What are Common use cases for Hadoop

[Ananya_Thakur]

What are the common use cases for Hadoop?

[Lakhan]

Hadoop has many operational as well as strategic use cases across multiple industries.
The 3 main categories are:
1. Transformation: Hadoop helps you transform large amounts of data more quickly, reliably, and affordably (e.g., for loading into the data warehouse).
2. Active archiving: HDFS gives you access to data that would otherwise be taken offline (e.g., to tape) due to the high cost of actively managing it.
3. Exploration/Analytics: Hadoop lets you analyze and get value from data that otherwise could not be easily modelled in rigid relational systems

[Lakhan]

Dodd-Frank Compliance at a bank

A leading retail bank is using Cloudera and Datameer to validate data accuracy and quality to comply with regulations like Dodd-Frank

Problem: The previous solution using Teradata and IBM Netezza was time consuming and complex, and the data mart approach didn’t provide the data completeness required for determining overall data quality.

Solution: A Cloudera + Datameer platform allows analyzing trillions of records which currently result in approximately one terabyte per month of reports. The results are reported through a data quality dashboard.

[Lakhan]

Trucking data @ US Xpress

US Xpress - one of the largest trucking companies in US - is using Hadoop to store sensor data from their trucks. The intelligence they mine out of this, saves them $6 million / year in fuel cost alone.

Problem: Collecting and and storing 100s of data points from thousands of trucks, plus lots of geo data.

Solution: Hadoop allows storing enormous amount of sensor data. Also Hadoop allows querying / joining this data with other data sets.

[richa]

NetApp

NetApp collects diagnostic data from its storage systems deployed at customer sites. This data is used to analyze the health of NetApp systems.

Problem: NetApp collects over 600,000 data transactions weekly, consisting of unstructured logs and system diagnostic information. Traditional data storage systems proved inadequate to capture and process this data.

Solution: A Cloudera Hadoop system captures the data and allows parallel processing of data

[richa]

Storing and processing Medical Records

Problem: A health IT company instituted a policy of saving seven years of historical claims and remit data, but its in-house database systems had trouble meeting the data retention requirement while processing millions of claims every day

Solution: A Hadoop system allows archiving seven years’ claims and remit data, which requires complex processing to get into a normalized format, logging terabytes of data generated from transactional systems daily, and storing them in CDH for analytical purposes