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
It is suitable for companies without a proper data warehouse. He does very well in sales analysis and KPI management. It builds mini data warehouses, is good at data fusion, and interfaces well with other systems. Also, the export function and filter can greatly help you to get only the information you want in the format you want.
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.
We can easily provide the information that the user wants and customize it according to their needs. Sometimes a certain report can be used as the basis for creating another one that saves you time to deliver critical information in the shortest amount of time with the best results. Builds mini data warehouses, is good at data fusion, and interfaces well with other systems.
I love how easy it is to create prototypes due to its simple simulation and modeling system. Other than that, the codes are usually simple and not very complex and it's built-in debugging adds to that ease. is an excellent tool for analyzing, classifying, and visualizing data. I do this most of the time to help me grab huge collections of data.
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.
You can have a good reading of the data, you undoubtedly have cost savings and eliminate unnecessary and repetitive processes, we have unstructured data that, when structured, are elements of information that have become a competitive advantage for our organization, it is undoubtedly a strategic ally for the organization in the decision-making process
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.
