What users are saying about

Apache Flume

5 Ratings

Apache Sqoop

4 Ratings

Apache Flume

5 Ratings
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Score 7.9 out of 101

Apache Sqoop

4 Ratings
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Score 8.7 out of 101

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Likelihood to Recommend

Apache Flume

Apache Flume is well suited in small batch and near real time processing projects, taking data from one point to another with local processing (I mean not external enrichment).
Filtering, transforming and multiple push destinations are common grounds for Flume.
It is not so nice to use if your data needs external enrichment (taking data from external databases or web services), as transactions and (micro)batches may lead to reprocessing and it relies upon the application to avoid duplicates.
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Apache Sqoop

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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Pros

  • 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
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  • 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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Cons

  • Apache Flume develops new functionality at a slower pace than other OpenSource projects, it is well behing Kafka and has some compatibiliy issues with latest releases
  • It lack HA or FT, it relies on third party management software like Hortonworks or Cloudera
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  • 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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Alternatives Considered

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.
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  • 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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Return on Investment

  • 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
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  • 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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Pricing Details

Apache Flume

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Apache Sqoop

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details