

Faced with this explosion of tools, data is scattered across numerous sources: CRM, ERP, e-commerce platforms, analytics, customer support, and more. Centralising, cleaning, and leveraging this data has become a real challenge for businesses.

This is precisely the role of the ETL process: turning raw data streams into a powerful lever for analysis and performance.

👉 Discover how the ETL process works and why it remains a cornerstone of any modern data strategy. Learn about the main tools and use cases 🔍

It plays a crucial role in data management and analytics. Without ETL, data often remains raw, scattered and difficult to exploit.

By gathering and organising information, ETL facilitates reporting, business intelligence, and the building of a unified customer view.

The first step involves retrieving data from different sources. These may be relational databases (MySQL, Oracle), CRM systems, e-commerce platforms, APIs, or even simple CSV files.

Extracted data is often inconsistent, containing different formats, duplicates or missing values.

The transformation phase involves cleaning, enriching, reformatting and aggregating the data so it is coherent and ready for business use.

The final step is to load the transformed data into a data warehouse, data lake, or any other target system. The data can then be used for analysis, marketing personalisation or reporting.

Initial loads are comprehensive, followed by periodic (or incremental) updates.

💡 In some modern architectures, the extraction, transformation and loading stages do not occur sequentially. They can run in parallel to save time.

Originally, data was stored locally on internal company servers: invoices, commercial info, stock, HR data. Early data architectures were based on a few internal relational databases.

Today, data flows in from everywhere: SQL and NoSQL databases, SaaS tools, connected objects (IoT), activity logs, CRMs, social media...

This multiplication of sources has created challenges: different formats, varied protocols, inconsistent quality, and the risk of duplication or errors.

Without a clear methodology to organise this growing volume of data, its exploitation becomes complex, costly and unreliable.

To generate value, data must first be centralised, cleaned, and prepared. This is the core mission of ETL: consolidating disparate information to make it usable.

This process is essential for feeding Business Intelligence tools, performing reliable analysis, or launching data science projects. It forms the foundation of a data-driven strategy by ensuring access to accurate and up-to-date insights.

Delivering a personalised customer experience is now a business performance driver. With purchase journeys more fragmented than ever, marketing and sales teams need a single customer view.

ETL consolidates data from your CRM, shops, social media, website and customer support into one place. By cross-referencing this information, businesses can better understand journeys, detect opportunities, and tailor their actions.

It’s a crucial step to building a marketing activation strategy based on data rather than intuition.

As mentioned earlier, the ETL process follows three clearly defined steps: data extraction, transformation on an intermediate server, and then loading into the target warehouse.

With ELT, the order of transformation and loading steps is reversed. Data is first extracted, then directly loaded into the target cloud warehouse (such as Redshift, BigQuery or Snowflake).

These platforms have built-in transformation capabilities.

This highly flexible approach allows for the processing of large volumes of both structured and unstructured data. It takes advantage of the native computing power of cloud infrastructures to accelerate processing.

As the name suggests, Reverse ETL operates in the opposite direction to ETL.

Rather than centralising your data in a warehouse, it focuses on delivering that data to your business applications: CRM, marketing automation tools, customer support platforms, advertising channels, and more.

This is known as data activation. In practical terms, Reverse ETL allows you to push customer segments, loyalty scores, or purchasing behaviours directly from your data warehouse.

It serves operational teams such as sales, marketing, support, and product.

💡 This approach is central to DinMo’s mission: making it easier to leverage customer data in order to personalise the experience and drive performance.

There is a wide variety of ETL solutions, from open source software to turnkey SaaS solutions. Here is a breakdown of the most common categories.

Choosing an ETL tool depends primarily on your business and technical needs. Several criteria should be considered:

💡 Before selecting a tool, start by clearly mapping your operational and technical requirements.

The ETL process is essential for unlocking the value of your data, but it also comes with technical and organisational challenges. Here’s how to get the most out of it.

The transformation stage of ETL is where data is cleaned, harmonised, and enriched prior to use. This is essential for generating reliable KPIs and effective business management.

💡 Imagine you’re a major retail player, with both online and in-store activity. Your dashboards rely on data from your e-commerce platform, physical shops, and CRM system.

An ETL pipeline helps remove duplicates, unify date formats, and merge customer data. Power BI dashboards now deliver more accurate sales forecasts and help optimise stock management and loyalty campaigns.

Bringing together data from your CRM, sales, customer support or analytics tools is crucial to breaking down data silos.

A unified base enables a clearer understanding of customer journeys and aligns marketing, sales and product teams around a single 360° customer view.

ETL also plays a crucial role in preparing data for more targeted, effective campaigns.

Advanced segmentation, behavioural scoring, profile enrichment. All these steps rely on centralised, trustworthy data stored in a single source of truth.

💡 With Reverse ETL, your transformed data can then be automatically synced to business tools, enabling personalisation at scale.

As data sources become increasingly diverse, a well-designed ETL process enables you to collect, organise, clean and leverage insights effectively.

It is the first step in turning raw, fragmented data into high-performing marketing activation.

💡 Keen to explore how a modern approach to customer data can boost your marketing performance? Discover DinMo’s solutions.