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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Apache Flume

    Score7.1 out of 10
    N/AApache Flume is a product enabling the flow of logs and other data into a Hadoop environment.N/A

    IBM Analytics Engine

    Score7.1 out of 10
    N/AIBM BigInsights is an analytics and data visualization tool leveraging hadoop.N/A
    Pricing
    Apache FlumeIBM Analytics Engine
    Editions & Modules
    No answers on this topic
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    Offerings
    Pricing Offerings
    Apache FlumeIBM Analytics Engine
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Best Alternatives
    Apache FlumeIBM Analytics Engine
    Small Businesses
    No answers on this topic
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    Medium-sized Companies
    Cloudera Manager
    Score9.9 out of 10
    Cloudera Manager
    Score9.9 out of 10
    Enterprises
    Apache Spark
    Score8.9 out of 10
    Apache Spark
    Score8.9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Apache FlumeIBM Analytics Engine
    Likelihood to Recommend
    8.0
    (2 ratings)
    9.5
    (9 ratings)
    Support Rating
    5.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    Apache FlumeIBM Analytics Engine
    Likelihood to Recommend
    Apache
    Apache Flume is well suited when the use case is log data ingestion and aggregate only, for example for compliance of configuration management. It is not well suited where you need a general-purpose real-time data ingestion pipeline that can receive log data and other forms of data streams (eg IoT, messages).
    Incentivized
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    IBM
    • Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
    • But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.
    Incentivized
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    Pros
    Apache
    • Multiple sources of data (sources) and destinations (sinks) that allows you to move data form and to any relevant data storage
    • It is very easy to setup and run
    • Very open to personalization, you can create filters, enrichment, new sources and destinations
    Incentivized
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    IBM
    • Jobs with Spark, Hadoop, or Hive queries are rapidly attained
    • Can collect, organize and analyze your data accurately
    • You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.
    Incentivized
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    Cons
    Apache
    • It is very specific for log data ingestion so it is pretty hard to use for anything else besides log data
    • Data replication is not built in and needs to be added on top of Apache Flume (not a hard job to do though)
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    IBM
    • Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
    • Bundling of the Cloud Object Storage should be included with the Analytics Engine.
    • The inability to add your own Hadoop stack components has made some transfers a little more complex.
    Incentivized
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    Support Rating
    Apache
    Apache Flume is open-source so support is limited. Never the less, it has great documentation and best practices documents from their end-users so it is not hard to use, setup and configure.
    Incentivized
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    IBM
    No answers on this topic
    Alternatives Considered
    Apache
    Apache Flume is a very good solution when your project is not very complex at transformation and enrichment, and good if you have an external management suite like Cloudera, Hortonworks, etc. But it is not a real EAI or ETL like AB Initio or Attunity so
    you need to know exactly what you want. On the other hand being an opensource project give Apache a lot of room to personalize thanks to its plug-able architecture and has a very nice performance having a very low CPU and Memory footprint, a single server can do the job on many occasions, as opposed to the multi-server architecture of paid products.
    Incentivized
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    IBM
    We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
    Incentivized
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    Return on Investment
    Apache
    • Flume has simplified a lot many of our ingest procedures, easier to deploy and integrate than a classical EAI, reducing the time to market
    • But opposed to EAIs if the project starts to grow in complexity Apache Flume project may not be as suitable
    Incentivized
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    IBM
    • This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
    • IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
    • The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.
    Incentivized
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