Top 7 Tools for ETL on AWS Data Pipelines

ETL (Extract, Transform, Load) is an essential process for modern data management. With the increasing volume of data that businesses generate, it’s crucial to have efficient and scalable tools to handle the data pipeline. 

AWS provides a range of ETL tools, making it easy to extract data from various sources, transform it into a desired format, and load it into data storage systems. 

In this article, we’ll explore the top five tools for ETL on AWS data pipelines. These tools are carefully selected based on their capabilities, ease of use, and reliability. Whether you’re just starting with ETL or looking to upgrade your current setup, this article provides a comprehensive guide to help you choose the right tool for your needs.

What is AWS Data Pipeline?

AWS Data Pipeline is a fully managed service by Amazon Web Services (AWS) that helps move data between different AWS services and on-premises data sources. 

AWS Data Pipeline sits within the wider AWS ecosystem, making it easy to integrate with other AWS services such as Amazon S3, Amazon Redshift, Amazon DynamoDB, and Amazon EC2. The integration capabilities allow users to create complex data processing workflows using a combination of AWS services, with Data Pipeline acting as the orchestration service to manage the movement of data between services.

What are the key components of AWS Data Pipeline?

The primary components of AWS Data Pipeline are:

  • Pipelines define the data movement and processing workflows, which consist of one or more activities executed in a specified order. 
  • Activities define the individual processing steps executed within a pipeline, such as copying data from one location to another or running a script.
  • Preconditions are optional components that determine when an activity should be executed. For example, you can set a precondition to run an activity only if data is present in a specific location, ensuring that data is processed only when available.

In addition to these core components, AWS Data Pipeline includes several features, such as scheduling, error handling, and reporting, that make it easier to manage data pipelines.

What are the key components of AWS Data Pipeline

Business Use Cases For AWS Data Pipeline

The most popular business case for AWS Data Pipeline is data integration. AWS Data Pipeline simplifies the process of unifying data from different sources into a data warehouse or a data lake, making it easier for businesses to analyse and make decisions based on that data. This includes integrating data from databases, applications, files, cloud services, and more. 

Continuous data migration is yet another key use case for AWS Data Pipeline. Moving data from on-premises sources to cloud services like Amazon S3 or Redshift can be challenging, but AWS Data Pipeline provides a reliable and scalable solution for this process. With Data Pipeline, businesses can take advantage of the benefits of cloud computing while minimizing the risks associated with data migration.

What are the key features of AWS Data Pipeline?

Here are some of the key features of the AWS Data Pipeline.

  • Automates workflows between sources and targets, including AWS and on-premise sources like JDBC-based databases
  • Supports scheduling and task chaining based on task success or failure.
  • Offers comprehensive transformations with activities like HiveActivity, PigActivity, SQLActivity, and custom code-based transformations through HadoopActivity.
  • Allows customers to use on-premise systems for data sources or transformations, with task runners on those systems.
  • It provides cost-effective pricing as businesses only pay for used compute resources and a flat fee for periodic tasks.
  • It has a simple interface that enables customers to set up complex workflows with ease.

Top 7 Tools for ETL on AWS Data Pipeline

1. Equalum


Equalum is an end-to-end ETL platform that provides real-time data integration and transformation capabilities on top of AWS Data Pipelines. It has built-in connectors to AWS services (including S3, RDS, and EMR) for a quick and seamless setup. 

Key features:

  • CDC capabilities: Streams real-time data into AWS services via industry-leading CDC capabilities.
  • Extensive connectivity options: Supports a wide range of data sources and targets, and seamless connections between AWS services, other cloud services and legacy systems/on-premise sources. 
  • Reduce labor costs: Supports data replication of thousands of objects automatically and supports changes with end-to-end schema evolution, so your data engineers don’t need to spend time on this. 

Best for: Cost reduction. Equalum replicates your operational and transactional data into AWS services. It automates time-consuming administration tasks like error handling, data consistency, and monitoring, so you can leave Equalum to integrate the pipelines without worrying about incorrect, missing, or duplicate data. 

Review: Equalum provides a single platform for core architectural use cases, including CDC replication, streaming ETL, and batch ETL. That is important to our clients because there is no other single-focus product that covers these areas in that much detail, and with this many features on the platform.”

2. AWS Glue

AWS Glue

AWS Glue is one of the most popular AWS ETL tools. It is a fully managed ETL service that simplifies data movement and transformations on AWS Data Pipelines. It offers seamless integration with other AWS services, making it a popular choice among data engineers.

Key features:

  • Serverless infrastructure: Runs ETL jobs without needing to manage any servers.
  • Dynamic data discovery: Automatically discovers data and schemas in AWS data stores.
  • Automated code generation: Generates PySpark or Scala code for transformations and data cleaning.

Best for: Companies looking for a fully managed solution that integrates seamlessly with other AWS services and can scale with changing data demands. With its serverless infrastructure and automated code generation, it reduces the operational overhead associated with data pipelines.

Review: “We utilize AWS Glue in all of our data pipelines and use it to sync external and internal data sources and auto-generate SQL-based ETL based on AWS Glue catalog objects.”

3. AWS Kinesis

AWS Kinesis

AWS Kinesis is a real-time data streaming service provided by AWS. It helps organizations process and analyze large data streams in real-time, making it ideal for big data processing. 

Key features:

  1. Real-time streaming: Processes data as it arrives, which is especially important for time-sensitive applications.
  2. Scalable: Handles large volumes of data, and you can easily scale the number of shards based on your needs.
  3. Secure: Secures the data in transit and at rest.

Best for: Real-time data processing and analysis, as well as for event-driven applications that require low-latency data processing. 

Review: “We used kinesis mainly for batch-processing events in a microservices architecture. The best thing about kinesis would be easy integration and compatibility with other AWS products.”

4. Hevo Data

Hevo Data

Hevo Data offers real-time data migration with a completely automated setup. With integrations with sources and targets like S3 buckets, SaaS apps, and Amazon Redshift, getting set up and implementing ETL without coding expertise is possible. 

Key features:

  1. Automated data migration: Hevo provides automated data migration from various data sources, including databases and cloud storage, to AWS Data Pipeline.
  2. Real-time data transfer: Hevo offers real-time data transfer from the data source to AWS Data Pipeline with minimal latency.

Best for: Organizations looking for a simple, easy-to-set-up solution for data integration that doesn’t require expert knowledge. 

Review: “It really helped us out of a jam in figuring out our data warehouse strategy and implementation.”

5. Talend


Talend is an open-source data integration tool that enables organizations to efficiently manage large-scale data transfers and transformations. It supports a wide range of data sources and destinations, including AWS data pipelines, making it a popular choice for ETL on the cloud.

Key features:

  • Integration options: Supports integration with AWS data pipeline and other cloud platforms
  • Easy to set up: Offers a comprehensive library of pre-built connectors and data transformations
  • Deployment options: Supports deployment on-premise, in the cloud, or as a hybrid solution.

Best for: Organizations looking for an open-source ETL solution that can handle large-scale data transfers and transformations with ease.

Review: “It enables users to scale up and down services as needed. In addition, backup and catastrophe recovery are automated; that’s an advantage.”

6. Keboola


Keboola’s platform has 400 pre-built connectors to help you get started with ETL (and “reverse ETL,” as seen in the image above, which is another ETL use case) quickly, so you can deliver data to all users across your company. It’s an AWS Partner and offers integration with S3. 

Key features:

  • Extensive documentation: Engineers who need support can find plenty of it in Keboola’s widely available documentation.
  • Customer service: Keboola has a team of over 100 data partners to help with integration and setup. 

Best for: An online support network and community. As well as Keboola documentation, you’ll find forums and informative articles. 

Review: “The good collection of extractors & writers, simplicity in building direct ETLs, and ease of setup the orchestrations.”

7. StreamSets


You can execute the StreamSets data integration platform on a range of data processing platforms on AWS, including EC2 and Elastic. It’s an AWS Data & Analytics Competency holder and Advanced Technology Partner. 

Key features:

  • Data drift detection: StreamSets helps identify when there’s a problem with your pipelines with the data drift detection feature. 
  • Native integration: It integrates with AWS Linux 2, Redshift, Kinesis, S3, and more. 

Best for: StreamSets supports both streaming and batch pipelines. 

Review: “Build fast and efficient data pipelines. Setting up environments is not complex and can be done within minutes.”

Unlock the Power of AWS Data Pipeline with Equalum

There are several powerful tools available for performing ETL on AWS Data Pipelines, each with its own strengths and capabilities. If you are looking to take advantage of the power of the AWS ecosystem, Equalum stands out as a comprehensive solution that offers native integration with AWS services and a user-friendly platform for advanced data transformations and automated data replication.
We invite you to discover the full potential of real-time data analytics with Equalum. Get started today.

Top 10 Data Integration Tools for 2023

Data is like breathing to your organization, especially if you’re pushing toward digital transformation and data-backed decision-making. Statista estimates the world will produce over 180 zettabytes of data by 2025. That’s a lot of data! So, what’s the challenge for organizations? Data is scattered across multiple sources, and integrating your data into a single place is a labor-intensive and time-consuming task.

That’s where data integration tools come in. It provides a centralized, efficient, and scalable solution for combining data into a single, unified view. In addition, it has handy additional features like automating manual tasks, improving data quality, enhancing security, and more.

This article will discuss everything you need to know about data integration tools, including the types, features, benefits, and ten different data integration tools you should know about.

Jump to…

data integration graph

What are data integration tools?

A data integration tool is a software system that combines data from multiple sources into a unified view. It automates the process of extracting, transforming, and loading data from different sources, including databases, applications, and cloud services. In addition, data integration platforms include features such as data mapping, data validation, data transformation, and data warehousing, as well as security and management tools to help ensure the quality and integrity of the integrated data.

Types of Data Integration Tools

  • Extract, transform, load (ETL) platforms: Used to extract data from source systems, transform them into a common format, and load it into a data warehouse or data lake.
  • Enterprise application integration (EAI) platforms: Designed to integrate business applications such as ERP and CRM systems.
  • Cloud integration platforms: Cloud integration platforms integrate data from cloud-based applications and services, such as Salesforce, Amazon Web Services (AWS), and Microsoft Azure.
  • Real-time integration platforms: Used to integrate data in real-time for time-sensitive applications such as financial services and e-commerce.
  • Change data capture (CDC) platforms: CDC platforms capture and replicate changes to data in real-time.
  • Master data management (MDM) platforms: Used to manage and maintain a consistent view of critical business data, such as customer and product information.

10 Benefits of Using a Data Integration Tools

  1. Improved data quality
  2. Increased efficiency
  3. Enhanced decision-making
  4. Real-time insights
  5. Improved data security
  6. Scalability
  7. Reduced costs through automation
  8. Improved data governance
  9. Better collaboration
  10. Increased agility

Key Features to Look For in a Data Integration Tool

When evaluating data integration tools, it’s important to consider the following key features:

  • Data connectivity: Linking to various data sources, such as on-premises systems, cloud apps, and databases.
  • Data transformation: Including tools for transforming and cleaning data, such as mapping, filtering, and normalizing, to ensure that data is accurate and consistent.
  • Data integration: Supporting real-time data integration and offering a centralized view of data from several sources.
  • Scalability: Handling large amounts of data and supporting the organization’s increasing data integration needs as it grows.
  • Security: Providing secure data management, including controlling access to sensitive data and ensuring that data is protected against unauthorized access and breaches.

Top 12 Data Integration Tools For 2023

1. Equalum 

equalum solution

Equalum is a data integration and orchestration tool designed to help organizations automate and streamline their data processing and management workflows. It offers a unified platform for data ingestion, transformation, orchestration, and delivery, allowing businesses to combine data from various sources, format it as needed, and transport it in real-time to target systems.

Best for: Real-time, cloud-native data integrations.

Price: Offers a free trial, and the pricing model is based on per feature. 

Review: “The platform is a powerful data integration and replication tool that combines advanced technologies, such as Kafka and Spark, with CDC (change data capture) capabilities. It provides unique features and performance advantages, making it an ideal solution for data migration and replication.”

2. Talend

Talend solution

Talend is a data integration and data management solution that offers a range of tools for data integration, including ETL, data migration, data quality, and master data management. Some of these tools are available in the cloud and on-premises, allowing organizations to choose the deployment option that best meets their needs.

Best for: Flexible cloud or on-premises deployment options.

Price: A free basic option is available, and Talend data cloud integration will cost 1170 USD per month/user.

Review: “Talend Data Integration helps to collaborate between different services and helps in data ingestion from various sources like Azure, AWS, on Premise, etc.”

3. Hevo Data

Hevo data solution

Hevo Data is a cloud-based data integration platform that enables organizations to collect, process, and analyze data from various sources in real-time. Hevo supports a range of data sources, including databases, cloud applications, and APIs, and allows real-time data ingestion. 

Best for: Strong data security features, such as encryption and user management.

Price: A free version with limited monthly events and basic features, a starter version for $239 per month with more connectors and support, and a custom-priced business version are available.

Review: “Hevo Data is a highly efficient and user-friendly data integration platform designed to meet the modern needs of ETL, ELT, and Reverse ETL. This platform requires no coding and provides bi-directional data pipeline capabilities, making it easy for data teams to manage and automate organizational data flows.”

4. Fivetran

Fivetran solution

Fivetran is a cloud-based data integration tool that enables organizations to seamlessly integrate their data in real-time without requiring manual intervention. It supports many data sources, including databases, cloud apps, and APIs. 

Best for: Zero-maintenance architecture makes sure that integrations are always current.

Price: Offers four models, Starter, Starter Select, Standard, and Enterprise, with custom pricing.

Review: “Fivetran provides pre-built connectors for over 100 data sources, with databases, SaaS applications, cloud storage, and more. I can’t not mention the Automated Data Pipelines. It means Fivetran automatically ingests, transforms, and loads data into the data warehouse, eliminating the need for manual data integration.”

5. StreamSets

Streamsets solution

StreamSets is an open-source data integration platform that provides a range of features for collecting, processing, and delivering data from various sources to various destinations. Developers and data engineers can easily create, manage, and monitor data pipelines with its user-friendly interface, which is meant to be extremely scalable and effective. 

Best for: DataOps with data integration.

Price: A free trial, a professional package for 1000 USD per month, and an enterprise package with custom pricing.

Review: “The platform is optimized for DataOps, improving team collaboration and efficiency. It supports modern analytics and hybrid integration, providing organizations with the ability to leverage their data for better decision-making.”

6. Aggua

Aggua solution

While not strictly a data integration platform, Aggua is a cloud-based data management platform that provides tools for data integration, quality, and governance. It helps organizations connect to and integrate data from various sources, ensure data accuracy and consistency, and manage data lineage and metadata. The platform has a user-friendly interface for working with data and includes collaboration and data-sharing tools.

Best for: Teams with Snowflake or BigQuery at the heart of their architecture.

Price: Contact the vendor for pricing plans.

Review: “It’s a tool for everyone on the data team and even for business analysts that are not part of the data team. I think I would frame it as a tool for everyone that is related to data somehow in the organization.”

7. Oracle GoldenGate


Oracle GoldenGate is a real-time data integration and replication software solution. It provides real-time data integration and replication capabilities for organizations looking to consolidate data from multiple sources into a centralized data repository for analysis and reporting.

Best for: Streaming across multiple databases and platforms.

Price: A free trial and a license model at 250 USD.

Review: “With its ability to efficiently synchronize data between multiple systems, Oracle Golden Gate helps organizations keep their data up-to-date and ensures that critical information is always available when needed.”

8. Brtye

Bryte solution

Bryte is a data integration and management platform that assists businesses in streamlining workflows, automating data management processes, and enhancing the accuracy and quality of their data. Furthermore, it offers a range of data quality tools, including data profiling, data matching, and data standardization, to help organizations improve the accuracy and consistency of their data.

Best for: Automated data integration in real-time.

Price: BryteFlow pricing plans change based on the cloud service provider.

Review: “BryteFlow Ingest’s CDC capability for working with enterprise relational databases (e.g. MS SQL and Oracle) at scale and with operational stability, particularly with very large data volumes.”

9. Matillion

Matillion Solution

Matillion is a cloud-based data transformation and integration platform that enables organizations to extract, transform, and load data from various sources into cloud data warehouses such as Amazon Redshift, Snowflake, and Google BigQuery. In addition, the platform provides a visual interface for building and executing data integration jobs, making it easy for users with little to no coding experience to work with big data.

Best for: Perform ETL tasks for large amounts of data in cloud data warehouses for further analysis and reporting.

Price: Matillion Credits are the form of payment for the usage of Matillion ETL. They cost 2.00 USD per credit. Matillion offers a free version and three pricing plans: Basic, Advanced, and Enterprise, which start at 2.00, 2.20, and 2.30 credits.

Review: “It is optimized for cloud data platforms such as Snowflake, Delta Lake on Databricks, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse, allowing for efficient and productive data management across organizations. With its easy-to-use interface, Matillion makes it simple to make the world’s data useful. “

10. Striim

striim solution

Striim is a real-time data integration and streaming analytics platform for collecting, processing, and analyzing large volumes of data from various sources. It offers a scalable, fault-tolerant, and secure platform for data integration. 

Best for: Streaming analytics.

Price: A free version is available for 10 million monthly events, and there are two plans starting from 2500 USD per month.

Review: “Striim is a real-time data preparation tool that handles data streaming. It is equipped with intelligent modules, automation, and scripts that enable accurate and fast data ingestion.”

Equalum: Streamlining Your Data Journey

Using data integration tools not only improves the efficiency of the data integration process but also enhances the overall data quality and security.

With Equalum’s built-in CDC capabilities, you can further improve the data integration process by continuously accessing real-time data, tracking changes, and applying transformations before ETL. Find more on how Equalum can support your organization by booking a free demo.

Improving Data Streaming, CDC, ETL, and Replication

Managing data streaming workloads, along with other large-scale data management tasks like Change Data Capture (CDC), Extract, Transfer, Load (ETL), and replication can be time and resource intensive activities for an IT organization. As a result, data managers have long sought solutions that can speed up the processes while requiring fewer person-hours for their execution. 3rd Party, online reviewing platform – Peerspot – asked its members to dive into this issue, with users of Equalum discussing how the solution enables them to improve the way they handle data streaming workloads, CDC, and more.

Equalum Use Cases

Equalum is being put to use in a variety of use cases. The end to end platform helps Joel B., a director of enterprise architecture at a pharma/biotech company with more than 10,000 employees, with CDC and data streaming.

A managing director at a small consultancy uses Equalum to take legacy data, siloed data, and information in the enterprise and integrate it into a consolidated database. From there, the data is used for transactions and their BI dashboard.

Shirin K., a database administrator at an energy/utilities company, employs Equalum for replication. She said, “We have databases and [Microsoft] SQL Server. There is some data that needs to go to Oracle for the application team because the application is connected to Oracle Databases, but the back-end application is connected to SQL Server. Then we create workflows where SQL Server is the source, Oracle is the target, and all the tables in SQL Server replicate to Oracle.” In total, she handles 59 workflows for five databases.

A senior software engineer at an online retailer with 500+ employees uses it for “micro-batching of Kafka topics, which is like small, little bits of clickstream data. For almost all our use cases, the target of the data goes into our data warehousing solution, Snowflake. We also take large XML files from multiple parties, transform them, and put them into our Snowflake.”

How Equalum Benefits these Organizations

Equalum users are finding that the solution delivers benefits on multiple fronts. For a software engineer specialist at an energy/utilities company whose organization writes data from one source to target using 5,000 websites, the effect of Equalum has been to improve data accuracy. As he explained, “It tells you where things are not matching. For example, bad dates were coming in and the target database would not accept this format. So Equalum will tell me if there is a problem over there. For error logging and error messaging, it is very efficient. It tells you what the problem is, e.g., your data type is not long enough on the targets. The logging is efficient, very detailed, and will also tell you where the problem is. You can fix the data, transform it, or change the target to accept that type of data. The accuracy is 100 percent. I have not seen any data anomalies.”

“We have been able to create features in some of our analytical environments which probably would have not been possible without Equalum,” said Joel B. He added that “the ability for Equalum to stream data in real time is very powerful. You can stream or you can batch with Equalum. Before using Equalum, our batch processes caused operation reports and our analytics environments to be in slight disagreement. Equalum has allowed us to close this gap. Data in different environments now match.”

The managing director at the small consultancy found the advantage of using Equalum came from its no-code UI, with Kafka and Spark fully managed in the platform engine. For him, it was a matter of human resources. He revealed that “the number of engineers that it takes to both develop something of this nature yourself, and then maintain it, is significant. It’s not easy. Anybody who tries to deal with Kafka as well as Spark, and then tries to use the technologies, is quite blown away by how quick and easy it is to get moving. You can realize ROI much faster with Equalum.”

Shirin K. elected to work with Equalum because some of her applications have stopped supporting Oracle. However, even though some applications migrated to Microsoft SQL Server, others are still linked to Oracle data. These applications still need Oracle. To fix this, her team initially wrote Python scripts to pull data from SQL Server to Oracle. This required a great deal of maintenance and development. Now, since beginning to work with Equalum, she noted that “the data has been flowing very fast.” She also remarked, “Overall, Equalum has resulted in a lot of system performance improvements in our organization. It has helped us out by keeping the source and target synchronized at all times.”

Value Points

Equalum users discussed specific points of value in their reviews on IT Central Station. These included performance monitoring, which stood out to the energy company’s software engineer specialist. As he shared, “If something is failing, you can see it in there at the higher level. E.g., your sources are down, your agent is down, or your flow is not running. All those kinds of things are very visual. You just log into the tool and can see what is happening.” The solution helped his team work faster. He said, “It takes me an hour to transition the solution’s knowledge to somebody else. It is really efficient that way. I haven’t seen any complications.”

“For consultants, it’s a dream tool,” said the managing director at the small consultancy. In his case, what mattered was ease of use. He noted that “it’s super-simple to use. It has its own Excel-type language, so as long as you know how to use Excel, in terms of data transformation, you can use this tool. And we’re talking about being able to do massive data integration and transformation. And that’s not referring to the drag-and-drop capabilities, which are for people who have zero skills. Even for them, it’s that easy.”

To learn more about what IT Central Station members think about Equalum, visit

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