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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

    SAP Vora

    Score6 out of 10
    N/ASAP Vora is a computing engine designed to provide better accessibility to Hadoop data from SAP HANA. SAP Vora manages unstructured Hadoop data by building structured data hierarchies and making the data queryable through an SQL interface.N/A
    Pricing
    Apache Sqoop (discontinued)SAP Vora
    Editions & Modules
    No answers on this topic
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    Offerings
    Pricing Offerings
    Apache Sqoop (discontinued)SAP Vora
    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)SAP Vora
    Small Businesses
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    Medium-sized Companies
    Apache Spark
    Score8.9 out of 10
    Cloudera Manager (no longer available standalone)
    Score9.9 out of 10
    Enterprises
    Apache Spark
    Score8.9 out of 10
    Hadoop
    Score7.5 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Apache Sqoop (discontinued)SAP Vora
    Likelihood to Recommend
    9.0
    (1 ratings)
    6.0
    (1 ratings)
    User Testimonials
    Apache Sqoop (discontinued)SAP Vora
    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
    Incentivized
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    SAP
    I spent more than 1 year with SAP Vora, SAP Datahub and SAP Leonardo with ML, iOt. I believe this product has potential but it is not easy to adopt. SAP has to keep in mind how open-source big data technologies are able to deliver quick results. I know SAP is stabilizing and fighting hard against many open source technologies, but it still has a long way to go there.
    Incentivized
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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
    Incentivized
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    SAP
    • Modelling with SAP HANA and Hadoop
    • Realtime Analysis using Vora and HANA as a Streaming engine
    • Time series Analysis on large chunks of datasets
    • Machine learning capabilities on Hadoop tables and spark contexts
    Incentivized
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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.
    Incentivized
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    SAP
    • Vora 2.0 in on premise scenarios could be improved, as adoption of the cloud is not an easy sell.
    • Kubernetes and Docker integration need to be more seamless and quick to understand. If this is simplified, it will be easy to adopt
    • Data hub orchestration and integrations could be simplified so that quick adoption within SAP BW, ECC, S4 HANa scenarios is possible.
    Incentivized
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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.
    Incentivized
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    SAP
    No answers on this topic
    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.
    Incentivized
    Read full review
    SAP
    • Negative impact would be Poc and RFI will need more time to adopt and decision making gets delayed
    • Positive impact would be it's a great leap from SAP to adopt a Big data technologies and AI within cloud stream. But selling is going to take time.
    Incentivized
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