Introduction to ETL
Every business today runs on data—but that data often lives in different places. ETL (extract, transform, load) is the process that brings it all together. It pulls raw data from various systems, cleans it up, and moves it into a central location so teams can analyze it and use it to inform business decisions.
As companies collect more data than ever, understanding ETL has become essential to making the most of that information. Whether you’re cleaning spreadsheets or managing large data flows, ETL helps you turn messy data into clear answers.
This guide will walk you through all the key concepts of ETL—from the basics to more advanced strategies and trends. You’ll also learn how new tools like Coalesce are making ETL faster, smarter, and easier for data teams.
What is ETL?
ETL stands for extract, transform, load. It’s a three-step process that moves data from its original source to a destination, usually a data warehouse or data lake:
- Extract: Collecting raw data from different systems
- Transform: Cleaning, reshaping, and preparing the data for analysis
- Load: Moving the final, ready-to-use data into a central storage system
ETL makes sure your data is accurate and ready to support analytics, dashboards, and business decisions.
Why ETL is important today
ETL plays a major role in helping organisations make sense of their data. Here’s why:
- It connects siloed data systems to create a complete view of operations
- It improves data quality, making sure the information is clean and reliable
- It supports historical analysis, storing records over time
- It helps meet compliance rules by tracking how data changes
- It prepares data for reporting tools like dashboards and business intelligence platforms
Without ETL, most organisations struggle to get useful insights from their data.
The extract, transform, load process explained
1. Extract: pulling the raw data
Let’s break down the three main steps of ETL:
This step collects data from different places, such as:
- Relational databases (e.g., MySQL, Oracle)
- NoSQL databases (e.g., MongoDB)
- APIs and cloud apps
- Files (CSV, JSON)
- IoT sensors
There are three main ways to extract data:
- Full extraction: Pulling all data each time
- Incremental extraction: Getting only the new or changed data
- Change data capture (CDC): Tracking and capturing changes as they happen
Metadata—like column names or data types—is also collected during this step to help guide transformations later.
2. Transform: cleaning and formatting
Once the data is collected, it needs to be prepared for use. This step might include:
- Fixing missing or incorrect values
- Standardising formats (like dates or units)
- Filtering out unneeded data
- Merging datasets together
- Summarising numbers or values
- Creating new fields or categories
- Changing data types
Some transformations are simple, but others involve detailed business logic. Tools like Coalesce help simplify this step by offering visual interfaces and reusable components.
3. Load: moving data to its final destination
The final step is delivering the cleaned and formatted data to:
- Data warehouses & platforms (e.g., Snowflake, Databricks, Fabric)
- Data lakes (e.g., AWS S3, Azure Data Lake)
- Data marts (smaller databases focused on specific teams)
Loading can happen in several ways:
- Full load: Replacing all data each time
- Incremental load: Updating only what has changed
- Upsert: Updating old records and adding new ones
This step often includes validation and logging to make sure the data moved correctly and can be tracked.
ETL vs. ELT: What’s the difference?
While ETL was the standard for many years, cloud platforms have made ELT—extract, load, transform—a popular alternative. In ELT, data is loaded into the destination system first and transformed afterward using the power of modern cloud warehouses.
| Feature | ETL | ELT |
| Order | Transform before loading | Load before transforming |
| Processing | Happens outside warehouse | Happens inside warehouse |
| Best for | Complex logic | Big data and flexibility |
| Storage | Lower (only final data) | Higher (stores all data) |
| Scalability | Limited by ETL tools | Scales with cloud systems |
Many teams now combine both approaches depending on their needs. Coalesce works well in ELT setups, especially with Snowflake, while still offering the control ETL teams expect.
Dive deeper into the key differences and use cases in our guide on ETL vs. ELT.
For teams upgrading from legacy tools to cloud-native platforms, this webinar with Group 1001 on moving from legacy ETL to the modern data stack offers a practical overview.
When to use ETL
ETL is a great choice when:
- Your data requires major cleanup before it’s usable
- Your destination system can’t handle large transformations
- You’re working with consistent and structured data
- Compliance rules mean sensitive data must be transformed before storage
- You need to enforce strict data quality
In these cases, ETL gives you control over how data is cleaned, shaped, and delivered.
Common challenges—and how to solve them
1. Dealing with large data volumes
When working with huge datasets, performance can suffer. You can fix this with techniques like incremental loading, breaking queries into smaller chunks, and using cloud-native tools like Coalesce that automatically scale and optimise workloads.
2. Low-data quality
Bad data leads to bad decisions. Spot issues early with profiling, set clear rules for fixing errors, and track data quality over time. Coalesce makes it easy to add these checks into your pipeline.
3. Changing data sources
Source systems often evolve, breaking your pipeline. Use metadata-driven tools and column-level lineage to keep track of changes. Coalesce helps detect and adapt to schema updates automatically.
4. Complex transformations
Advanced business logic can become hard to manage. Break it into smaller, reusable steps. Coalesce’s modular design lets you reuse components and stay flexible as needs grow.
5. Monitoring and maintenance
ETL failures can go unnoticed. Add logging, alerts, and dashboards so you always know when something breaks. Automating tests and health checks keeps everything running smoothly.
ETL tools and technologies
The ETL ecosystem includes a mix of traditional tools, cloud services, and open-source frameworks.
Traditional ETL tools:
- Informatica, IBM, DataStage, Microsoft SSIS, Oracle Data Integrator, and Talend: Enterprise-grade tools built for structured, on-premises environments.
Cloud-native platforms:
Coalesce is a low-code data transformation and governance platform designed to help organizations build, manage, and optimize data pipelines efficiently using an intuitive visual interface—without sacrificing the power and control of writing native SQL.
Open-source tools:
The right tool depends on your team, architecture, and data volume. Coalesce excels where teams want to work faster without losing control. Not sure which transformation tool is right for your team? This comparison guide from Coalesce walks through key criteria to help you choose.
Best practices for a strong ETL strategy
To get the most out of ETL, you need more than just tools—you need a strategy.
- Design smart ETL architectures:
- Use staging areas for intermediate data
- Add validation and quality checks
- Track data lineage with metadata
- Monitor performance at each step
- You can also explore this breakdown of CTEs vs. pipelines to learn why modular pipelines can outperform common SQL patterns in complex workflows
- Use good development practices:
- Keep pipelines modular and reusable
- Apply naming conventions and version control
- Build in error handling and restart logic
- Document everything—especially business logic
- Test thoroughly:
- Unit tests for individual steps
- Integration tests for end-to-end workflows
- Performance tests for scalability
- Regression tests to catch unintended changes
- Make governance easy:
- Assign data ownership
- Control who can access what
- Track changes and data usage
- Automate documentation wherever possible
Coalesce supports all of these practices, with built-in tools for versioning, testing, lineage, and role-based access.
Modern ETL trends and innovations
As data infrastructure evolves, so does ETL. Here are some of the key trends shaping the future of data pipelines.
Real-time and streaming ETL
Traditional ETL runs on schedules—hourly, daily, weekly. But in today’s fast-moving landscape, many businesses need data in real time.
That’s where streaming ETL comes in. It processes data continuously, as it arrives—enabling faster decision-making and more responsive operations.
Technologies like Apache Kafka and Apache Flink help make this possible by supporting event-based pipelines. Change Data Capture (CDC) is also gaining ground, allowing teams to track database updates in real time.
Solutions like Estuary, built specifically for real-time streaming ETL workflows, make it easier to ingest, transform, and act on data as it moves—without the delays of batch processing.
Coalesce integrates with streaming tools and supports near real-time transformation on Snowflake, which is useful for use cases like fraud detection, customer recommendations, and inventory updates.
DataOps and automation
Just like DevOps transformed software, DataOps is transforming data. It brings agility, automation, and collaboration into ETL workflows.
Some of the most impactful practices include:
- Infrastructure-as-code for spinning up ETL environments
- CI/CD for pipelines, enabling faster and safer updates
- Automated testing, so issues are caught before reaching production
- Self-service ETL, letting analysts build and deploy their own workflows without IT support
Coalesce supports all of these by offering a code-friendly platform with built-in testing, versioning, and environment promotion. Curious how automation compares to manual coordination? Learn how to move from manual orchestration to fully automated, synchronized data movement using Coalesce + Fivetran’s native integration.
AI-powered ETL
AI is improving ETL by reducing manual work and improving accuracy. Here’s how:
- Data quality monitoring: AI spots anomalies and alerts you to issues
- Smart schema mapping: Speeds up onboarding by matching fields automatically
- Pipeline optimization: AI recommends faster, cheaper ways to run workflows
- Natural language interfaces: Let users describe what they want, and the system builds the transformation logic
Coalesce leverages AI in its AI Data Catalog, AI-powered documentation assistant, and intelligent schema suggestions—making teams more productive and data governance more effective.
Low-code and no-code ETL
Not everyone who works with data writes code. Low-code ETL platforms open up data transformation to analysts and business users.
Modern platforms (like Coalesce) allow teams to:
- Build workflows using visual drag-and-drop tools
- Use prebuilt templates for common tasks
- Reuse and customise logic across teams
- Work in SQL if needed, but not rely on it exclusively
This flexibility speeds up development and increases collaboration across departments.
Why Coalesce stands out
Coalesce is built for the modern data team. It brings together the best of traditional ETL with the scalability and flexibility of the cloud.
Key features:
- A visual interface and full SQL access side by side
- Column-level data lineage and impact analysis
- Version control, testing, and CI/CD support
- Optimised for Snowflake and other cloud platforms
- AI-driven tools that automate documentation, schema mapping, and quality checks
If you’re using Snowflake and want to control transformation costs, this cost optimization guide breaks down how to maximize performance without overspending.
With Coalesce, teams build faster, collaborate better, and deliver reliable data pipelines that scale.
Final thoughts
ETL is more than just a technical process—it’s how organizations turn raw data into real business value. Whether you’re dealing with a few spreadsheets or a global data warehouse, mastering ETL helps you stay in control and move fast.
Modern tools like Coalesce make that journey smoother. By combining automation, collaboration, and governance, they let data teams focus on insights—not infrastructure.
Want to see how Coalesce can simplify your ETL workflows?
Design, transform, and automate your data models—all in one place.