Are you searching for a Big Data ETL tool? Are you confused about which ETL tool fits your requirements? If yes, then this blog will answer all your queries. This blog will take you through Big Data, ETL, and Big Data ETL tools available in the market. You will also learn what each Big Data ETL tool offers at different price ranges.

What is Big Data?

Big data is a term used to describe a large volume of complex data. This data can be in a structured, semi-structured, or unstructured format. It’s almost impossible to process big data using traditional methods as this data grows exponentially.

Traditional methods include a relational database system, but because of the different structures of data, traditional methods failed. Big data helps us to manage different formats of data conveniently.

Some of the use cases of Big data are:

  1. Companies like Facebook ingest 500+ terabytes of data almost every day in an unstructured format.
  2. Companies use Big data to get valuable insights into their data and help them improve their marketing campaigns.
  3. Companies like Jet Airways generate 10+ terabytes of data every day.
  4. The data generated can help you reveal how customers feel about the company or brand. It can help you improve your customer service and product.

What are ETL Tools?

ETL stands for ‘Extract, Transform, and Load’. ETL is the process of moving your data from a source to a data warehouse. This step is one of the most crucial steps in a data practitioners’ data analysis process. ETL tools are applications that let users execute the ETL process. These tools help users move their data from source to destination.

The modern Big Data ETL process includes a large number of scheduled processes for data migration. Coordination and execution of all these activities with a large and complex volume of data makes Big Data ETL tools extremely important. 

Choosing an ETL tool for your use case can be a make-or-break situation. You can consider the following factors while choosing a Big Data ETL tool for yourself:

  1. Overview
  2. Pricing
  3. Use Case
Best Big Data ETL Tools in 2020

Top 9 Big Data ETL Tools

Let’s compare some of the top-notch Big Data ETL tools in the market using the factors stated above.

1. Hevo Data: No-code Data Pipeline

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. You can get started with Hevo’s 14-day Free Trial and instantly move data from 150+ pre-built integrations comprising a wide range of SaaS apps and databases. What’s more – our 24X7 customer support will help you unblock any pipeline issues in real-time.

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With Hevo, fuel your analytics by not just loading data into Warehouse but also enriching it with in-built no-code transformations. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Check out what makes Hevo amazing:

  • Near Real-Time Replication: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depend on API call limits.
  • In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation. 
  • Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
  • Reliability at Scale: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.

Hevo Pricing to bring complete visibility to your ETL spend.


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Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow. Simplify your Data Analysis with Hevo today!

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2. Talend (Talend Open Studio For Data Integration)


Talend is one of the most popular big data and cloud integration software. It is built on Eclipse graphic environment. Talend supports cloud and on-premise databases. It offers a connector to other software as Saas. It offers a smooth workflow and can be adapted easily. You can deploy it on the cloud. 


Talend offers a variety of pricing plans. It costs $12,000 annually or $1170 monthly for the data integration.

Use Case

If you are a company with strict compliance requirements to spread risk across several clouds, then Talend is the right tool. Talend offers data integration with on-premise data warehouses like Amazon Web Services (AWS), Microsoft Azure, SAP, etc.

3. Informatica – PowerCenter


Informatica is an on-premise Big Data ETL tool. It supports data integration with various traditional databases. It is capable of delivering data on-demand, i.e., real-time and data capturing. It is best suited for large organizations. Advanced transformation, dynamic partitioning, data masking are some of the key features of PowerCenter. It is batch-based.


The basic plan starts at $2000 a month. The pricing also depends on data sources, security, etc. Informatica doesn’t offer transparent pricing. Informatica through AWS and Azure offer pay as you go policy.

Use Case

It is best suited for a large organization that requires enterprise-grade security and data governance within their on-premise data. 

Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.

4. IBM Infosphere Information Server


IBM Infosphere Information Server is similar to Informatica. It’s an enterprise product for large organizations. It supports a cloud version that can be hosted on IBM cloud. It works well with mainframe computers. It supports integration with cloud data storage such as AWS S3, Google Storage, etc. With the help of JDBC, you can also integrate it with Redshift. Parallel processing is one of the most important features of Datastage.


IBM Information Server pricing includes Information Server Edition and InfoSphere DataStage. The pricing starts at $19,000 per month. It is considered expensive compared to other ETL tools.

Use Case

It is best suited for large enterprise-grade applications that have on-premise databases. 

5. Pentaho Data Integration 


Pentaho is an open-source ETL. It is also termed as Kettle. It focuses on batch ETL and on-premise use cases. It supports hybrid and multiple cloud-based architectures. It allows data migration, data cleansing, and data loading for a large set of data sources. It offers a drag and drop interface and so, has a minimal learning curve. In the case of ad-hoc analysis, Pentaho is better than Talend as it interprets ETL procedures in XML files.  


Pentaho community is free to use, whereas the Enterprise edition is not transparent.

Use Case

If you want an open-source Big Data ETL in an on-premise ecosystem, then Pentaho is the right choice. The entire Pentaho suite can be deployed on a cloud provider or on-premise.

6. CloverDX


CloverDX is a Java-based ETL tool for rapid automation of data integration. It supports data transformations and data integration with numerous data sources like emails, XML, JSON, etc. It has job scheduling and monitoring. It offers a distributed environment which provides high scalability and availability.


Its pricing starts at $5000 as a one-time payment per user. It offers a free trial for its users.

Use Case

If you are looking for an open-source Big Data ETL tool with real-time data analysis, then CloverDX is the right choice. You can also use it for the deployment of data workloads on a cloud provider or on-premise.   

7. Oracle Data Integrator


Oracle Data Integrator is an ETL tool developed by Oracle. It combines the features of the proprietary engine with an ETL tool. It is fast and requires minimal maintenance. Load plan contains an object for the execution of your Big Data ETL process. You can select your load plan by choosing one or more data sources. It is capable of identifying faulty data and recycles it before it reaches your destination. Some of the supported databases are IBM DB2, Exadata, etc.


Companies can take license ODI on a ‘named user plus’ per processor or basis. It costs $900 per named user plus and $198 for a software update license.

Use Case

It can be used for business intelligence, data migration, big data integration, application integration, etc. If you have big data that needs to be deployed on the cloud, then it is a wise choice. It supports deployment using a bulk load, batch, real-time, cloud, and web services.

8. StreamSets


StreamSets is a DataOps tool. It supports data monitoring and a variety of data sources and destinations for integration. It is a cloud-optimized and real-time ETL tool. Many enterprises use StreamSets to consolidate dozens of data sources for analysis. It also supports data protectors with major data security guidelines like GDPR and HIPAA.


StreamSet’s Standard plan is free, but for the Enterprise Edition, you have to visit streamsets pricing page.

Use Case

StreamSets allows companies to use their on-premise or cloud provider for defining a real-time data pipeline. If you are going to use a large number of Saas offerings, then StreamSet is not recommended. 

9. Matillion


Matillion is built specifically for Amazon Redshift, Azure Synapse, Google BigQuery, and Snowflake. It sits in between your raw data and BI tools. It takes over the compute-intensive activities of data loading from your on-premise. It is highly scalable as it is built to take data warehouse advantages. It can automate your data flows and offers drag and drops browser-based UI, to ease the building of ETL jobs.


It offers four pricing models starting from $1.79 per hour. The Enterprise edition can be customized, and the price will depend on your usage.

Use Case

If you are using Amazon Simple Storage Service (S3), Azure Synapse, BigQuery, or any similar data warehouse, then Matillion is a wise choice. However, it doesn’t support ETL load jobs to most of the data warehouses.  

10. Hadoop


One of the open-source ETL tools for Big data, called Hadoop, is made to manage massive amounts of data. Hadoop has enormous computational power and can store vast volumes of data of all types because of the way it is modeled. Compared to other automated, no-code ETL solutions, Hadoop has a higher learning curve.


Hadoop is one of the free ETL tools for big data.

Use Case

It is beneficial for businesses that need customization or have a lot of data.

11. Microsoft SQL Server Integration Services (SSIS)


One of the ETL tools for big data, SSIS is a platform for data integration and transformation at the corporate level. Connectors are included to enable data extraction from many sources, including relational databases, flat files, and XML files. The graphical user interface of SSIS designers allows practitioners to create data flows and transformations.

The platform reduces the amount of code needed for development by including a library of built-in transforms. 


varies from $15,123 for the enterprise edition to free for developers.

Use Case

SSIS provides thorough instructions for creating unique processes. The platform’s complexity and high learning curve may deter beginners from building ETL pipelines fast.

12. Qlik


Qlik Compose is one of the best ETL tools for big data which provides a different method for the ELT procedure. The technology duplicates the data and transmits it to data warehouses almost instantly via change data capture. The company’s website claims that automating the creation of the ETL code required to convert the data and the design of data warehouses it significantly shortens the time required for the data integration process.


Qlik prices will be available upon request

Use Case

It is possible to verify and guarantee the quality of the data with Qlik Compose. Integrating Compose with Qlik Replicate is another option for practitioners who want data in real-time.

Before wrapping up, let’s cover some FAQs on big data ETL.

FAQs on Big Data ETL

How is ETL different from ELT?

ETL is the process where transformation of data happens after the data is extracted and before the data is loaded to a destination. ELT is the process where transformation of raw data happens after the data is loaded.

2. What are the types of ETL tools?

ETL tools can be classified into 4 major categories based on different parameters–Enterprise ETL tool, Custom ETL tool, Open-source ETL tool and Cloud-based ETL tool.

3. What are some of the benefits of a cloud ETL tool?

Cloud ETL tools are scalable, flexible, and cost-effective, among many of the other things. They scale dynamically depending on your organization’s growing volume of data.


In this blog, you have learned about various Big data ETL tools based on various factors. You can choose your Big Data ETL tool according to your requirements. If you want an open-source Big Data ETL, the CloverDX and Talend can be a wise choices. But, if you are looking for a real-time data pipeline, then try Hevo.

In case you want to integrate data into your desired Database/destination, then Hevo Data is the right choice for you!

It will help simplify the ETL and management process of both the data sources and the data destinations.

Visit our Website to Explore Hevo

Give Hevo a try by signing up for a 14-day free trial today.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Share your experience of using Big Data ETL tools in the comment section below.

Oshi Varma
Freelance Technical Content Writer, Hevo Data

Driven by a problem-solving ethos and guided by analytical thinking, Oshi is a freelance writer who delves into the intricacies of data integration and analysis. He offers meticulously researched content essential for solving problems of businesses in the data industry.

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