Apache Sqoop vs. HPE Ezmeral Data Fabric (MapR)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Sqoop
Score 8.8 out of 10
N/A
Apache Sqoop is a tool for use with Hadoop, used to transfer data between Apache Hadoop and other, structured data stores.N/A
HPE Ezmeral Data Fabric (MapR)
Score 9.4 out of 10
N/A
HPE Ezmeral 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. Just as a loom weaves multiple threads into a single piece of fabric, HPE Ezmeral Data Fabric weaves distributed data into a single enterprise-wide data layer that ingests, processes, and stores data once and then makes it available for reuse across multiple…N/A
Pricing
Apache SqoopHPE Ezmeral Data Fabric (MapR)
Editions & Modules
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Offerings
Pricing Offerings
Apache SqoopHPE Ezmeral Data Fabric (MapR)
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
Community Pulse
Apache SqoopHPE Ezmeral Data Fabric (MapR)
Top Pros
Top Cons
Best Alternatives
Apache SqoopHPE Ezmeral Data Fabric (MapR)
Small Businesses

No answers on this topic

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Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SqoopHPE Ezmeral Data Fabric (MapR)
Likelihood to Recommend
9.0
(1 ratings)
7.2
(4 ratings)
User Testimonials
Apache SqoopHPE Ezmeral Data Fabric (MapR)
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
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
  • 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
  • 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
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
  • 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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