Cloudera DataFlow vs Spring Cloud Data Flow comparison

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Executive Summary

We performed a comparison between Cloudera DataFlow and Spring Cloud Data Flow based on real PeerSpot user reviews.

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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"DataFlow's performance is okay.""The initial setup was not so difficult""This solution is very scalable and robust."

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"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like.""There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure.""The product is very user-friendly.""The most valuable feature is real-time streaming."

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Cons
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning.""Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages.""It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."

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"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required.""Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications.""Some of the features, like the monitoring tools, are not very mature and are still evolving.""The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

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Pricing and Cost Advice
  • "DataFlow isn't expensive, but its value for money isn't great."
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  • "This is an open-source product that can be used free of charge."
  • "If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."
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    Questions from the Community
    Top Answer:The initial setup was not so difficult
    Top Answer:It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning. This feature could be improved.
    Top Answer:Sometimes I need this workflow to make my modules, not for campaign preparation. It is solely focused on developing quality modules for direct telecommunication companies.
    Top Answer:Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD… more »
    Top Answer:Spring Cloud Data Flow is used for asynchronous workloads. We are working on streams. For example, a workload is generated at a particular point, and at the source, it gets passed down through a… more »
    Top Answer:The solution requires little maintenance. My advice to others is for them to follow the documentation. The solution is very well-designed and they deliver on their promises. I rate Spring Cloud Data… more »
    Ranking
    13th
    out of 38 in Streaming Analytics
    Views
    1,988
    Comparisons
    1,068
    Reviews
    3
    Average Words per Review
    288
    Rating
    6.7
    10th
    out of 38 in Streaming Analytics
    Views
    3,998
    Comparisons
    3,018
    Reviews
    1
    Average Words per Review
    610
    Rating
    7.0
    Comparisons
    Also Known As
    CDF, Hortonworks DataFlow, HDF
    Learn More
    Overview

    Cloudera DataFlow (CDF) is a comprehensive edge-to-cloud real-time streaming data platform that gathers, curates, and analyzes data to provide customers with useful insight for immediately actionable intelligence. It resolves issues with real-time stream processing, streaming analytics, data provenance, and data ingestion from IoT devices and other sources that are associated with data in motion. Cloudera DataFlow enables secure and controlled data intake, data transformation, and content routing because it is built entirely on open-source technologies. With regard to all of your strategic digital projects, Cloudera DataFlow enables you to provide a superior customer experience, increase operational effectiveness, and maintain a competitive edge.

    With Cloudera DataFlow, you can take the next step in modernizing your data streams by connecting your on-premises flow management, streams messaging, and stream processing and analytics capabilities to the public cloud.

    Cloudera DataFlow Advantage Features

    Cloudera DataFlow has many valuable key features. Some of the most useful ones include:

    • Edge and flow management: Edge agents and an edge management hub work together to provide the edge management capability. Edge agents can be managed, controlled, and watched over in order to gather information from edge hardware and push intelligence back to the edge. Thousands of edge devices can now be used to design, deploy, run, and monitor edge flow apps. Edge Flow Manager (EFM) is an agent management hub that enables the development, deployment, and monitoring of edge flows on thousands of MiNiFi agents using a graphical flow-based programming model.
    • Streams messaging: The CDF platform guarantees that all ingested data streams can be temporarily buffered so that other applications can use the data as needed. This makes it possible for a business to scale efficiently, as data streams from thousands of origination points start to grow to petabyte sizes. To achieve IoT-scale, streams messaging allows you to buffer large data streams using a publish-subscribe strategy.
    • Stream analytics and processing: The third tenet of the CDF platform is its capacity to analyze incoming data streams in real time and with minimal latency, providing actionable intelligence in the form of predictive and prescriptive insights. This stage is essential to completing the Data-in-Motion lifecycle for an enterprise because there is only a use in absorbing all real-time streams if something useful is done with them in the moment to benefit your company.
    • Shared Data Experience (SDX): The most crucial component that transforms CDF into a genuine platform is Cloudera Data Platform's SDX. It is a powerful data fabric that offers the broadest possible deployment flexibility and guarantees total security, governance, and control across infrastructures. You get a single experience for security (with Apache Ranger), governance (with Apache Atlas), and data lineage from edge to cloud because all the CDF components seamlessly connect with SDX.

    Cloudera DataFlow Advantage Benefits

    There are many benefits to implementing Cloudera DataFlow . Some of the biggest advantages the solution offers include:

    • Completely open source: Invest in your architecture with confidence, knowing that there will be no vendor lock-in.
    • More than 300 pre-built processors: This is the only product that provides edge-to-cloud connection this comprehensive as well as a no-code user experience
    • Integrated data provenance: The market's only platform that offers out-of-the-box, end-to-end data lineage tracking and provenance across MiNiFi, NiFi, Kafka, Flink, and more.
    • Multiple stream processing engines to choose from: Supports Spark structured streaming, Kafka Streams, and Apache Flink for real-time insights and predictive analytics.
    • Hundred of Kafka consumers: Cloudera has hundreds of satisfied customers who receive exceptional support for their complex Kafka implementations.
    • Use cases for edge IoT: IoT data from thousands of endpoints may be easily collected, processed, and managed from the edge to the cloud with a multi-cloud/hybrid cloud strategy.
    • Hybrid/multi-cloud approach: Choose a flexible deployment option for your streaming architecture that spans across edge, on-premises, and various cloud environments with ease thanks to the power of CDP.

    Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
    Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

    Sample Customers
    Clearsense
    Information Not Available
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company18%
    Financial Services Firm14%
    University8%
    Educational Organization7%
    VISITORS READING REVIEWS
    Financial Services Firm29%
    Computer Software Company16%
    Manufacturing Company7%
    Retailer7%
    Company Size
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise11%
    Large Enterprise74%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise9%
    Large Enterprise77%
    Buyer's Guide
    Streaming Analytics
    March 2024
    Find out what your peers are saying about Databricks, Amazon, Confluent and others in Streaming Analytics. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Cloudera DataFlow is ranked 13th in Streaming Analytics with 3 reviews while Spring Cloud Data Flow is ranked 10th in Streaming Analytics with 5 reviews. Cloudera DataFlow is rated 6.6, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Cloudera DataFlow writes "A scalable and robust platform for analyzing data". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Good logging mechanisms, a strong infrastructure and pretty scalable". Cloudera DataFlow is most compared with Databricks, Confluent, Amazon MSK, Hortonworks Data Platform and Informatica Data Engineering Streaming, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and Mule Anypoint Platform.

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    We monitor all Streaming Analytics 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.