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Pentaho Data Integration VS Apache Airflow

Compare Pentaho Data Integration VS Apache Airflow and see what are their differences

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Pentaho Data Integration logo Pentaho Data Integration

Hitachi Vantara brings Pentaho Data Integration, an end-to-end platform for all data integration challenges, that simplifies creation of data pipelines and provides big data processing.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Pentaho Data Integration Landing page
    Landing page //
    2023-05-08
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Pentaho Data Integration features and specs

  • User-Friendly Interface
    Pentaho Data Integration offers an intuitive drag-and-drop interface that simplifies the ETL process, making it accessible even for users without extensive technical expertise.
  • Extensive Connectivity
    Pentaho supports a wide range of data sources, including relational databases, NoSQL databases, cloud services, and big data platforms, providing flexibility for integration needs.
  • Scalability
    The platform can handle large volumes of data, making it suitable for enterprise-level data integration tasks and supporting growth in data needs over time.
  • Open-Source Community
    As an open-source tool, Pentaho benefits from a large and active community that contributes to its continuous improvement and provides a wealth of shared resources and plugins.
  • Integration with BI Tools
    Pentaho Data Integration seamlessly integrates with Pentaho's business intelligence tools, allowing for streamlined workflow from data ingestion to analytics and reporting.

Possible disadvantages of Pentaho Data Integration

  • Learning Curve
    While the interface is user-friendly, mastering the full capabilities of Pentaho can take time, especially for users new to ETL processes and data integration.
  • Performance Issues
    Some users report performance bottlenecks, especially when dealing with very large datasets or complex transformations, which may require additional optimization.
  • Limited Advanced Features
    Compared to some commercial ETL tools, Pentaho might lack certain advanced features, requiring additional customization or third-party solutions to fulfill complex requirements.
  • Documentation Quality
    The quality and depth of official documentation can sometimes be lacking, leading users to rely on community forums and external sources for troubleshooting.
  • Enterprise Edition Costs
    While the community edition of Pentaho is free, accessing the full suite of enterprise features and support requires a commercial license, which may be costly for some organizations.

Apache Airflow features and specs

  • Scalability
    Apache Airflow can scale horizontally, allowing it to handle large volumes of tasks and workflows by distributing the workload across multiple worker nodes.
  • Extensibility
    It supports custom plugins and operators, making it highly customizable to fit various use cases. Users can define their own tasks, sensors, and hooks.
  • Visualization
    Airflow provides an intuitive web interface for monitoring and managing workflows. The interface allows users to visualize DAGs, track task statuses, and debug failures.
  • Flexibility
    Workflows are defined using Python code, which offers a high degree of flexibility and programmatic control over the tasks and their dependencies.
  • Integrations
    Airflow has built-in integrations with a wide range of tools and services such as AWS, Google Cloud, and Apache Hadoop, making it easier to connect to external systems.

Possible disadvantages of Apache Airflow

  • Complexity
    Setting up and configuring Apache Airflow can be complex, particularly for new users. It requires careful management of infrastructure components like databases and web servers.
  • Resource Intensive
    Airflow can be resource-heavy in terms of both memory and CPU usage, especially when dealing with a large number of tasks and DAGs.
  • Learning Curve
    The learning curve can be steep for users who are not familiar with Python or the underlying concepts of workflow management.
  • Limited Real-Time Processing
    Airflow is better suited for batch processing and scheduled tasks rather than real-time event-based processing.
  • Dependency Management
    Managing task dependencies in complex DAGs can become cumbersome and may lead to configuration errors if not properly handled.

Analysis of Apache Airflow

Overall verdict

  • Yes, Apache Airflow is a good choice for managing complex workflows and data pipelines, particularly for organizations that require a scalable and reliable orchestration tool.

Why this product is good

  • Apache Airflow is considered good because it provides a robust and flexible platform for authoring, scheduling, and monitoring workflows. It is open-source and has a large community that contributes to its continuous improvement. Airflow's modular architecture allows for easy integration with various data sources and destinations, and its UI is user-friendly, enabling effective pipeline visualization and management. Additionally, it offers extensibility through a wide array of plugins and customization options.

Recommended for

    Apache Airflow is recommended for data engineers, data scientists, and IT professionals who need to automate and manage workflows. It is particularly suited for organizations handling large-scale data processing tasks, requiring integration with various systems, and those looking to deploy machine learning pipelines or ETL processes.

Pentaho Data Integration videos

+ Add

pentaho Data Integration review

Apache Airflow videos

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Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Pentaho Data Integration and Apache Airflow)

Pentaho Data Integration

Apache Airflow

Backup & Sync
100 100%
Backup & Sync
0% 0
Workflow Automation
0 0%
Workflow Automation
100% 100
Data Integration
100 100%
Data Integration
0% 0
Automation
0 0%
Automation
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Pentaho Data Integration and Apache Airflow

Pentaho Data Integration Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
In conclusion, there are many different ETL and data integration tools available, each with its own unique features and capabilities. Some popular options include SSIS, Talend Open Studio, Pentaho Data Integration, Hadoop, Airflow, AWS Data Pipeline, Google Dataflow, SAP BusinessObjects Data Services, and Hevo. Companies considering these tools should carefully evaluate...
Source: www.datacamp.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Pentaho Data Integration enables the user to cleanse and prepare the data from various sources and allows the migration of data between applications. PDI is an open-source tool and is a part of the Pentaho business intelligent suite.
Source: www.softwaretestinghelp.com

Apache Airflow Reviews

5 Airflow Alternatives for Data Orchestration
While Apache Airflow continues to be a popular tool for data orchestration, the alternatives presented here offer a range of features and benefits that may better suit certain projects or team preferences. Whether you prioritize simplicity, code-centric design, or the integration of machine learning workflows, there is likely an alternative that meets your needs. By...
Source: www.kdnuggets.com
Top 8 Apache Airflow Alternatives in 2024
Apache Airflow is a workflow streamlining solution aiming at accelerating routine procedures. This article provides a detailed description of Apache Airflow as one of the most popular automation solutions. It also presents and compares alternatives to Airflow, their characteristic features, and recommended application areas. Based on that, each business could decide which...
Source: blog.skyvia.com
10 Best Airflow Alternatives for 2024
In a nutshell, you gained a basic understanding of Apache Airflow and its powerful features. On the other hand, you understood some of the limitations and disadvantages of Apache Airflow. Hence, this article helped you explore the best Apache Airflow Alternatives available in the market. So, you can try hands-on on these Airflow Alternatives and select the best according to...
Source: hevodata.com
A List of The 16 Best ETL Tools And Why To Choose Them
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
Source: www.datacamp.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Apache Airflow programmatically creates, schedules and monitors workflows. It can also modify the scheduler to run the jobs as and when required.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Based on our record, Apache Airflow seems to be more popular. It has been mentiond 79 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Pentaho Data Integration mentions (0)

We have not tracked any mentions of Pentaho Data Integration yet. Tracking of Pentaho Data Integration recommendations started around Mar 2021.

Apache Airflow mentions (79)

  • dgsh – Directed Graph Shell
    There is a lot of stuff for Python which follows the "express computation as a dag" approach, especially Apache Airflow https://airflow.apache.org/. - Source: Hacker News / 4 days ago
  • Unable to emit metadata to DataHub GMS with Airflow - a solution
    Doing ingestion or data processing with Airflow, a very popular open-source platform for developing and running workflows, is a fairly common setup. DataHub's automatic lineage extraction works great with Airflow - provided you configure the Airflow connection to DataHub correctly. - Source: dev.to / about 2 months ago
  • Top ETL Tools for MongoDB in 2025: Which One Fits Your Use Case?
    Apache Airflow represents the open-source workflow orchestration approach to MongoDB ETL. By combining Airflow's powerful scheduling and dependency management with a Python library like PyMongo, you can build highly customized ETL workflows that integrate seamlessly with MongoDB. - Source: dev.to / 2 months ago
  • Building Effective AI Agents \ Anthropic
    You appear to be making the mistake of assuming that the only valid definition for the term "workflow" is the definition used by software such as https://airflow.apache.org/ https://www.merriam-webster.com/dictionary/workflow thinks the word dates back to 1921. There no reason Anthropic can't take that word and present their own alternative definition for it in the context of LLM tool usage, which is what they've... - Source: Hacker News / 4 months ago
  • The DOJ Still Wants Google to Sell Off Chrome
    Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 7 months ago
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What are some alternatives?

When comparing Pentaho Data Integration and Apache Airflow, you can also consider the following products

SAP Data Services - SAP Data Services provides functionality for data integration, quality, cleansing, and more.

Make.com - Tool for workflow automation (Former Integromat)

Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

Oracle Data Integrator - Oracle Data Integrator is a data integration platform that covers batch loads, to trickle-feed integration processes.

Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.

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