Apache Pulsar vs Cloudera DataFlow comparison

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Pulsar Logo
1,593 views|1,089 comparisons
100% willing to recommend
Cloudera Logo
1,945 views|1,019 comparisons
66% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Apache Pulsar and Cloudera DataFlow based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics.
To learn more, read our detailed Streaming Analytics Report (Updated: April 2024).
768,857 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 solution operates as a classic message broker but also as a streaming platform."

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"DataFlow's performance is okay.""This solution is very scalable and robust.""The initial setup was not so difficult"

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Cons
"Documentation is poor because much of it is in Chinese with no English translation."

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"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists.""It is not easy to use the R language.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."

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Pricing and Cost Advice
Information Not Available
  • "DataFlow isn't expensive, but its value for money isn't great."
  • More Cloudera DataFlow Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The solution operates as a classic message broker but also as a streaming platform.
    Top Answer:The solution is open-source freeware.
    Top Answer:Documentation is poor because much of it is in Chinese with no English translation.
    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.
    Ranking
    12th
    out of 38 in Streaming Analytics
    Views
    1,593
    Comparisons
    1,089
    Reviews
    1
    Average Words per Review
    470
    Rating
    8.0
    13th
    out of 38 in Streaming Analytics
    Views
    1,945
    Comparisons
    1,019
    Reviews
    3
    Average Words per Review
    288
    Rating
    6.7
    Comparisons
    Also Known As
    CDF, Hortonworks DataFlow, HDF
    Learn More
    Pulsar
    Video Not Available
    Overview

    Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project

    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.

    Sample Customers
    Information Not Available
    Clearsense
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company24%
    Financial Services Firm11%
    Manufacturing Company8%
    Government7%
    VISITORS READING REVIEWS
    Computer Software Company19%
    Financial Services Firm14%
    University8%
    Government7%
    Company Size
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise17%
    Large Enterprise61%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise10%
    Large Enterprise74%
    Buyer's Guide
    Streaming Analytics
    April 2024
    Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics. Updated: April 2024.
    768,857 professionals have used our research since 2012.

    Apache Pulsar is ranked 12th in Streaming Analytics with 1 review while Cloudera DataFlow is ranked 13th in Streaming Analytics with 3 reviews. Apache Pulsar is rated 8.0, while Cloudera DataFlow is rated 6.6. The top reviewer of Apache Pulsar writes "The solution can mimic other APIs without changing a line of code". On the other hand, the top reviewer of Cloudera DataFlow writes "A scalable and robust platform for analyzing data". Apache Pulsar is most compared with Apache Flink, Apache Spark Streaming, Amazon Kinesis, Amazon MSK and Azure Stream Analytics, whereas Cloudera DataFlow is most compared with Databricks, Confluent, Amazon MSK, Spring Cloud Data Flow and Informatica Data Engineering Streaming.

    See our list of best Streaming Analytics vendors.

    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.