The better way is to read the data directly into spark using spark sql read jdbc . Apply the udf's locally . Then save the data frame back to Oracle using dataframe's write jdbc.
Thanks Deepak On Jan 29, 2017 7:15 PM, "Jörn Franke" <jornfra...@gmail.com> wrote: > One alternative could be the oracle Hadoop loader and other Oracle > products, but you have to invest some money and probably buy their Hadoop > Appliance, which you have to evaluate if it make sense (can get expensive > with large clusters etc). > > Another alternative would be to get rid of Oracle alltogether and use > other databases. > > However, can you elaborate a little bit on your use case and the business > logic as well as SLA requires. Otherwise all recommendations are right > because the requirements you presented are very generic. > > About get rid of Hadoop - this depends! You will need some resource > manager (yarn, mesos, kubernetes etc) and most likely also a distributed > file system. Spark supports through the Hadoop apis a wide range of file > systems, but does not need HDFS for persistence. You can have local > filesystem (ie any file system mounted to a node, so also distributed ones, > such as zfs), cloud file systems (s3, azure blob etc). > > > > On 29 Jan 2017, at 11:18, Alex <siri8...@gmail.com> wrote: > > Hi All, > > Thanks for your response .. Please find below flow diagram > > Please help me out simplifying this architecture using Spark > > 1) Can i skip step 1 to step 4 and directly store it in spark > if I am storing it in spark where actually it is getting stored > Do i need to retain HAdoop to store data > or can i directly store it in spark and remove hadoop also? > > I want to remove informatica for preprocessing and directly load the files > data coming from server to Hadoop/Spark > > So My Question is Can i directly load files data to spark ? Then where > exactly the data will get stored.. Do I need to have Spark installed on Top > of HDFS? > > 2) if I am retaining below architecture Can I store back output from spark > directly to oracle from step 5 to step 7 > > and will spark way of storing it back to oracle will be better than using > sqoop performance wise > 3)Can I use SPark scala UDF to process data from hive and retain entire > architecture > > which among the above would be optimal > > [image: Inline image 1] > > On Sat, Jan 28, 2017 at 10:38 PM, Sachin Naik <sachin.u.n...@gmail.com> > wrote: > >> 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 >>> >>> >>> >
