Apache Sqoop (discontinued) vs. IBM Confluent

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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Very reliable product and help with streaming messages without fail which is a significant confidence booster as an SRE leader. Would recommend the product considering the fact that now it is backed by IBM in addition to all IBM Confluent was doing well before the acquisition. Has been a game changer for our organization

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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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• It works really well in providing data context for our customers.
• It is helpful in real time data streaming.
• It is really wonderful tool for doing data validations and massaging the data.

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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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• Iit needs to improve in AI agents usage.
• It needs to be more user friendly.
• It is too complicated for beginners.

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Kafka and Schema Registry are the rock solid fully managed offerings from IBM Confluent Cloud. Other than that the rest of the offerings do not work as expected.

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The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.

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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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For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.

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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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• IBM Confluent helps us get data moving from system to system at near real time.
• IBM Confluent makes pushing data into a data lake easy - analytics, targeted deals, etc
• The RBAC and ACL setup helps us govern and secure our data, even between teams/applications.

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