5 Must Have ETL Development Tools

Mastering the right ETL development tool is a game-changer for any data engineer. ETL tools help accelerate data pipeline design, reduce manual tasks, and ensure data is consistent and high quality for machine learning algorithms.

We've compiled a list of the top five must-have ETL development tools designed to optimize your data workflows and empower you to unlock valuable insights from your data sources.

Here are the five key takeaways about ETL development tools: 

• Using ETL tools in data engineering allows for better machine learning processes and streamlined data analytics.
• When evaluating ETL tools, consider popular features such as integration capabilities, pricing structure, and robust customer support.
• The complexity of tools like Fivetran and Talend can pose challenges for non-technical users, highlighting the importance of accessibility and ease of use in ETL tools.
• Just because a tool has more pre-built connectors doesn't mean that it's a better ETL tool. The best ETL tool for your business will depend on your current and future needs.
• Free trials offer valuable opportunities to test and assess a platform’s functionalities and fit before committing. 

What Is ETL?

ETL stands for Extract, Transform, and Load, which are the three main steps for managing and moving data within a system, typically from multiple sources, into a data warehouse.

ETL is fundamental in data engineering because it facilitates streamlined data management and eliminates common human errors. Data engineers who use ETL processes create more efficient data pipelines to feed their machine-learning algorithm's prediction capabilities.

What Are ETL Development Tools?

ETL development tools are software applications designed to manage the process of extracting, transforming, and loading data from one system to another.

These tools play a critical role in business intelligence, data integration, and data warehousing strategies by helping businesses consolidate data from multiple sources into a unified view for data analysis.

What are the ETL Use Cases for Data Engineering?

There are many ways in which ETL tools are used in data engineering. Here are a few of the most popular real-world examples:

• Data warehousing: Through ETL, data is extracted from various sources, transformed into a unified format, and stored in a data warehouse, making data analysis tasks more efficient.
• Data migration: ETL tools facilitate efficient and accurate data transfer between databases and systems with minimal downtime.
• Real-time data processing: ETL tools enable real-time data processing, which is useful for making quick business decisions.
• Data cleaning: Using ETL processes, data is identified, corrected, or replicated to ensure high-quality and accurate data for machine learning algorithms.
• Business intelligence: By integrating data from various sources and loading it into BI tools, ETL tools enable businesses to visualize clean and consistent data in a structured format.

What To Look for in an ETL Development Tool

When evaluating ETL development tools, prioritize your specific requirements and challenges to ensure you choose the tool that aligns with your goals.

Here are some of the most popular factors when deciding on an ETL tool for your business:

• Integration capabilities: Does the tool support seamless connection with various data sources like the databases, APIs, apps, and other software solutions your organization uses?
• Ease of use: How does this tool cater to users of all technical proficiency? Does it have a simple user interface?
• Advanced features: Does the platform support common programming languages like Python, MySQL, and Java?
• Pricing considerations: Is the vendor's pricing aligned with your current and future needs?
• Scalability: How well can the platform scale with your business as you grow and your needs change? Can it support more integrations and data without compromising speed and quality?
• Testimonials and reviews: What are current users of the ETL tool saying about the platform? Are there common themes you can glean through multiple reviews?

Although all ETL tools should meet these fundamental criteria, your specific business needs may require you to consider additional factors. 

What are the Best ETL Tools for Handling Complex Regulated Environments?

Integrate.io, Talend, and Fivetran are top ETL tools for complex regulated environments. Integrate.io offers end-to-end encryption, role-based access control, and compliance with HIPAA, GDPR, SOC 2, and other industry standards, making it ideal for sectors like healthcare, finance, and government. It enables secure, low-code pipelines across 200+ sources with built-in monitoring and audit trails. Talend provides advanced data governance frameworks, while Informatica delivers enterprise-scale compliance controls and metadata management for large, highly regulated organizations.

The best ETL tool for you will vary depending on your situation and specific needs. Here are five of the best ETL development tools available today:

Integrate.io

Rating: 4.3/5 (G2)

Key Features:

• Scheduling and automation of data pipelines
• 150+ pre-built integrations
• Drag-and-drop interface for building data workflows
• ETL & Reverse ETL
• ELT & CDC

Integrate.io stands out as one of the best ETL tools for handling complex regulated environments due to its ability to simplify the creation and management of data pipelines without sacrificing functionality or data quality.

Featuring a user-friendly drag-and-drop interface, Integrate.io ranks as one of the most accessible ETL tools available, enabling everyone from beginners to expert data engineers to become proficient within minutes.

Pros:

• Low-code interface: Easy drag-and-drop builder suitable for non-engineers.

• Cloud-native: Fully hosted and managed, no need to manage infrastructure.

• Versatile connectivity: Supports a broad range of sources including databases, SaaS platforms, and cloud storage.

• Real-time and scheduled jobs: Enables both batch and near real-time workflows.

• Strong customer support: Consistently rated highly for support responsiveness.

• ETL and ELT support: Flexibility depending on data strategy.

Cons:

• Pricing aimed at mid-market and Enterprise with no entry-level pricing for SMB

Pricing:

Fixed fee, unlimited usage-based model.

The Verdict:

Integrate.io is an excellent choice for businesses of any size seeking enterprise-grade ETL capabilities in an uncomplicated format. This platform offers pre-built integrations that effortlessly connect with the most popular SaaS tools, like Snowflake. Due to its user-friendly interface, anyone in your organization can easily create and manage their data pipelines, making Integrate.io a versatile and powerful tool to address any data use cases.

AWS Glue

Rating: 4.2/5 (G2)

Key Features:

• Centralized metadata management
• AWS-native and serverless data integration
• Seamless compatibility with AWS products

As a serverless SQL database, AWS Glue from Amazon simplifies data processing tasks for data engineers by facilitating the discovery, cleaning, enrichment, and transfer of data between data warehouses.

AWS Glue eliminates the need for infrastructure and maintenance costs. When setting up a data warehouse, like Amazon Redshift, or data lake, the AWS Glue Catalog is available to help create, monitor, search, and query ETL tasks.

Pros:

• Serverless: No infrastructure management required.

• Tight integration with AWS ecosystem: Works seamlessly with S3, Redshift, Athena, and more.

• Supports PySpark: Offers a powerful transformation engine.

• Data cataloging: Automatically crawls and catalogs data, making it searchable.

• Scalable: Handles large volumes of data efficiently.

Cons:

• Steep learning curve: Especially for non-developers unfamiliar with PySpark or AWS IAM.

• Debugging is difficult: Logs and error tracing are not intuitive.

• Slow job startup times: Cold starts can delay pipeline execution.

• UI limitations: GUI is not as user-friendly compared to other ETL platforms.

Pricing:

Billing for AWS Glue is based on usage; increased tool utilization results in higher costs. You'll be charged hourly for discovering, processing, and loading data. 

A monthly fee does apply to store and access metadata within AWS Glue Catalog. The first million objects stored are free.

The Verdict:

AWS Glue is an excellent solution for companies that are existing AWS customers since it works seamlessly across their ecosystem of tools. The tool offers 70 pre-existing data sources to connect to, which may limit functionality if you use multiple applications and don’t have the technical resources to integrate them properly.

Talend

Rating: 4.0/5 (G2)

Key Features:

• Open-source platform
• ELT/ETL
• 1,000+ connectors for various data sources

As an open-source ETL solution, Talend provides numerous pre-built connectors and data integration capabilities suitable for on-premises, cloud, or hybrid environments. 

Talend’s ETL capabilities make it a good choice for data warehousing, synchronization, migration, and other data management tasks. Due to its open-source nature, Talend is one of the more technical ETL development tools on this list. If you are planning on empowering your non-technical users to create and manage data pipelines, consider a tool that is easier to use.

Pros:

• Open-source availability: Talend Open Studio is free and highly customizable.

• Strong transformation logic: Extensive processing options and component libraries.

• Enterprise version has strong governance features: Lineage, monitoring, data quality, and security.

• Connects to many data sources: Including cloud services, databases, APIs, etc.

Cons:

• Resource-heavy: Requires significant compute resources for complex pipelines.

• Steep learning curve: Especially in the open-source version.

• Not cloud-native by default: Needs configuration or the use of Talend Cloud.

• UI can feel outdated: Particularly in Talend Open Studio.

Pricing:

Talend's Open Studio is available free of charge due to its open-source nature. However, Talend Data Fabric's paid data integration solution's pricing details are private, with some reviewers suggesting the platform may be costly.

The Verdict:

Talend is a reliable choice for organizations searching for a robust ETL tool that can integrate with an array of data sources and handle unstructured data. Non-technical users may have challenges using this application due to its complexity, which means technical resources are necessary to create and manage data pipelines.

Fivetran

Rating: 4.2/5 (G2)

Key Features:

• ETL
• Real-time CDC
• 300+ pre-built connectors

With its cloud-based ETL solution, Fivetran includes an extensive range of pre-built data sources and the option to incorporate custom integrations. The platform mainly targets technical users, allowing developers and data engineers to build cloud functions for schema management from sources such as AWS, Microsoft Azure, and Google Cloud Platform.

Fivetran has more than 300 built-in connectors, ensuring seamless data transfer from different sources and datasets and making it a great match for companies using multiple systems in their data management workflows.

Pros:

• Fully managed: No maintenance, upgrades, or pipeline setup required.

• Wide connector coverage: Supports 300+ sources, updated regularly.

• Reliable incremental sync: Great for maintaining up-to-date data with minimal load.

• Schema evolution: Automatically adjusts to changes in source schemas.

Cons:

• Expensive: Usage-based pricing (Monthly Active Rows) can get costly at scale.

• Limited transformations: No drag-and-drop interface; relies on dbt for transformations.

• Lack of customization: Little control over sync frequency, retry logic, or data flow.

Pricing:

Fivetran operates on a consumption-based billing model. The company offers a free plan limited to 500,000 monthly active rows, while their three paid plans include a 14-day free trial.

The Verdict:

Although Fivetran is a robust and customizable ETL tool, it focuses on serving a more technical audience, making building data pipelines difficult for non-technical team members. If budget constraints and technical resources aren't an issue, Fivetran is a good choice.

It’s worth noting that the platform does not transform data before sending it to a data warehouse, which could lead to complications in your data workflow processes.

Apache Hadoop

Rating: 4.4/5  (G2)

Key Features:

• Apache Hive for SQL-like data querying and transformation
• Apache Pig for scripting complex data transformations
• Apache Airflow for workflow management
• Apache Spark for handling real-time data efficiently

Apache Hadoop is not traditionally considered an ETL  tool in the same way that platforms such as Integrate.io, Talend, or Fivetran are. Instead, it's an open-source software library that provides frameworks for distributed storage and processing of large data sets.

However, Hadoop's ecosystem contains tools used in ETL processes. For example, Apache Pig and Apache Hive are used for data transformation, Apache Airflow for workflow management, and Apache Spark for handling real-time data efficiently.

So, while Hadoop is not specifically an ETL tool, it’s a must-have tool for data engineers, especially when dealing with big data. 

Pros:

• Highly scalable: Handles petabyte-scale datasets across distributed systems.

• Open-source: No licensing fees.

• Ecosystem flexibility: Works with Spark, Hive, Pig, HBase, and others.

• Good for batch processing: Designed for high-throughput large-scale analytics.

Cons:

• Complex setup and maintenance: Requires significant operational overhead.

• Not real-time: Primarily designed for batch processing, not streaming or interactive queries.

• Steep learning curve: Requires knowledge of HDFS, MapReduce, and related components.

• Shifting industry adoption: Many teams have moved toward cloud-native or Spark-based alternatives.

Pricing

Apache Hadoop is open source and available to download at no cost.

The Verdict

Apache Hadoop presents a unique offering for data engineers dealing with big data scenarios. Although not a traditional ETL tool like its counterparts, Hadoop's versatile ecosystem and associated applications adapt to various ETL processes. Data engineers who require a scalable and high-performance framework for managing sizable datasets can benefit from incorporating Hadoop into their toolbox.

Comparison of Top ETL Development Tools

How To Evaluate ETL Tools

To ensure you choose the best data integration tool to meet your needs, use the following steps as a starting point:

• Assess your requirements: Consider your current system requirements, your required integrations, and your team's technical expertise when identifying your data integration needs.
• Document your must-have features: Opt for platforms that offer user-friendly interfaces, no-code/low-code capabilities, pre-built connectors with the tools you use today, and automation.
• Review pricing and support: Pick a tool with transparent pricing, considering future needs to the best of your abilities. Make sure the company has detailed documentation, tutorials, and customer support options.  
• Shortlist and test: Narrow down your options based on your research and take advantage of each platform's free trial to choose the best tool based on your requirements.
• Select the tool and provide training: Once you’ve selected a platform, provide ongoing training for non-technical users to support them in creating and managing their own data pipelines.

ETL development tools are a powerful addition to any data engineer's toolkit, especially when used to feed machine learning algorithms for data analytics.

Integrate.io: The Ideal ETL Tool for Data Engineers

Integrate.io is a comprehensive ETL development tool that delivers enterprise-level features within an easy-to-use, no-code/low-code data integration platform.

With clear pricing, superior user accessibility, scalability, and robust ETL capabilities, Integrate.io is a must-have tool for data engineers. Try it yourself by signing up for a free 14-day trial or schedule a demo with one of our experts to get the most out of your trial.

FAQs

What are the best low-code ETL solutions for data integration?

• Integrate.io offers a low-code interface with drag-and-drop pipeline design and visual connectors for seamless integration without coding.

• Matillion delivers a cloud-native, low-code ELT platform with AI-assisted data engineering workflows.

• Boomi is an iPaaS solution with visual design, API management, and automation capabilities for integration projects.

• Keboola provides a balance of no-code simplicity and low-code flexibility for building integrations and custom transformations.

Which ETL tools offer extensive data transformation capabilities?

• KNIME features modular, GUI-based workflows supporting advanced transformations like joins, splits, conversions, and enrichment.

• Pentaho Data Integration (PDI) is an enterprise ETL tool with a wide range of transformation components and a user-friendly design studio.

• Apache NiFi includes hundreds of processors for transformation, format conversion, enrichment, and secure routing.

• Integrate.io combines powerful prebuilt transformation logic with a visual pipeline environment.

What ETL platforms include strong monitoring and data observability?

• IBM App Connect Enterprise provides detailed performance dashboards, connector throughput metrics, and resource usage monitoring.

• Apache NiFi offers built-in audit logging, data provenance tracking, and flow monitoring for full observability.

• Many ETL platforms, including Integrate.io, also include pipeline monitoring, error logging, and real-time alerts.

What’s the difference between ETL and ELT development tools?

• ETL tools perform transformations before loading data into the target system.

• ELT tools load raw data first, then transform it using the target system’s compute engine.
  Modern ETL tools often support both approaches.

Are ETL development tools low-code or code-intensive?

Many modern ETL tools offer low-code visual interfaces for designing workflows, but still allow scripting for complex logic. Open-source and developer-oriented tools may require more coding in SQL, Python, or Java.