I strongly agree with Jorn and Russell. There are different solutions for data movement depending upon your needs frequency, bi-directional drivers. workflow, handling duplicate records. This is a space is known as " Change Data Capture - CDC" for short. If you need more information, I would be happy to chat with you. I built some products in this space that extensively used connection pooling over ODBC/JDBC.
Happy to chat if you need more information. -Sachin Naik >>Hard to tell. Can you give more insights >>on what you try to achieve and >>what the data is about? >>For example, depending on your use case sqoop can make sense or not. Sent from my iPhone > On Jan 27, 2017, at 11:22 PM, Russell Spitzer <russell.spit...@gmail.com> > wrote: > > You can treat Oracle as a JDBC source > (http://spark.apache.org/docs/latest/sql-programming-guide.html#jdbc-to-other-databases) > and skip Sqoop, HiveTables and go straight to Queries. Then you can skip > hive on the way back out (see the same link) and write directly to Oracle. > I'll leave the performance questions for someone else. > >> On Fri, Jan 27, 2017 at 11:06 PM Sirisha Cheruvu <siri8...@gmail.com> wrote: >> >> On Sat, Jan 28, 2017 at 6:44 AM, Sirisha Cheruvu <siri8...@gmail.com> wrote: >> Hi Team, >> >> RIght now our existing flow is >> >> Oracle-->Sqoop --> Hive--> Hive Queries on Spark-sql (Hive >> Context)-->Destination Hive table -->sqoop export to Oracle >> >> Half of the Hive UDFS required is developed in Java UDF.. >> >> SO Now I want to know if I run the native scala UDF's than runninng hive >> java udfs in spark-sql will there be any performance difference >> >> >> Can we skip the Sqoop Import and export part and >> >> Instead directly load data from oracle to spark and code Scala UDF's for >> transformations and export output data back to oracle? >> >> RIght now the architecture we are using is >> >> oracle-->Sqoop (Import)-->Hive Tables--> Hive Queries --> Spark-SQL--> Hive >> --> Oracle >> what would be optimal architecture to process data from oracle using spark >> ?? can i anyway better this process ? >> >> >> >> >> Regards, >> Sirisha >>
