Top 15 ETL tools in 2023

Modern advanced marketing analytics is hard to imagine without ETL tools. After all, before a company starts building reports and searching for insights, all the data they collect from disparate sources must be processed: cleaned, verified, brought into a single format, and combined. In this article, we detail the top 15 ETL softwares for 2023 so you can choose the best one for your business.

What is ETL?

ETL (Extract, Transform, Load) is the data integration process that underpins data-driven analytics. It consists of three steps:

  1. Data is extracted from the original source.

  2. Data is then transformed into a format suitable for analysis.

  3. Finally, data is loaded into storage, a data lake, or a business intelligence (BI) system.

ETL provides the foundation for successful data analysis and a single source of truth to ensure that all enterprise data is consistent and up to date.

What are ETL tools?

ETL tools are services that help you execute  the Extract, transform, and load process. Simply put, ETL softwares allow companies to collect data of various types from multiple sources, convert it into a single format, and upload it to a centralized repository such as Google BigQuery, Snowflake, or Azure.

What are the benefits of ETL tools?

  • Save time and eliminate manual data processing. ETL tools help you collect, transform, and consolidate data automatically.

  • Make it easy to work with a large amount of complex and diverse data: time zones, client names, device IDs, locations, etc.

  • Reduce the risk of data errors caused by human factors.

  • Improve decision-making. By automating work with critical data and reducing errors, ETL ensures that the data you receive for analysis is high-quality and trustworthy.

  • Because you save time, effort, and resources, the ETL process ultimately helps you increase your ROI.

Let’s consider types of ETL tools.

Types of ETL tools

All ETL tools can be roughly divided into four types depending on their infrastructure and the supporting organization or vendor. Some are designed to work in the local environment, some in the cloud, and others both locally and in the cloud.

1. Cloud-based ETL tools

Cloud-based ETL tools extract data from sources and load it directly into cloud storage. They can then transform this data using the power and scale of the cloud. This is essentially a modern approach to the familiar ETL process, in which data transformation occurs after data is loaded into storage.

Traditional ETL tools extract and transform data from different sources before loading it into the warehouse. With the advent of cloud storage, there is no longer a need for data cleaning at an intermediate stage between the source and the target storage location.

Cloud-based ETL tools are especially relevant for advanced analytics. For example, you can load raw data into a data lake and then combine it with data from other sources or use it to train predictive models. Saving data in its raw format allows analysts to expand their capabilities. This approach is faster because it harnesses the power of modern data processing engines and reduces unnecessary data movement.


2. Enterprise ETL tools

These are ETL tools developed by commercial organizations and are often part of larger analytics platforms. The advantages of enterprise ETL tools include reliability and maturity, as they have been on the market for a long time. They may also offer advanced functionality: a graphical user interface (GUI) for designing ETL flows, support for most relational and non-relational databases, a high level of customer support, and extensive documentation.

In terms of minutes, enterprise ETL tools are usually more expensive than alternatives, require additional training for employees, and are difficult to integrate.


3. Open-source ETL tools

These are free ETL tools that offer a GUI for creating and managing data flows. Thanks to the open-source nature of these services, users can understand how they work and can extend their functionality.

Open-source ETL tools are a budget alternative to paid services. Some do not support complex transformations and may not offer customer support.

4. Custom ETL tools

These are ETL tools that companies create themselves using SQL, Python, or Java. On the one hand, such solutions have great flexibility and can be adapted to business needs. On the other hand, they require a lot of resources for their testing, maintenance, and updating.

What are the criteria for choosing ETL tools?

When choosing an ETL tool, you should consider your business requirements, the amount of data to be collected, the sources of that data, and how you will use it.

What to pay attention to when choosing an ETL tool:

  • Ease of use and maintenance.

  • Speed of the tool.

  • Data security and quality. ETL tools offering data quality audits help identify inconsistencies and duplicates and reduce errors. Monitoring features can warn you if you’re dealing with incompatible data types and other issues.

  • Ability to process data from many different sources. One company can work with hundreds of sources with different data formats. There can be structured and semi-structured data, real-time streaming data, flat files, CSV files, etc. Some of this data is best converted in batches, while other data is best handled through continuous streaming data conversion.

  • The number and variety of connectors available.

  • Scalability. The amount of data collected will only grow over the years. Yes, you might be fine with a local database and batch uploading right now, but will that always be enough for your business? It’s ideal to be able to scale ETL processes and capacity indefinitely! When it comes to making data-driven decisions, think big and fast, and take advantage of cloud storage services (like Google BigQuery) that allow you to quickly and inexpensively process large amounts of data.

  • Ability to integrate with other data platform components, including warehouses and data lakes.

Now that we have covered the types and features of ETL tools, let’s take a look at the most popular of these tools.

Top 15 ETL tools for collecting marketing data

There are a lot of ETL tools on the market to help you simplify your data management while also saving you time and money. Let’s take a look at some of them, starting with ETL software that work in the cloud.

1. OWOX BI

OWOX BI is a no-code ETL/ELT digital analytics platform that simplifies data management and reporting. The OWOX BI platform allows you to collect marketing data for reports of any complexity in secure Google BigQuery cloud storage.

OWOX BI is a no-code ETL/ELT digital analytics platform

Key features of OWOX BI:

  • Automatic data collection from various sources.

  • Automatic importing of raw data into Google BigQuery.

  • Cleaning, deduplication, quality monitoring, and data updating.

  • Data modeling and preparation of business-ready data.

  • Ability to build reports without the help of analysts or knowledge of SQL.

OWOX BI automatically collects raw data from various sources and converts it into a format that’s convenient for building reports. You will receive ready-made data sets automatically transformed into the necessary structure, taking into account the nuances that are important for marketers. You won’t need to spend time developing and maintaining complex transformations, delving into the data structure, and identifying reasons for discrepancies.

OWOX BI frees up your precious time so you can pay more attention to optimizing advertising campaigns and growth areas.

When you rely on OWOX BI, you no longer need to wait for reports from an analyst. Based on simulated data, you can get ready-made dashboards or customized reports that are right for your business.

Due to OWOX BI’s unique approach, you can change data sources and data structures without rewriting SQL queries or changing the order of reports. This is especially relevant with the release of Google Analytics 4.

Sign up for a demo to learn more about the OWOX BI value for your business.

2. AWS Glue

AWS Glue is Amazon’s serverless ETL service that makes it easy to discover, prepare, move, and integrate data from multiple sources for analysis, machine learning, and application development.

AWS Glue

Key Features of AWS Glue:

  • Integration with more than 70 different data sources.

  • Ability to use both a GUI and code (Python/Scala) to create and manage data flows.

  • Possibility to work in both ETL and ELT modes — AWS Glue is mainly focused on batch processing, but it also supports streaming data.

  • Support for custom SQL queries, making for easier data interactions.

  • Ability to run processes on a schedule — For example, you can configure AWS Glue to run your ETL tasks when new data becomes available in Amazon S3 storage.

  • Data Catalog allows you to quickly find different datasets on AWS without moving them around — Once cataloged, data is immediately available for search and query using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

  • Data quality monitoring functionality.

3. Azure Data Factory

Azure Data Factory is Microsoft’s cloud-based ETL service for scalable serverless data integration and transformation. It offers a no-code user interface to intuitively create, monitor, and manage data flows.

Azure Data Factory

Key Features of Azure:

  • Supports integration with various on-premises, cloud-based, and software-as-a-service data sources and sinks, including Azure Blob Storage, Azure SQL Data Warehouse, Azure Cosmos DB, and many others.

  • Azure Offers the capability to create, schedule, and manage data pipelines that can move and transform data between supported data stores.

  • Utilizes a code-free environment for designing ETL and ELT processes, while also providing an option to use transformations in the Azure-Integrated Apache Spark-based environment.

  • Through Azure Monitor and Azure Management, you can monitor real-time data integration runs, pinpoint failures, and re-run activities inside the pipeline.

  • For organizations that rely on SQL Server Integration Services (SSIS) for ETL tasks, Azure Data Factory offers managed capabilities for running SSIS packages in the cloud.

  • Supports event-driven ETL processes. For example, a pipeline can be configured to run when a file is uploaded to Azure Blob Storage.

4. Google Cloud Dataflow

Dataflow is a cloud-based ETL service from Google that allows you to process both streaming and batch data and does not require you to own a server.

Google Cloud Dataflow

Key features of Google Cloud Dataflow:

  • Supports a lot of data sources (excluding SaaS) — Cloud Dataflow offers both batch and streaming data ingestion. For batch processing, it can access both GCP-hosted and local databases. PubSub is used for streaming. The service transfers data to Google Cloud Storage or BigQuery.

  • Runs Apache Beam pipelines on the Google Cloud Platform — Apache offers Java, Python, and Go SDKs for presenting and transferring datasets, both batch and streaming. This allows users to choose the right SDK for their data pipeline.

  • Flexible pricing — You only pay for the resources you consume, and resources automatically scale based on your requirements and workload.

  • Dataflow SQL allows you to use your SQL skills to develop Dataflow streaming pipelines right from the BigQuery web interface.

  • Built-in monitoring allows you to troubleshoot batch and streaming pipelines in a timely manner. You can also set alerts for outdated data and system delays.

  • High level of customer support — Google offers several support plans for the Google Cloud Platform (which Cloud Dataflow is a part of) as well as comprehensive documentation.

5. Integrate.io

Integrate.io is an ETL data integration platform designed specifically for e-commerce projects. It allows you to process data from hundreds of sources using various methods (Integrate.io ETL, Reverse ETL, API Management). It offers an intuitive, no-code interface to make it easier for non-technical people to work with data streams.

Integrate.io

Key features of Integrate.io:

  • Built-in connectors for 150+ data sources and destinations, including data warehouses, databases, and SaaS cloud platforms.

  • Automatic transformation — There are over 220 conversion options with minimal code to meet any data requirement.

  • Monitoring and alerts — Set up automatic alerts to make sure your pipelines are running on schedule.

  • Ability to receive data from any source that has a Rest API — If there is no Rest API, you can create your own using the Integrate.io API generator.

  • Support and consultation by phone or video call.

Next, let’s consider enterprise ETL tools.

6. Informatica PowerCenter

PowerCenter is a high-performance enterprise data integration platform developed by Informatica. The company also has a cloud-native ETL and ELT solution called Cloud Data Integration.

Informatica PowerCenter

Key features of PowerCenter:

  • Huge number of connectors, including for cloud data stores such as AWS, Azure, Google Cloud, and Salesforce.
  • Supports both batch and streaming data processing.
  • A graphical user interface and pre-built transformations make PowerCenter useful for non-technical professionals, such as marketers.
  • Automated testing and data validation — PowerCenter warns about errors and failures in the operation of data pipelines.
  • Additional services are available that allow you to design, deploy, and monitor data pipelines. For example, Repository Manager helps manage users, Designer lets users specify the flow of data from source to destination, and Workflow Manager defines the task sequence.

7. Oracle Data Integrator

Oracle Data Integrator is an enterprise ETL platform for building, deploying, and managing complex data warehouses. The tool loads and transforms data into a data warehouse using the capabilities of the target database instead of relying on a regular ETL server. Pre-built connectors simplify integration by automating the manual integration tasks required to connect databases and big data.

Oracle Data Integrator

Key features of Oracle Data Integrator:

  • Compatible with databases such as Sybase, IBM DB2, Teradata, Netezza, and Exadata.
  • Supports work in ETL and ELT modes.
  • Automatically finds errors in data and processes them before moving them to the target storage location.
  • Built-in big data support — You can use Apache Spark code in accordance with big data standards to transform and map data.

8. SAP Data Services

SAP Data Services is enterprise data management software. The tool allows you to extract data from any source as well as transform, integrate, and format this data into any target database. You can use it to create data marts or data warehouses of any kind.

8. SAP Data Services

Key features of SAP Data Services:

  • A graphical user interface greatly simplifies the creation and transformation of data streams.

  • Can work both in batch mode and in real time.

  • Supports integrations with Windows, Sun Solaris, AIX, and Linux.

  • Great for scaling no matter the number of clients.

  • The shallow learning curve and drag-and-drop interface make it possible for data analysts or data engineers to use this tool without special coding skills.

  • Easy to plan and control ETL processes.

  • The presence of variables helps to avoid repetitive tasks — Variables allow users to perform various actions, such as decide which steps to perform in a task or which environment the task should run in, and easily modify process steps without recreating the entire task.

  • Built-in functions (if/then, or deduplication logic) help to normalize data and improve its quality.

  • Great for companies that use SAP as their ERP system.

9. IBM DataStage

IBM DataStage is a data integration tool that helps you design, develop, and execute data movement and transformation tasks. DataStage supports both ETL and ELT processes. The base version is for local deployment. However, a cloud version of the service is also available, called IBM Cloud Pak for Data.

IBM DataStage

Key features of IBM DataStage:

  • Large number of built-in connectors for integration with data sources and data stores (including Oracle, Hadoop System, and all services included in IBM InfoSphere Information Server).
  • Complete any ETL task 30% faster thanks to a parallel engine and workload balancing.
  • User-friendly interface and machine learning-assisted design help to reduce development costs.
  • Data lineage allows you to see how data is transformed and integrated.
  • IBM InfoSphere QualityStage allows you to monitor data quality.
  • Especially relevant for companies working with large datasets and large enterprises.

10. Microsoft SQL Server Integration Services (SSIS)

SQL Server Integration Services is an enterprise ETL platform for data integration and transformation. It allows you to extract and transform data from sources such as XML files, flat files, and relational databases, then load it into a data warehouse. Because it is a Microsoft product, SSIS only supports Microsoft SQL Server.

Microsoft SQL Server Integration Services (SSIS)

Key features of SSIS:

  • Can use SSIS GUI tools to create pipelines without writing a single line of code.
  • Offers a wide range of built-in tasks and transformations that minimize the amount of code required for development.
  • Can be integrated with Salesforce and CRM using plugins; can also be integrated with change control software such as TFS and GitHub.
  • Debugging capabilities and easy error handling in data streams.

Now let’s consider open-source ETL tools.

11. Talend Open Studio (TOS)

Talend Open Studio is free open-source integration software that helps turn complex data into understandable information for decision-makers. This simple and intuitive tool is widely used in the US. It can easily compete with products by other major players.

With TOS, you can start building basic data pipelines in no time. You can perform simple ETL and data integration tasks, get graphical profiles of your data, and manage files from a locally installed open-source environment.

Talend Open Studio (TOS)

Key features of Talend Open Studio:

  • Over 900 connectors to connect various data sources — Data sources can be connected through the Open Studio GUI using drag-and-drop from Excel, Dropbox, Oracle, Salesforce, Microsoft Dynamics, and other data sources.
  • Works great with cloud storage giants such as Amazon AWS, Google Cloud, and Microsoft Azure.
  • Java technology allows users to integrate multiple scripts from libraries around the world.
  • The Talend Community is a place to share best practices and find new tricks you haven’t tried.

12. Pentaho Data Integration (PDI)

Pentaho Data Integration (formerly known as Kettle), is an open-source ETL tool owned by Hitachi. The service has several graphical user interfaces for creating data pipelines. Users can design tasks and data transformations using the Spoon PDI client and then run them using Kitchen.

Pentaho Data Integration (PDI)

Key features of Pentaho Data Integration:

  • Available in two versions: Community and Enterprise (with advanced functionality).
  • Can be deployed in the cloud or on-premises, though it specializes in local batch scenarios for ETL.
  • Convenient graphical user interface with drag-and-drop functionality.
  • Shared library simplifies ETL execution and development process.
  • Works on the basis of ETL procedures stored in XML format.
  • Differs from competitors in that it does not require code generation.

13. Apache Hadoop

Apache Hadoop is an open-source platform for processing and storing large amounts of data by distributing the computing load across computing clusters. The main advantage of Hadoop is scalability. It seamlessly transitions from running on a single node to thousands of nodes. In addition, its code can be changed according to business requirements.

Apache Hadoop

Key features of Hadoop:

  • Open-source based on Java applications and therefore compatible with all platforms.
  • Fault tolerant — When a node fails, data on that node can be easily restored from other nodes.
  • Multiple copies of data mean it will be available even in the event of a hardware failure.
  • No need for a distributed computing client, as the framework takes care of everything.

14. Skyvia Data Integration

Skyvia is Devart’s all-in-one cloud data platform for integration, management, backup, and data access.

Skyvia Data Integration is a no-code ETL and ELT tool for various data integration scenarios. It works with CSV files, databases (SQL Server, Oracle, PostgreSQL, MySQL), cloud storage (Amazon Redshift, Google BigQuery, Snowflake), and applications (Salesforce, HubSpot, Dynamics CRM, and many more).

Skyvia Data Integration

Key features of Skyvia Data Integration:

  • Working with the cloud saves you from manual updates or deployments.
  • Allows you to import data into cloud applications and databases, replicate cloud data, and export it to a CSV file for sharing.
  • Creates a fully customizable data sync — You decide exactly what you want to extract, including custom fields and objects.
  • Creating integrations does not require special technical knowledge.
  • Ability to automatically run integrations on a schedule
  • Duplicate-free data import with bi-directional synchronization.
  • Ready-made templates for common data integration scenarios.

15. Jaspersoft

Jaspersoft ETL is Jaspersoft’s open-source software that is data and architecture agnostic. This means you can connect to data from any source and work with it anywhere: on-premises, in the cloud, or in a hybrid environment. In addition, you can make changes to the Jaspersoft source code according to your needs.

The Jaspersoft tool is part of the Jaspersoft Business Intelligence suite, which offers a customizable, flexible, and developer-friendly business intelligence platform.

Jaspersoft

Key features of Jaspersoft:

  • Integration with standard data management systems (Hadoop, Google Analytics, and Cassandra), applications (SugarCRM, SAP, Salesforce), and big data environments (Hadoop, MongoDB).
  • Can be deployed both locally and in the cloud.
  • Graphical user interface allows the user to easily design, plan, and execute data movement and transformation.
  • Activity dashboard helps monitor the execution of ETL tasks and the tool’s performance.
  • Mobile app where you can check your data from anywhere at any time.

Key takeaways

The volumes of data collected by companies are getting bigger every day and will continue to grow. For now, working with local databases and batch loading is enough, but very soon, this will no longer satisfy business needs. Thus, the ability to scale ETL processes is convenient and especially relevant for advanced analytics.

When it comes to choosing an ETL tool, think about the specific needs of your business. If you are working locally and your data is predictable and comes from only a few sources, then a traditional ETL tool will be enough. But don’t forget that more and more companies are moving to a cloud or hybrid architecture.

Book a demo

Gain clarity for better decisions without chaos

No switching between platforms. Get the reports you need to focus on campaign optimization

Book a demo

FAQ

Expand all Close all
  • What are some popular ETL tools for collecting marketing data?

    Some popular ETL tools for collecting marketing data include Alteryx, Talend, Stitch, Fivetran, and OWOX BI Pipeline.
  • Why is ETL important for collecting marketing data?

    Marketing data is often scattered across different platforms and databases. ETL tools make it easier to gather this data, transform it into a standardized format, and make it accessible for analysis.
  • What is ETL in the context of data analytics?

    ETL stands for extract, transform, and load. It is the process of acquiring data from different sources, transforming it into a usable format, and loading it to a destination database for analysis.