A Guide to ETL Automation in 2025

Does your ETL automation handle schema changes in real-time?

If not, this blog is for you.

Manual ETL pipelines are error-prone and time-consuming, making them difficult to scale.

In 2025, the automation of ETL (Extract, Transform, and Load) processes is crucial for businesses to streamline data workflows, improve data integrity, and minimize processing times.

76% of businesses use automation to standardize workflows, and 31% have already automated at least one function. This highlights the growing importance of automated ETL.

With automated workflows, you can manage growing data volumes and future-proof your data stack.

In this post, we’ll learn how ETL automation enhances workflow efficiency, explore the top ETL automation tools, and examine real-life use cases.

But first, let’s understand ETL and how it differs from manual ETL pipelines. 

What is ETL and ETL Automation?

Unlike the traditional ETL process that facilitated batch-oriented workflows and required heavy scripting, the modern ETL process goes beyond it.

ETL stands for Extract, Transform, and Load. The ETL process starts with extracting raw data from diverse sources, then transforming it into clean, consistent, and analytics-ready datasets, which are then loaded into a data warehouse.

ETL automation refers to the process of automating the steps involved in moving data from a source to a target destination (data warehouse or data lake). This streamlines ETL code processes and minimizes manual intervention.

“Automating ETL (Extract, Transform, Load) with AI is not just an upgrade to the traditional data pipeline; it’s a fundamental shift in how we handle, process, and prepare data for analysis.” – Chris Garzon, founder of the Data Engineer Academy 

Let’s understand how it differs from manual ETL:

Automated ETL	Manual ETL
Requires minimal maintenance	Requires manual updates and debugging
Fast with pre-built connectors	Slow due to custom coding
Low-code/no-code UI	Technical expertise required
Built-in alerts and retries	Requires custom error-handling logic

Why is ETL Automation Important in 2025?

ETL automation is a strategic approach to reduce operational costs and reliance on manual labor. It prevents overconsumption of resources and improves decision-making, managing the growing scale and complexity of modern data environments. 

The key aspects where automation helps in overcoming traditional ETL challenges are:

1. Real-time data integration

By automating the ETL workflow continuously, you can capture and synchronize data from multiple source systems to target data destinations, such as a data warehouse or a data lake. This enables real-time data integration faster than batch-oriented ETL, allowing businesses to be proactive, detect fraud as it happens, and optimize operations on real-time data. 

2. Better data quality

Manual processes are prone to human error, which results in inconsistent datasets and poor data integrity. Workflow automation includes built-in validation mechanisms that standardize data ingestion. This advanced process identifies data anomalies like incorrect formats and missing values to ensure data quality.

3. Scalability

Traditional ETL pipelines require manual coding and assistance in handling schema upgrades, making scaling difficult. Automated ETL pipelines scale effortlessly by processing up to terabytes of complex datasets from diverse sources like IoT devices, cloud storage, and social media.

This allows you to integrate new data sources and expand operations without significant additional costs.

4. Data governance

Manual workflows lack lineage tracking, version control, and centralized monitoring, leading to poor data privacy. Automated ETL pipelines feature built-in compliance features, such as audit logging, data masking, and encryption.

This makes data transformations traceable and ensures strict compliance with data privacy regulations, like GDPR and HIPAA.

How to Get Started With ETL Automation

In data integration vs ETL, data integration is a broader concept, and ETL is a structured process within the integration field. Although the process might feel daunting, breaking it into actionable steps reduces complexity.

1. Assess data sources

• Identify all data sources: This includes databases, APIs, cloud storage, and flat files, to determine whether datasets are structured, semi-structured, or unstructured.

• Understand requirements: Specify the frequency of ETL jobs, data extraction, expected data volumes, and necessary transformations.

2. Choose the right ETL automation tool

• Evaluation: White evaluating ETL tools, ensure compatibility with existing databases, cloud infrastructure, data volume, and data formats. Consider various kinds of tools, like commercial, self-hosted, cloud-native, and open-source ETL tools.

• Features: Prioritize essential features such as no-code/low-code UI, a broad pre-built connector library, auto-scheduling, in-built monitoring mechanisms, and event-based automation.

• Scalability: Prioritize tools that have advanced transformation capabilities and can grow with evolving data needs without exceeding your budget.

Tip: Opt for a no-code tool to minimize the learning curve and enable smooth adaptability across teams.

3. Design the ETL workflow

• ETL: The first is to map out the ETL process, which means defining the extraction, transformation, and loading steps for each data source. This has three key aspects:

• Extraction of data from data sources.
• Operations in the data transformation (filtering, joining, and sorting raw datasets).
• The preferred destination to store data.

• Design: Use a modular design that breaks down the ETL process into independent, reusable components to handle specific tasks, simplifying troubleshooting and updates.

• Data staging: Include a staging area to temporarily store data before loading it into the target destination. This enhances validation and ensures that only high-quality datasets are loaded.

4. Scheduling and orchestration

Scheduling and orchestration refer to coordinating and triggering ETL jobs at predefined times to handle dependencies and ensure end-to-end data processing.

• Job scheduling: Leverage built-in schedulers to run ETL jobs based on business needs, like fixed intervals, event-based, or on-demand triggers.

• Tools: Use cloud orchestration services to allocate compute resources, trigger workflows, optimize throughput, and minimize operational overhead to scale seamlessly.

Note: Although orchestration improves data freshness, it can also increase compute usage and complexity. Therefore, it is important to distinguish between scenarios that require real-time processing and those where batch processing is sufficient.

5. Data quality

Monitoring is the key element in ensuring data quality in automated ETL pipelines. Automation tools include features like recovery mechanisms and performance tracking to maintain efficiency.

• Quality checks: Integrate validation rules, schema checks, and cleansing routines at every stage.

• Logging: Ensure the automated system captures, logs, and notifies you of errors for instant troubleshooting and optimization.

• Performance: Set pipelines that track resource usage and pipeline performance to measure pipeline throughput, assess transformation failures, and facilitate faster debugging.

6. Test and validate the ETL process 

• Test plan: Develop a detailed test plan with defined objectives, scope, and resources for ETL testing. Document business requirements and reporting needs.

• Testing: Use sample data to perform data completeness checks, transformation validation, and loading to ensure consistency in the ETL workflow.

• Performance testing: Evaluate throughput and latency under realistic data loads and run load tests to ensure automatic pipeline recovery from failures.

• Load validation: For CDC and delta loading, verify that only intended records are processed. Also, compare incremental batches with audit tables to prevent missed updates and duplication.

Top ETL Automation Tools

We have curated a list of top cloud ETL tools that can help you build scalable, resilient, automated, and low-maintenance data pipelines. The tools are:

1. Hevo Data

Hevo Data is a fully managed, no-code data movement platform designed for both technical and non-technical users to build and manage data pipelines. Using Hevo, you can set up a data warehouse or database and analyze datasets with minimal effort.

Its core value is enabling organizations to automate the processing of billions of records and scale with evolving workload needs. With just a five-minute setup, you can perform data transformations using Python scripting and dbt models. Transformations are applied automatically as data flows through the pipeline to deliver analytics-ready data.

Key features:

• Data replication: The platform offers flexible data replication that syncs data between the source and the destination. You can schedule the replication process to replicate entire databases and capture changes occurred since the last replication.

• Data processing: Hevo supports both real-time streaming and batch data processing. This combined approach auto-detects and adapts to schema upgrades in pipelines, minimizing manual intervention.

• Data sync: Hevo fetches all the historical data through the Recent Data First approach to deliver the latest Event first.

• Assistant: Hevo Smart Assist is a built-in smart assistant that provides a comprehensive view of the pipeline and initiates alerts about pipeline anomalies. It also enables 24/7 chat support to help you solve queries anytime from anywhere.

Use cases:

• Centralizing data: Organizations operating with data scattered across CRM databases and cloud storage can seamlessly aggregate data from diverse sources using Hevo’s 150+ pre-built connectors. 

The data is loaded into a single repository, creating a unified view for reporting and analytics.

• Replication: Businesses often require near real-time replication of vast transactional databases. They can use Hevo’s CDC feature to efficiently sync source databases with destinations, ensuring minimal latency and high data integrity.

2. Fivetran

Fivetran is a modern, cloud-based platform designed to automate data movement from disparate sources to your target destination. The key offering of this tool is its extensive connector library of 700+ connectors that facilitate data integration from multiple data sources.

The platform features pre-built data models with fast historical syncs and end-to-end pipeline updates with low latency. Its in-built data standardization mechanisms enable smooth integration with BI and visualization tools.

Key features:

• Data blocking: This feature eliminates the risk of Personally Identifiable Information (PII) getting leaked. Fivetran removes specific tables and columns during replication to sync only relevant data with the destination.

• Schema migration: Fivetran handles the changes in a connector’s schema to ensure data integrity and flow in downstream systems. This rectifies issues and incorporates the source data.

• Deployment: The platform facilitates cloud-based, on-premise, and hybrid deployments to allow businesses to host pipelines with an extensible solution complete with metadata sharing, APIs, and more.

Use cases:

• Real-time analytics: Enterprises require up-to-date dashboards to make informed decisions. By automating data extraction and syncing from SaaS apps, databases, and files into cloud data warehouses in real-time, Fivetran ensures analytics teams work with current, reliable data.

Teams can instantly access terabytes of operational data for analytics and reporting.

• Modernization: Fivetran helps organizations that shift from on-premise systems to cloud environments. It automates the migration and ongoing synchronization of data from diverse sources, supporting flexible data architectures. Moreover, automated schema handling and pre-built connectors centralize data efficiently.

3. Matillion

Matillion is an ETL/ELT data integration platform designed for cloud database platforms. The platform pushes down data transformations into data warehouses and processes large-scale rows in seconds with real-time feedback.

The unique offering is the visual pipeline builder with pre-built connectors that empower users to automate and manage complex ETL workflows. Data teams can handle vast data volumes while maintaining enterprise-grade security and governance.

Key features:

• UI: The platform features a user-friendly, drag-and-drop browser-based interface, allowing both technical and non-technical users to design, manage, and automate complex pipelines with minimal coding.

• Custom connector: Apart from the extensive connector library, Matillion provides 80 out-of-the-box connectors along with the liberty to build customized connectors.

• Collaboration: Matillion features built-in version control, collaboration tools, and audit mechanisms to ensure compliance with privacy regulations and encourage teamwork.

Use cases:

• Data processing: A global retail company needs to analyze billions of customer transactions daily from multiple stores and online platforms. By automating ETL with Matillion, the company processes and integrates massive datasets efficiently, enabling timely insights into customer behavior and sales trends.

• Data compliance: Hospitals and clinics must integrate sensitive patient data from various sources while adhering to strict privacy regulations. Matillion streamlines data integration and transformation, ensuring data quality, integrity, and compliance with industry standards like HIPAA, CCPA, and GDPR.

What is ETL Testing?

ETL testing is the process of verifying and validating that data is accurately extracted from data sources, correctly transformed, and properly loaded into target systems. This ensures data integrity, completeness, and quality throughout the ETL pipeline.

Its importance in ETL automation:

• ETL testing identifies anomalies and data inconsistencies early in the pipeline to prevent flawed data from reaching downstream systems.

• Validate accurate incorporation of new data models and ETL structures with proper data mapping.

• Ensures seamless integration of new data sources into existing workflows without any errors.

• Maintain data integrity in scenarios when ETL scripts are updated.

• Early detection reduces the risk of costly data issues, regulatory violations, and inaccurate reporting.

To sum it up, ETL testing is important to confirm if the process is built as per business requirements and specifications. It ensures compliance with internal policies and external regulations (such as GDPR and HIPAA), minimizing legal risks.

What Are the Top ETL Testing Tools?

Here are the three leading ETL testing tools in 2025:

1. iceDQ

iceDQ is an enterprise-grade data testing automation tool designed to automate the creation, execution, and management of ETL test cases, preventing error-prone testing. The platform enables users to define automation rules for data validation, ensuring data integrity.

Moreover, iceDQ supports connectivity with diverse databases, files, and APIs to enable comprehensive testing across heterogeneous environments.

Key features:

• UI: Offers a no-code/low-code UI that helps you automate test integration and powerful scripting. The rule-based testing approach manages complex scenarios with advanced scripting.

• High performance: iceDQ enables in-memory and Spark-based cluster processing. It tests millions of records every second with flexible deployment options.

• Data Ops: iceDQ enables DataOps with an API-first, CI/CD-compatible platform that automates end-to-end data testing, supports regression packs, and ensures data quality across evolving pipelines.

Use cases:

• Testing for data warehouses: It ensures data integrity by reconciling source and target data, detecting mismatches, duplicates, and format issues. You can validate ETL processes by checking data extraction, transformation, and loading steps.

• Cloud migration testing: The tool automates testing during data migration projects. This ensures datasets are completely and accurately transferred from legacy systems to new platforms.

2. QuerySurge

QuerySurge is a smart, AI-driven testing solution for data warehouse, BI reporting, data migration, ERP, and big data projects. The platform automates the data validation process as data moves from source systems, transformation stages, and finally into the target destinations.

QuerySurge offers 200+ data store integrations that support databases, Hadoop data lakes, flat files, NoSQL stores, and data warehouses.

Key features:

• AI-driven test creation: QuerySurge leverages generative AI to generate validation tests based on data mapping, reducing the expertise required to create comprehensive test frameworks.

• BI tester: The built-in BI tester is an end-to-end testing solution for data sourced from a Big Data lake. It supports full regression reports of BI data and migration testing from one BI vendor to another.

• Design library: Use the QuerySurge Design Library to develop powerful tests across existing data architecture. 

Use cases:

• Regression and functional testing: ETL testers use QuerySurge to perform both functional and regression testing. This ensures that new changes or enhancements to ETL code do not introduce errors or break existing functionality.

• Operational monitoring: Operations teams leverage QuerySurge for ongoing monitoring of production ETL runs. The platform provides real-time dashboards, alerts, and analytics, allowing teams to proactively identify and resolve data quality issues before they impact business intelligence or reporting.

3. BiG EVAL

BiG EVAL is an autopilot testing software for BI teams designed to facilitate end-to-end integration testing and prevent manual validation for different data formats. BiG EVAL automates the process of comparing and checking data between source systems, staging areas, data warehouses, and all intermediate components.

The tool applies test cases automatically across the entire data warehouse based on metadata.

Key features:

• User-defined test cases: The easy-to-use scripting language offers great flexibility in scenarios where you require your testing algorithms.

• Simple dashboards: Offers intuitive dashboards for monitoring test results and alerting users about data quality issues, enabling quick identification and resolution of problems.

• Scalability: BiG EVAL can handle thousands of test cases daily and adapt to data validation needs. This is well-suited for enterprise environments to scale efficiently.

Use cases:

• Unit testing: During ETL development, BiG EVAL allows teams to test individual transformation tasks or formulas in isolation, ensuring each component functions correctly before integration.

• Regression testing: When ETL processes or data models evolve, BiG EVAL automatically reruns test suites. This ensures that recent changes don’t break existing functionality, supporting continuous integration and deployment.

Real-Life Use Cases of ETL Automation

Here are the top 5 real-world use cases of ETL automation:

1. E-commerce

E-commerce businesses collect data from multiple sources, including payment gateways and customer databases. Automation of ETL pipelines facilitates effortless integration with diverse sources into a centralized data repository.

For example, an e-commerce brand extracts data from Amazon and PayPal and applies built-in transformations to load it into a unified data storage. The storage of standardized data formats delivers the latest insights and fosters real-time analytics.

2. Finance

Finance firms merge data from diverse systems for reporting and analytics. ETL automation extracts data from accounting databases, bank transactions, and tax reports while ensuring consistency and avoiding errors. The process validates numbers and categorizes transactions in the transformation stage before loading them into the target system.

For example, JP Morgan Chase developed a real-time ETL framework to facilitate ML hedging and risk management. The framework automated the data processing of tons of transactions per hour and facilitated low-latency fraud detection. 

3. Healthcare

Healthcare institutions gather data from electronic health records (EHRs), insurance documents, and medical test reports. Automated ETL workflows filter, standardize, and aggregate patient data, providing a comprehensive view of health records to healthcare professionals.

For example, the Cleveland Clinic automates ETL pipelines to consolidate patient records from various systems. This enables clinics to access timely, unified medical data, streamlining workflows and accelerating decision-making.

4. Marketing

Digital marketing services accumulate data from web analytics platforms like Google Analytics, CRM systems, email marketing platforms, and social media applications. ETL automation aggregates the accumulated data into a centralized database to evaluate campaign performance and customer engagement.

For example, Airbnb leverages ETL pipelines to process tons of user data sourced from web activity, bookings, and campaign engagement. These pipelines power personalization systems that identify high-value users and deliver timely, behavior-driven recommendations.

5. Banking

The banking sector uses ETL transformation to integrate data from various sources, including customer accounts, transaction reports, and external databases. Automated workflows source transaction data, apply error detection algorithms for transformation, and load the datasets into a monitoring system. 

For example, within PayPal’s Data Mesh architecture, ETL automation processes billions of transactions daily. Data is ingested in real-time to detect errors, minimize false positives, and ensure legitimate transactions.

5 Best Practices for ETL Automation

The five key ETL best practices include:

1. Define clear objectives

Before implementing automated ETL pipelines, thoroughly understand your organization’s goals and data needs.

• Collaborate with stakeholders to document the expected outcomes of your ETL workflows.
• Specify the data sources, transformation rules, and loading procedures.
• Setting measurable and clear objectives makes the workflow purpose-driven and easier to maintain.

2. Design for scalability

With growing complexities and data volume, ETL pipelines must scale effortlessly.

• Build modular ETL workflows to develop, test, and reuse each component independently.
• Leverage parallel processing and partitioning to manage vast datasets and enable faster data processing.

3. Automate data quality checks

Automated data validation fosters trustworthy analytics.

• Implement validation rules, cleansing routines, and schema checks for early error detection.
• Use automation tools to identify duplicates, missing values, and data mismatches.
• Set up error-handling mechanisms to isolate datasets and run analysis without interrupting the pipeline.

4. Prioritize monitoring

Efficient automation requires robust scheduling and robust monitoring.

• Leverage the ETL tool’s built-in scheduling features to automate job execution at desired intervals or in response to specific events.
• Set up monitoring dashboards to track job status, performance metrics, and pipeline health.
• Automate alerting and logging tools to notify teams about pipeline failures, anomalies, and downtimes.

5. Maintain documentation

A detailed documentation prevents confusion while operating.

• Document reusable components, templates, and transformation logic for both common and complex ETL jobs.
• Maintain thorough documentation of ETL workflows, including data sources, schedules, and error-handling procedures.
• Frequently review the documentation to reflect changes in system configuration, streamlining troubleshooting, and future ETL tasks.

Scaling Smarter with ETL Automation

ETL automation isn’t a luxury, it’s a necessity in the rapidly growing data ecosystem. In 2025, orgamizations that adapt to this ETL revolution are moving ahead. From delivering real-time insights, improving operational efficiency, to aligning with business requirements, ETL automation marks its importance as a growth enabler.

Whether you are a tech startup or an enterprise, now is the time to combine ETL automation with ETL testing to develop a resilient workflow. Follow the best practices outlined in this blog and start your automation journey.

Sign up for Hevo’s 14-day free trial to experience automated ETL workflows with advanced features.

FAQs on ETL Automation