Azure Data Factory vs Rivery comparison

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24,893 views|19,539 comparisons
91% willing to recommend
Rivery Logo
329 views|226 comparisons
100% willing to recommend
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Executive Summary

We performed a comparison between Azure Data Factory and Rivery based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration.
To learn more, read our detailed Data Integration Report (Updated: May 2024).
772,679 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The most valuable feature is the copy activity.""One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams.""The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain.""The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.""From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature.""Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure.""The data copy template is a valuable feature.""The most valuable feature of this solution would be ease of use."

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"Connects to many APIs in the market and new ones are being added all the time."

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Cons
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on.""They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.""Lacks in-built streaming data processing.""For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better.""There's space for improvement in the development process of the data pipelines.""Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory.""The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way.""The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."

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"Lineage and an impact analysis or logic dependency are lacking."

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Pricing and Cost Advice
  • "In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
  • "This is a cost-effective solution."
  • "The price you pay is determined by how much you use it."
  • "Understanding the pricing model for Data Factory is quite complex."
  • "I would not say that this product is overly expensive."
  • "The licensing is a pay-as-you-go model, where you pay for what you consume."
  • "Our licensing fees are approximately 15,000 ($150 USD) per month."
  • "The licensing cost is included in the Synapse."
  • More Azure Data Factory Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:AWS Glue and Azure Data factory for ELT best performance cloud services.
    Top Answer:Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up and… more »
    Top Answer:Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power… more »
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    Overview

    Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

    Rivery is a serverless, SaaS DataOps platform that empowers companies of all sizes around the world to consolidate, orchestrate, and manage internal and external data sources with ease and efficiency.

    By offering comprehensive data solutions and partnering with complementary technology providers, including Google, Snowflake, Tableau, and Looker, Rivery enables data-driven companies to build the perfect ecosystems for all their data processes.

    Sample Customers
    1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
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    Top Industries
    REVIEWERS
    Computer Software Company34%
    Insurance Company11%
    Manufacturing Company8%
    Financial Services Firm8%
    VISITORS READING REVIEWS
    Computer Software Company13%
    Financial Services Firm13%
    Manufacturing Company8%
    Healthcare Company7%
    VISITORS READING REVIEWS
    Financial Services Firm16%
    Computer Software Company14%
    University9%
    Recreational Facilities/Services Company8%
    Company Size
    REVIEWERS
    Small Business29%
    Midsize Enterprise19%
    Large Enterprise52%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise13%
    Large Enterprise69%
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise27%
    Large Enterprise52%
    Buyer's Guide
    Data Integration
    May 2024
    Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration. Updated: May 2024.
    772,679 professionals have used our research since 2012.

    Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Rivery is ranked 58th in Data Integration. Azure Data Factory is rated 8.0, while Rivery is rated 9.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Rivery writes "Great logic and the ability to call outside API if needed. Key feature is management of different sources". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas Rivery is most compared with Alteryx Designer and AWS Glue.

    See our list of best Data Integration vendors.

    We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.