Apache Sqoop vs. IBM InfoSphere Information Server

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

It's super terrific with workflow automation. Terrific with data backup and convenient with encryption of data. Reliable with asset management Great to discover virtual servers

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

• Any source to any target support.
• ETL flexibility without coding.
• Extreme data volume processing.
• Native integration with other Data integration functionalities such as data profiling, data cleansing, metadata management.

• 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

• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

Incentivized

• Scale of implementation
• IBM techsupport

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

DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

• 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

• If you don't use all of the product family, it will be expensive. But if you want to plan use all the products and you will position it in the center of your infrastructure ROI will be effective.

Incentivized