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Economical Data Warehousing Using Amazon Web Services and Hadoop

How to transfer data between an Oracle database and Hadoop using Sqoop

Sqoop makes it very easy to transfer data between Oracle and Hadoop using a single command. The reason why we would want to import data from an Oracle database into Hadoop/Hive is that we might want to join Hive tables with Oracle lookup tables, or other data residing in Oracle database.

Data originating from an Oracle database can help better understand and analyze raw, more granular data contained in Hive/HDFS.

Sqoop uses JDBC driver to connect to an Oracle database. If you have a table results in your Oracle database and want data from it to be imported to Hadoop HDFS ( Hadoop Distributed File System ) for further processing by Hive you only need to issue a single command:

./sqoop import --connect jdbc:oracle:thin:@ec2-23-21-167-145.compute-1.amazonaws.com:1521:TEST --username system -P --table results --columns "owner" -m 1

This command will connect to the Oracle database TEST residing on Amazon Web Services server ec2-23-21-167-145.compute-1.amazonaws.com, as user system and import column owner from table results into HDFS.

It is now easy to load this table into Hive for further processing using HiveQL language. HiveQL is capable of SQL-like data processing while transparenlty utilizing MapReduce paradigm ( there is no need to write MapReduce programs ):

hive>LOAD DATA  INPATH '/usr/lib/hadoop-0.20/sqoop-1.3.0/bin/results/part-m-00000' OVERWRITE INTO TABLE results;
Loading data to table default.results
Deleted file:/user/hive/warehouse/results
OK
Time taken: 0.166 seconds

We can now issue various HiveQL commands to query and further process this data:

hive> select * from results limit 10;
select * from results limit 10;
OK
SYS
SYS
SYS
SYS
SYS
SYS
SYS
SYS
SYS
SYS

Time taken: 0.219 seconds

Once data warehousing analytics is completed in Amazon AWS Hadoop  it is often convenient to upload aggregate data ( results ) to relational database like Oracle for further data processing or visualization.

Let's say that result of our data analysis is contained in the file target.txt, residing in HDFS.

Following command will export file target.txt from HDFS into Oracle database ORCL, residing on the server  ec2-23-21-178102.compute1.amazonaws.com, connecting as user system. Data will be exported to the Oracle database table HADOOP_SOURCE.

$ sqoop export --connect jdbc:oracle:thin:@ec2-23-21-178-102.compute-1.amazonaws.com:1521:ORCL --username SYSTEM --table HADOOP_SOURCE  --export-dir /usr/lib/hadoop-0.20/input/target.txt -P

If your Hadoop cluser resides on Amazon Web Services it is very easy to add more processing power - Hadoop DataNodes. It is also possible to load extremely large volume of data ( petabytes ) using AWS Export/Import service. You can also upload data to AWS S3 service, or straight to AWS EC2 EBS volumes that can be attached to Hadoop DataNodes of your choice.

More Stories By Ranko Mosic

Ranko Mosic, BScEng, is specializing in Big Data/Data Architecture consulting services ( database/data architecture, machine learning ). His clients are in finance, retail, telecommunications industries. Ranko is welcoming inquiries about his availability for consulting engagements and can be reached at 408-757-0053 or [email protected]

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