A company needs to process large amounts of data and store them in a data store accordingly. The data consists of all IP addresses which are accessing their website. There would be around billions of rows being stored in the data store. The company have decided to use the AWS EMR service. The company needs to be able to query the data efficiently based on the IP address. Which of the following would be an ideal implementation plan for this?

Answer options:

A.Use S3 as the underlying storage for the EMR cluster. Ensure a bucket is created for each IP address
B.Make use of HBase on EMR. Ensure that the IP address is used as the underlying key
C.Use S3 as the underlying storage for the EMR cluster. Ensure that the prefixes have the IP address attached such as bucketname/IPaddress-filename
D.Post the data from EMR to Redshift for analysis

Answer – B
The best way to handle so much of data is to make use of HBase
The AWS Blog mentions the following
 HBase offers a number of powerful features including:
Strictly consistent reads and writes.
High write throughput.
Automatic sharding of tables.
Efficient storage of sparse data.
Low-latency data access via in-memory operations.
Direct input and output to Hadoop jobs.
Integration with Apache Hive for SQL-like queries over HBase tables, joins, and JDBC support.
Options A and C would be less efficient than using HBase.
Option D would just be an overhead.
For more information on Hbase on EMR, please visit the url
https://aws.amazon.com/blogs/aws/apache-hbase-on-emr/