Apache Sqoop vs. VMware Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)

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

If you need to execute ml algorithms, learning techniques, or mathematical calculations on large amounts of heterogeneous data, VMware Tanzu Data Services will be ideal. It will be really simple to set up, particularly if you choose AWS as your integrated cloud provider. However, if you're working with lower data amounts, such as gigabytes, it can be superfluous.

Incentivized

• 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

• Apache MADlib provides popular machine learning functionality.
• Allows you to query terabytes of data databases.
• Interoperability for AWS S3 is effortless.

Incentivized

• 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

• Running on Azure is a little more difficult.
• Synchronization with Kafka may be a little easier.

Incentivized

They were very helpful. We needed support for initial implementation.

Incentivized

• 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

• 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

• There was a noticeable reduction in system reliability.
• Saw a reduction in unsuccessful analytics operations.

Incentivized