Likelihood to Recommend
Sqoop is great for sending data between a JDBC compliant database and a
environment. Sqoop is built for those who need a few simple CLI options to import a selection of database tables into
, 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
loading, in which case
's JDBC utilities might be preferred
Read full review
My recommendation obviously would depend on the application. But I think given the right requirements, IBM DB2 Big SQL is definitely a contender for a database platform. Especially when disparate data and multiple data stores are involved. I like the fact I can use the product to federate my data and make it look like it's all in one place. The engine is high performance and if you desire to use Hadoop, this could be your platform.
Read full review Pros 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 Read full review data storage data manipulation data definitions data reliability Read full review Cons 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. Read full review Cloud readiness. Ease of implementation. Read full review Usability
IBM DB2 is a solid service but hasn't seen much innovation over the past decade. It gets the job done and supports our IT operations across digital so it is fair.
Read full review Support Rating
IBM did a good job of supporting us during our evaluation and proof of concept. They were able to provide all necessary guidance, answer questions, help us architect it, etc. We were pleased with the support provided by the vendor. I will caveat and say this support was all before the sale, however, we have a ton of IBM products and they provide the same high level of support for all of them. I didn't see this being any different. I give IBM support two thumbs up!
Read full review Alternatives Considered 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. Read full review
MS SQL Server was ruled out given we didn't feel we could collapse environments. We thought of MS-SQL as more of a one for one replacement for Sybase ASE, i.e., server for server.
was evaluated and given a big thumbs up but was rejected because the SQL would have to be rewritten at the time (now they have an accelerator so you don't have to). Also, there was a very low adoption rate within the enterprise. IBM DB2 Big SQL was not selected even though technically it achieved high scores, because we could not find readily available talent and low adoption rate within the enterprise (basically no adoption at the time). We ended up selecting Exadata because of the high adoption rate within the enterprise even though technically HANA and Big SQL were superior in our evaluations.
Read full review Return on Investment 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. Read full review better data visibility solid reliability for mission critical data Read full review ScreenShots