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We use Hive for reporting and ad hoc queries.
We use Hive for data mining and analysis on our 435M monthly global users.
We use Hive for data mining, internal log analysis and ad hoc queries.
We use Hive for data mining, internal log analysis, R&D, and reporting/analytics.
We use Hadoop to store copies of internal log and dimension data sources and use it as a source for reporting/analytics and machine learning. Currently have a 640 machine cluster with ~5000 cores and 2PB raw storage. Each (commodity) node has 8 cores and 4 TB of storage.
We use Hive for user analytics, dataset cleaning, and machine learning R&D.
We use Hive for analytics, machine learning and social graph analysis.
We use Hive as part of a larger Hadoop pipeline to serve near-realtime web analytics.
We use Hive for various ad hoc queries.
We implemented Hive to analyse large amounts of doctors across the United States, and for internal analytics for over 1M pageview/day.
We use hive for replacing Oracle DW, big data analysis and integrating R. We develop the enterprise Hive.
We use Hive as a customer-facing analysis destination for our hosted syslog and app log management service.
We use Hive to host all our fact and dimension data. Off this warehouse, we do reporting, analytics, machine learning and model building, and various ad hoc queries.
We use Hive for analytics, machine learning and customer interaction analysis of web applications.
We use Hive for data mining, internal log analysis and ad-hoc queries. We also do some extensively developing work on Hive.
Hot Wikipedia Topics, Served Fresh Daily. Powered by Cloudera Hadoop Distribution & Hive on EC2. We use Hive for log data normalization and building sample datasets for trend detection R&D.
We use Hive as the core database for our data warehouse where we track and analyze all the usage data of the ads across our network.