What Is the ETL Process? Complete Guide to ETL, ELT, and Data Integration

When it comes to moving data from one system to another, ETL has long been the gold standard. Extract, transform, load—it’s a tried-and-true framework for making raw data usable across different platforms. And more recently, ELT has emerged as a cloud-first evolution of that model. However, as business tools and data pipelines become increasingly complex, traditional ETL methods are starting to reveal their limitations, particularly for teams without dedicated engineering resources.

Today, no-code platforms like Knack are giving teams the ability to build powerful data workflows without relying on custom code or expensive IT infrastructure. In this guide, we’ll walk you through everything you need to know about ETL and ELT and discuss when no-code might be the better option.

Understanding What Is the ETL Process (Extract, Transform, Load)

Before modern data tools, business teams relied on developers to build custom pipelines to move data from point A to point B. The ETL process—short for extract, transform, load—changed that by offering a repeatable, scalable way to centralize information from multiple sources. Still widely used today, ETL is the backbone of data pipelines that power everything from dashboards and reports to AI-driven insights. Let’s take a closer look at the ETL process.

Step 1 of ETL Process: Extract – Gathering Raw Data

The first step in any ETL pipeline is extracting data from its original sources. These sources can include CRMs, ERPs, APIs, flat files like CSVs, and both SQL and NoSQL databases. The goal here is to pull raw, unstructured (or semi-structured) data into a staging area where it can be processed further.

At this stage, you’re not changing the data; you’re simply gathering it. This is especially useful when dealing with siloed systems that don’t easily talk to each other. ETL helps bridge those gaps and bring all your data into one centralized workflow.

Step 2 of ETL Process: Transform – Preparing Data for Analysis

Once the data has been extracted, it must be cleaned. This transformation step is where raw inputs get turned into analysis-ready formats. That might mean standardizing date formats, normalizing inconsistent entries, or enriching datasets by adding new columns or calculated values.

Other common techniques include:

• Data validation: Checking for errors or missing fields
• Data mapping: Aligning different formats or schemas
• Filtering: Excluding irrelevant records
• Aggregation: Summarizing values for reporting

Step 3 of ETL Process: Load – Delivering Data to Target Systems

In the final step, transformed data is loaded into a destination system like a data warehouse, cloud storage solution, or application dashboard. How you load data depends on the needs of your business.

• Full Load: Transfers the entire dataset every time; simple but resource-heavy.
• Incremental Load: Only adds or updates new and changed records—much more efficient for growing databases.
• Batch vs. Real-Time: Batch loading sends data at set intervals, while real-time (or streaming) pipelines push updates continuously.

Each method has its trade-offs, and choosing the right one depends on your data volume, latency requirements, and integration goals.

What Is a Data Pipeline and How Does It Relate to the ETL Process?

A data pipeline is a system that transfers data from a source (such as a CRM or SQL database) to a destination (like a data warehouse, dashboard, or application). Along the way, the pipeline may automatically clean, enrich, filter, or reformat the data, depending on your business logic. Data pipelines are designed to handle scheduled batch jobs, real-time events, and complex workflows across tools.

ETL Process as a Subset of Data Pipelines

ETL is one of the most established models for building data pipelines, but it’s not the only one. In fact, ETL can be seen as a subset of the many ways data can be processed and delivered within a pipeline. Other variations include:

• ELT, where transformation happens after data is loaded
• Streaming pipelines that process data in real time
• No-code data pipeline workflows, like those built with Knack, move data automatically without writing code

ETL has played a key role in traditional data integration, but modern pipelines are more flexible, modular, and business-friendly than ever.

ETL vs ELT: Key Differences in the ETL Process

As cloud computing and data storage technologies have evolved, so have the strategies for moving and transforming data. While ETL has been the go-to method for years, a newer approach—ELT—has emerged as a better fit for cloud-native and big data environments. Understanding the difference between ETL vs. ELT is key to choosing the right method for your data needs.

Aspect	ETL (Extract → Transform → Load)	ELT (Extract → Load → Transform)
Sequence	Data is transformed before loading into the data warehouse	Data is loaded first, then transformed inside the warehouse
Best For	Legacy systems, strict compliance, structured transformation	Cloud platforms, big data, flexible schema requirements
Performance	Slower with large datasets due to transformation before load	Faster on modern infrastructure with scalable storage
Storage Use	Requires staging area or intermediary storage	Uses the target data warehouse for both storage and compute
Flexibility	Limited flexibility after transformation	High flexibility—transformations can be adjusted post-load
Popular Tools	Informatica, Talend, IBM DataStage	Snowflake, BigQuery, Azure Synapse, Knack (no-code)

How ELT Works (Compared to ETL Process)

In ELT (Extract, Load, Transform), data is first extracted from the source system, then loaded directly into the destination before being transformed within the destination environment. This approach has several advantages:

• Scalability: ELT leverages the power of cloud platforms like Snowflake, BigQuery, and Redshift to transform massive datasets quickly.
• Flexible schemas: You can load raw data first and decide how to structure it later, which is ideal for iterative analysis.
• Cost and performance: Pushing transformations downstream means you’re using powerful (and often cheaper) cloud compute resources.
  

As businesses adopt cloud-first strategies, ELT has become a natural fit for managing complex, high-volume data pipelines.

When to Use ETL Process vs ELT

So, how do you choose between ETL and ELT? It depends on your infrastructure, data volume, and use case.

Use ETL when:

• You need to clean or filter data before loading it into a system
• You’re working with legacy systems or on-premise databases
• Your target system has limited processing power

Use ELT when:

• You’re using a modern, cloud-based data warehouse
• You want to store raw data for future reprocessing or analysis
• You need scalability and fast performance with large datasets

The Rise of No-Code Data Integration in ETL Process

As data becomes more central to workflows across organizations, the demand for flexible, easy-to-build data solutions is growing fast. However, not every team has access to developers or wants to spend weeks writing custom scripts just to move data around.

No-code platforms are changing the game by putting data tools directly in the hands of business users. With visual builders, drag-and-drop interfaces, and automation tools, teams can now design ETL-like workflows without writing a single line of code. The result? Faster insights, less engineering lift, and more control over your data.

What Is No-Code Data Integration?

No-code data integration refers to building data pipelines and automated workflows using visual interfaces instead of traditional programming. These tools let users connect systems, move data between them, and apply logic from a simple user interface (UI). No-code tools can be used for data tasks like:

• Syncing CRM data to internal dashboards
• Automating weekly sales or inventory reports
• Connecting form submissions to backend databases
• Combining multiple data sources into one unified view

How Knack Enables ETL Process Without Code

Knack makes it easy to replicate traditional ETL or ELT workflows without writing code or setting up complex infrastructure. Here’s how:

• Extract: Use forms, app connections, or API integrations to pull in data from tools like CRMs, spreadsheets, or external platforms.
• Load: Store that data securely in your custom Knack database exactly the way your team needs it.
• Transform: Apply logic rules, filters, validations, and conditional workflows right inside the platform. Need to standardize data formats, clean entries, or trigger downstream updates? You can do it all visually.
• Automate: With native tools and third-party integrations, you can set up end-to-end automations that keep everything running behind the scenes.

Cloud ETL Process: Modern Platforms and Compliance

In the past, building an ETL pipeline meant provisioning servers, maintaining infrastructure, and scaling systems manually. Now, modern businesses are turning to cloud-based ETL services that offer built-in scalability, pay-as-you-go pricing, and powerful integration capabilities without the operational overhead. Cloud ETL gives organizations the flexibility and performance needed to move fast, stay compliant, and focus on outcomes instead of maintenance.

Leading Cloud ETL Process Platforms (AWS, Azure, Snowflake)

There are many cloud-based ETL platforms on the market, but the most popular options are:

• AWS Glue: A fully managed ETL service that integrates with the AWS ecosystem. It automates much of the heavy lifting, including job scheduling, schema discovery, and infrastructure provisioning.
• Azure Data Factory: Microsoft’s cloud ETL solution designed for building data pipelines across hybrid and multi-cloud environments. It offers both code-first and no-code options for building workflows.
• Snowflake: While not an ETL tool by definition, Snowflake enables ELT workflows by offering powerful, cloud-based data warehousing and transformation capabilities.

Security, Privacy & Compliance

Cloud ETL tools are built with security and compliance at the core. Most leading services offer:

• Built-in encryption (both in transit and at rest)
• Role-based access controls and multi-factor authentication
• Compliance with industry standards like GDPR, HIPAA, SOC 2, and ISO 27001
• Automated data governance and audit logging for traceability

Shifting ETL workflows to the cloud enables businesses to meet strict data protection requirements while also reducing the risk of human error. Managed services handle updates, security patches, and infrastructure monitoring, which frees up your team to focus on strategy rather than system upkeep.

Real-World Example: Replacing ETL Process with Knack

Traditional ETL tools are great for large-scale data pipelines, but they can be overkill (and over-budget) for teams that just need to move data from one place to another, apply some logic, and report on it. With Knack, you can replicate the functionality of ETL without the complexity. Let’s look at how a real team might replace a traditional ETL flow using Knack’s no-code tools.

Scenario: Syncing Form Data in ETL Process with Knack

Imagine a distributed team collecting data from multiple retail store locations through custom forms. This could include things like daily sales summaries, inventory levels, or customer feedback. With a traditional setup, you might:

• Extract form submissions into a staging database
• Transform the data to standardize formats, clean errors, and flag outliers
• Load it into a central dashboard or reporting system

With Knack, this entire process can happen inside a single no-code app:

• Forms: Each store submits data through a branded, location-specific form
• Logic rules: Behind the scenes, Knack automatically cleans and normalizes entries (e.g., formatting currencies, correcting date fields, flagging missing info)
• Workflows: Automated actions route entries to the right team members, trigger alerts, or populate related tables
• Dashboards: Regional managers can log in and view real-time summaries, trends, and filtered reports without touching a spreadsheet

No scripts. No external pipeline. Just a streamlined flow that’s easy to update, scale, and adapt as your needs evolve.

Key Tools and Automation in ETL Process

ETL has come a long way from hand-coded scripts and custom SQL routines. Today’s ETL tools empower both technical and non-technical users to build powerful data workflows. Whether you’re managing a large data warehouse or syncing app data between teams, automation and modern interfaces now play a central role in building and maintaining ETL pipelines.

Drag-and-Drop ETL Tools vs Code-Based ETL Process Tools

Classic ETL platforms like Talend and Informatica are powerful and enterprise-ready, but they often come with steep learning curves, higher costs, and a need for dedicated developers to manage and maintain workflows. 

On the other hand, Knack and other no-code platforms offer drag-and-drop simplicity without sacrificing flexibility. Here’s how they stack up:

Tool	Accessibility	Flexibility	Cost
Knack	Built for everyone	High (no-code logic)	$ (scales with you)
Talend	Developer-focused	High (requires code)	$$$
Informatica	Technical teams only	Enterprise-grade	$$$$

Workflow Automation in ETL Process with APIs and Logic

Modern ETL tools automate data workflows from input to transformation to delivery. Knack users can do this using no-code tools like:

• APIs: Connect external apps to pull or push data into your Knack database
• Filters & logic rules: Automatically clean, flag, or enrich records based on conditions you define
• Scheduled workflows: Run tasks at specific times or in response to triggers, like a new form submission or status update
• Third-party integrations: Use tools like Zapier, Make, or direct webhooks to plug into virtually any external system

Best Practices and Considerations for ETL Process Success

No matter if you’re using a legacy tool, building in the cloud, or creating workflows in a no-code platform like Knack, every successful ETL pipeline has a few things in common: solid planning, clear documentation, and built-in controls to ensure data accuracy and integrity.

Modern tools make it easier than ever to build data pipelines, but without the right foundation, things can quickly spiral into disorganized or unreliable data. Let’s explore a few key areas that help keep your ETL workflows sustainable.

ETL Process Metadata, Schema Mapping, and Documentation

Before building any ETL flow, it’s essential to get your data model right. That means thinking through:

• Schema design: How your data is structured, including relationships between objects
• Field mapping: Aligning fields across systems, especially when names or formats differ
• Metadata tracking: Keeping tabs on where data comes from, how it’s used, and what it connects to
• Documentation: Creating a record of your workflow logic so teams can understand, audit, and update it over time

Even in no-code tools, taking time to name fields clearly, track changes, and define your schema upfront can prevent confusion later, especially as more users interact with your data.

Ensuring Data Quality and Auditability in ETL Process

A pipeline is only as good as the data it delivers. That’s why data quality controls should be built into every step of your ETL process. With tools like Knack, you can automate these best practices:

• Field validation: Set rules to ensure users input the right formats (like dates, emails, or dropdown selections)
• Deduplication: Use logic to prevent duplicate records based on key fields
• Audit trails: Automatically track who made changes and when, giving you visibility into data history
• Rollback options: Allow admins to undo changes or restore previous versions if something goes wrong

5 Reasons to Choose Knack for No-Code ETL Process

Looking to replace complex ETL pipelines with something faster, simpler, and more accessible? Knack makes it easy to build and automate data workflows—no coding required. Here’s why Knack is the go-to platform for no-code ETL:

1. Visual Builder: Create workflows, filters, and transformations with an intuitive drag-and-drop interface.
2. Seamless Integrations: Connect to CRMs, forms, spreadsheets, databases, and third-party apps with ease.
3. Built-In Logic: Automate data validation, transformation, and routing using smart rules and condition-based actions.
4. Secure Permissions: Control access with role-based permissions and encrypted data handling.
5. All-in-One Platform: Combine frontend forms, backend databases, and automation in a single, no-code solution.

Transforming the ETL Process for the Modern Era with Knack

While traditional ETL and ELT still have their place, modern teams need solutions that are more flexible, more accessible, and less dependent on engineering resources. With a visual builder, built-in logic, and seamless integrations, Knack empowers teams to design powerful ETL workflows that are easy to build, easy to scale, and tailored to real business needs.

Ready to build better data workflows? Sign up with Knack and start building for free today!

FAQ: What Is the ETL Process and Common Questions

What is the ETL process in simple terms?

ETL (extract, transform, load) is a three-step process that helps you move data from one system to another. First, you extract data from sources like CRMs, spreadsheets, or APIs. Then, you transform it by cleaning, formatting, or enriching it. Finally, you load the data into a destination like a database, data warehouse, or dashboard. It’s how teams turn raw information into something structured, useful, and ready to analyze.

What’s the Difference Between ETL Process and ELT?

Both ETL (extract, transform, load) and ELT (extract, load, transform) are methods for moving and preparing data, but the processes are slightly different. In ETL, data is transformed before it’s loaded into the destination. In ELT, raw data is loaded first and transformed after it’s in the system (usually a cloud data warehouse).

Can No-Code Tools Replace the ETL Process?

In many cases, no-code tools can replace traditional ETL. No-code tools like Knack let you build ETL-like workflows using visual interfaces, logic rules, and app integrations instead of writing code. They’re ideal for teams that want to automate data movement, apply transformations, and build dashboards or reports without relying on a developer or managing complex infrastructure.