Solving Real & Big (Data) Problems Using Hadoop
summary
Describes using Hadoop as an enterprise data hub with HDFS, MapReduce, YARN and connectors like Sqoop/Flume, plus Impala, Spark, Hive, Solr, HBase, Oozie and Hue to run real workloads on terabytes and 10 years of logs. Gives concrete wins: predictive logistics that save millions by preshipping parts, anomaly detection across years of data, a Twitter-driven product shift (pineapple outsells vanilla), and a suicide-risk model claiming 80% likelihood detection and tumor-DNA processing reduced from 11 months to 6 weeks.
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chapters (18)
- 0:00Start
- 2:42The New Types of Data
- 9:56To Deploy and Use Hadoop as an Enterprise Data Hub
- 10:54Hadoop What Is It
- 11:39Yarn
- 17:28Sentry
- 18:01Workflow Scheduler
- 19:40Typical Use Cases
- 19:59Etl Offload
- 24:44Examples
- 25:13Event Prediction
- 27:48Anomaly Detection
- 32:08Why Do Customers Not Like Us Anymore How Do We Prevent Customers from Turning Away from Our Service
- 34:48How Do We Increase Our Online Sales
- 38:41How Do We Prevent Suicide
- 41:25How Do We Reduce World Hunger
- 43:43Global Warming
- 44:27How Do We Improve Education
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