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Apache Sqoop (discontinued) vs. HPE Data Fabric

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Apache Sqoop (discontinued)

    Score8.8 out of 10
    N/AApache Sqoop is a discontinued open-source command-line tool for bulk data transfer between Apache Hadoop and structured data stores. It was commonly used to import relational database tables or mainframe datasets into HDFS for processing with Hadoop tools, then export processed data back to a relational database.N/A

    HPE Data Fabric

    Score9.4 out of 10
    N/AHPE Data Fabric (formerly MapR, acquired by HPE in 2019) is a software-defined datastore and file system that simplifies data management and analytics by unifying data across core, edge, and multicloud sources into a single platform.N/A
    Pricing
    Apache Sqoop (discontinued)HPE Data Fabric
    Editions & Modules
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    Offerings
    Pricing Offerings
    Apache Sqoop (discontinued)HPE Data Fabric
    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 Sqoop (discontinued)HPE Data Fabric
    Small Businesses
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    Medium-sized Companies
    Apache Spark
    Score8.8 out of 10
    HBase
    Score7.3 out of 10
    Enterprises
    Apache Spark
    Score8.8 out of 10
    Cassandra
    Score9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Apache Sqoop (discontinued)HPE Data Fabric
    Likelihood to Recommend
    9.0
    (1 ratings)
    7.2
    (4 ratings)
    User Testimonials
    Apache Sqoop (discontinued)HPE Data Fabric
    Likelihood to Recommend
    Apache
    Sqoop is great for sending data between a JDBC compliant database and a Hadoop environment. Sqoop is built for those who need a few simple CLI options to import a selection of database tables into Hadoop, do large dataset analysis that could not commonly be done with that database system due to resource constraints, then export the results back into that database (or another). Sqoop falls short when there needs to be some extra, customized processing between database extract, and Hadoop loading, in which case Apache Spark's JDBC utilities might be preferred
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    Hewlett Packard Enterprise (HPE)
    MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.
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    Pros
    Apache
    • Provides generalized JDBC extensions to migrate data between most database systems
    • Generates Java classes upon reading database records for use in other code utilizing Hadoop's client libraries
    • Allows for both import and export features
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    Hewlett Packard Enterprise (HPE)
    • MapR had very fast I/O throughput. The write speed was several times faster than what we could achieve with the other Hadoop vendors (Cloudera and Hortonworks). This is because MapR does not use HDFS, which is essentially a "meta filesystem". HDFS is built on top of the filesystem provided by the OS. MapR has their filesystem called MapR-FS, which is a true filesystem and accesses the raw disk drives.
    • The MapR filesystem is very easy to integrate with other Linux filesystems. When working with HDFS from Apache Hadoop, you usually have to use either the HDFS API or various Hadoop/HDFS command line utilities to interact with HDFS. You cannot use command line utilities native to the host operation system, which is usually Linux. At least, it is not easily done without setting up NFS, gateways, etc. With MapR-FS, you can mount the filesystem within Linux and use the standard Unix commands to manipulate files.
    • The HBase distribution provided by MapR is very similar to the Apache HBase distribution. Cloudera and Hortonworks add GUIs and other various tools on top of their HBase distributions. The MapR HBase distribution is very similar to the Apache distribution, which is nice if you are more accustomed to using Apache HBase.
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    Cons
    Apache
    • Sqoop2 development seems to have stalled. I have set it up outside of a Cloudera CDH installation, and I actually prefer it's "Sqoop Server" model better than just the CLI client version that is Sqoop1. This works especially well in a microservices environment, where there would be only one place to maintain the JDBC drivers to use for Sqoop.
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    Hewlett Packard Enterprise (HPE)
    • It takes time to get latest versions of Apache ecosystem tools released as it has to be adapted.
    • When you have issues related to Mapr-FS or Mapr Tables, its hard to figure them out by ourselves.
    • Sometime new ecosystem tools versions are released without proper QA.
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    Alternatives Considered
    Apache
    • Sqoop comes preinstalled on the major Hadoop vendor distributions as the recommended product to import data from relational databases. The ability to extend it with additional JDBC drivers makes it very flexible for the environment it is installed within.
    • Spark also has a useful JDBC reader, and can manipulate data in more ways than Sqoop, and also upload to many other systems than just Hadoop.
    • Kafka Connect JDBC is more for streaming database updates using tools such as Oracle GoldenGate or Debezium.
    • Streamsets and Apache NiFi both provide a more "flow based programming" approach to graphically laying out connectors between various systems, including JDBC and Hadoop.
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    Hewlett Packard Enterprise (HPE)
    I don't believe there is as much support for MapR yet compared to other more widely known products.
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    Return on Investment
    Apache
    • When combined with Cloudera's HUE, it can enable non-technical users to easily import relational data into Hadoop.
    • Being able to manipulate large datasets in Hadoop, and them load them into a type of "materialized view" in an external database system has yielded great insights into the Hadoop datalake without continuously running large batch jobs.
    • Sqoop isn't very user-friendly for those uncomfortable with a CLI.
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    Hewlett Packard Enterprise (HPE)
    • Increased employee efficiency for sure. Our clients have various levels of expertise in their deployment and user teams, and we never receive complaints about MapR.
    • MapR is used by one of our financial services clients who uses it for fraud detection and user pattern analysis. They are able to turn around data much faster than they previously had with in-house applications
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