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Types of ETL Tools: Must-Have Features + Top 10 Tools for 2024

Ethan
CEO, Portable

ETL helps businesses drive data analytics and business intelligence. Those analytics insights improve daily operations, boost customer relationships, and help companies react quickly to market trends.

But a data integration strategy needs an ETL process built around a high-quality ETL tool.

This guide covers what ETL tools are and the seven types of ETL tools on the market. We'll also share the top ETL tools for 2023 and the five factors to consider when choosing your platform.

What Are ETL Tools?

  • ETL stands for extract, transform, and load. It's a data integration method used to collect data from sources, convert this data into usable formats, and load it into a central data store.
  • Data engineers design ETL workflows to centralize data sets via data warehousing. Data analysts then use this data for analytics, machine learning, and business intelligence.
  • ETL tools are applications that enable data teams to run ETL data pipelines in minutes. They can create multiple pipelines to aggregate and push data to various data stores. These tools streamline pipeline maintenance.
  • ETL software provides faster data access and speeds up business intelligence. Tools cost less time, money, and labor than custom ETL development.

ETL Data Management Stages

There are three main stages in the ETL process.

  • Extract: Data is collected from different sources using connectors. A data source is any application, platform, or database that contains relevant information. It includes CRM, marketing software, sales databases, and more. Source data moves to a staging area.
  • Transform: Raw data from sources, collected in various formats and data types, is cleansed and standardized for analysis.
  • Load: Transformed source data is added to a target system, like a data warehouse or data lake. In some ETL workflows, data is fed directly into business intelligence tools.

Types of ETL Tools

There are hundreds of ETL solutions on the market, but you can categorize them into seven types of ETL tools.

The types of ETL tools are not mutually exclusive. The ETL tool you choose could have multiple functions --- for example, commercial, cloud-based, and real-time ETL. 

Open-Source ETL Tools

  • Open-source ETL tools allow users to download and modify source code. Most of these applications are free. Users can extend their functionality using Python, SQL, and other scripting languages.
  • Open-source tools vary widely in the features and integrations they offer. Some provide basic ETL capabilities, while others are a collection of data tools that can run complex ETL processes.
  • On-premise open-source ETL applications are ideal for limited data needs. Enterprises and growing businesses need advanced cloud features to process large volumes and varieties of data.
  • Airbyte, Meltano, Talend Open Studio, and Hadoop are popular open-source ETL tools.
  • Generally, support is limited or nonexistent with free ETL tools. 

Commercial ETL Tools

  • Commercial ETL tools are SaaS platforms that enable data pipeline creation and maintenance. They have advanced built-in features and connectivity. These tools are often part of a more extensive data integration and management suite.
  • Data teams in enterprises may prefer proprietary tools because they are easy to manage and scale.
  • Commercial tools are built for high-volume data processing. Many of these ETL applications are fully managed. The vendor manages all the backend processes related to feature updates, security, and compliance.
  • Some ETL tools allow integrations and custom connectors to extend the existing features.
  • Commercial tools include Fivetran, IBM DataStage, and Oracle Data Integrator.
  • Generally, paid ETL tools offer a standard level of support with optional premium support SLAs. 

Cloud-Based ETL Tools

  • Cloud-based ETL tools are SaaS applications that are hosted and deployed in the cloud. They eliminate expenses related to maintaining a data infrastructure on-premise.
  • Most businesses are migrating to cloud platforms since they simplify data integration. These tools enable greater flexibility by integrating with other apps and data sources.
  • Cloud ETL platforms can be expensive or affordable. They often use subscription models where businesses only pay for the necessary components and resources.
  • Portable, Microsoft Azure Data Factory, and Fivetran are examples of cloud-based ETL tools.

On-Premises ETL Tools

  • On-premise ETL tools have a physical infrastructure for data ingestion and storage. They use software to manage resources and run pipelines.
  • This is an older data management system. It is primarily used by companies that prioritize data security.
  • Managing an on-premise ETL infrastructure is expensive, labor-intensive, and harder to modify. Scaling and integrations are also more complex.
  • Informatica PowerCenter and Matillion are examples of on-premises tools and many open-source options.

Custom ETL Tools

  • Businesses with internal data engineering teams can build custom ETL tools using Java or Python.
  • Custom ETL applications are flexible since they can be tailored to a business's specific use cases.
  • However, they are expensive and require the most effort in building and maintenance. Internal teams have to do testing, documentation, and development.
  • Modern data teams can use commercial ETL platforms with custom connectors to increase functionality.

Batch Processing ETL Tools

  • Batch processing is a data ingestion method where data is collected and processed in batches. Data teams design a predetermined schedule for when this processing occurs.
  • For example, you can collect data weekly from Source A and use a bimonthly schedule for Source B.
  • Batch processing ETL tools are ideal for businesses that want to collect and process data during non-working or off-peak hours.
  • Portable is an example of a batch-processing ETL tool.

Real-Time ETL Tools

  • Real-time ETL tools extract, transform, and load data from source systems in real-time. Instant data collection and processing are becoming a necessity for modern data teams. They need fresh data to deliver relevant market insights.
  • These tools use technology like change data capture (CDC) to see changes to source data and start data extraction immediately.
  • Google Cloud Dataflow and Kafka are examples of real-time data tools.

5 Factors to Consider When Choosing an ETL Tool

Picking an ETL tool that works for your data integration strategy can be challenging. Here are five crucial factors that impact your decision:

1. Integration With Existing Systems

  • Integration with existing systems and applications is vital for effective data integration. Your data platform must work with all the sources that drive your data analytics.
  • A tool that only connects to popular, mainstream sources like Salesforce or Mailchimp is ineffective.
  • For ease of use, teams can choose a platform that can be augmented with custom data connectors.

2. Data Volume and Complexity

  • Big data processing is only possible with a high-performance ETL tool that can handle large volumes of data and complex data flows.
  • Your ETL tool must ingest structured, semi-structured, and unstructured data. It should work with various formats, including JSON, XML, and more. It must also extract source data without harming data integrity.

3. Scalability and Flexibility

  • Scaling is crucial in the modern data landscape. Data teams must be able to add a new source or create a new data pipeline without interrupting current processes.
  • Flexibility to design the ETL workflow to match analytics and business goals also matters.

4. Data Security and Compliance

  • Data breaches significantly impact a business's reputation and revenue. It drives existing customers away and makes prospects wary.
  • Compliance is another issue that data teams need help with. The mounting number of local, national, and international data regulations can take much work to comply with. A fully-managed ETL tool eliminates this worry.

5. Cost and Budget Constraints

  • Data teams have limited budgets to deliver the best data insights they can. They must invest in cost-effective tools with dynamic features and flexible payment models to make the most of this budget.
  • Alternatively, they can use free or low-cost ETL tools to extend the capabilities of larger, paid platforms.

Top ETL tools for 2023

The number of ETL applications to pick from is ever-increasing. To save you time, we've listed the top 10 ETL tools for 2023:

Portable

  • If your data team uses long-tail data sources, Portable is the best ETL tool for you. The free ETL platform has 350+ connectors for data sources that are otherwise hard to find.
  • We also have a development team that can create custom connectors in as little as a few hours.
  • Portable handles the ongoing maintenance for long-tail connectors. We also provide 24/7 customer support via call.
  • These features and affordable pricing make Portable the best tool for modern data teams. Teams can quickly collect data from multiple sources and focus on analysis rather than data pipeline maintenance.
  • Fully supports popular data destinations, including Snowflake, BigQuery, Redshift, PostgreSQL, and MySQL.

Fivetran

  • Fivetran is a fully-managed, cloud-based enterprise data integration platform. It helps users build no-code data pipelines.
  • The platform has 100+ connectors for apps, databases, and storage options, including Microsoft Azure, Amazon Redshift, Snowflake, and Google BigQuery.
  • It also has an extensive security, privacy, and compliance program to keep data safe during data migration.
  • Fivetran simplifies ELT data integration by enabling teams to transform data via pre-built data models. It uses a zero-maintenance architecture that can automate tasks like removing data duplicates and data translation.

Airbyte

  • Airbyte is a free, open-source data integration platform. It extracts data from many sources and loads it into a cloud data warehouse, data lake, or another repository.
  • It provides 300+ standard data connectors, which can be modified to fit different use cases.
  • You can also modify the platform to add/remove security measures, change deployment specifics, and more.

AWS Glue

  • Amazon Web Services (AWS) Glue is a cloud-based, serverless data integration solution. It enables data teams to create no-code ETL jobs using a drag-and-drop editor. It works with other tools in the AWS ecosystem, like S3, to discover, move, transform, and store data.
  • The platform uses the AWS Glue Data Catalog to store all your datasets in data lakes. AWS Glue Data Quality configures and monitors data quality.

Talend

  • Talend is a data management solution that offers a unified data integration platform, Talend Data Fabric. This platform combines integration, data security and governance, and metadata management.
  • Data Fabric can be used for data integration in on-premise, cloud, and hybrid environments. It supports various integration methods, including ETL, ELT, real-time processing, and batch processing.
  • Talend also offers an open-source integration solution --- Talend Open Studio. It supports simple data integration and extracts data from many sources, including SaaS apps and relational database management systems.

Stitch

  • Stitch is a no-code data integration tool that is part of Talend. It has connectors for 130+ sources --- including Google BigQuery, SQL Server, and MySQL --- and enables teams to centralize data in a data warehouse.
  • Data engineers can integrate the tool with applications in their data workflow using the Stitch Import API and Singer open-source framework.
  • The tool also has scheduling, logging, monitoring, and error-handling features.

Apache Airflow

  • Apache Airflow is a Python-based workflow automation tool used to create and run data pipelines. While it is not a dedicated ETL tool, Airflow uses Directed Acyclic Graphs (DAGs) to manage and organize ETL data pipelines.
  • Airflow is open-source and free to use. It also includes a web-based UI for monitoring workflows and sending email alerts when jobs fail.

Pentaho Data Integration

  • Pentaho is a business intelligence solution offering data integration, reporting, and OLAP services. Pentaho Data Integration (PDI) is an open-source ETL tool that uses XML files to represent pipelines and executes them using its ETL engine.
  • The tool generates reports in various formats: Excel, Text, PDF, CSV, XML, and HTML.
  • PDI is also known for its Spoon graphical desktop user interface, which creates transformations and manages jobs.

Hevo Data

  • Hevo Data is a cloud-based data integration solution. It helps businesses collect data from over 150 sources without writing any code.
  • It supports real-time data replication, automatic schema detection, data transformation, and dashboards. Customers have access to 24/7 support.

Integrate.io

  • Integrate.io is a low-code data integration platform that enables low-code data aggregation. Users can build ETL data pipelines in minutes and use the platform's 220+ built-in transformations to format this data.
  • Data teams can extend the tool's capabilities using rich expression language, advanced APIs, and webhooks.
  • Integrate.io also supports reverse ETL so organizations can create a data-driven environment.

Elevate Your Data Integration Strategy

ETL tools are an essential part of the modern data stack. They streamline data integration and drive better data analytics and business intelligence.

The type of ETL tool you choose depends on your data integration architecture and business and analytics goals. Our comprehensive list of the best ETL tools can help you pick the right one.

Choose Portable to simplify ETL for you by collecting data from hundreds of sources so you can focus on analytics and insights. Try it free today!