Big Data Analysis
summary
Covers practical Big Data patterns in Java with concrete tradeoffs: HDFS with 3x replication and file append-once semantics, MapReduce (JobTracker) limits for batch jobs (cluster streaming by block, failure-prone cheap hardware), and Spark in-memory for low-latency analytics. Describes sharding pain, replica counts, occasional 1 TB jobs on a small cluster, job distribution and reducer key skew, and prototypes that work but expose operational complexity and consistency/latency tradeoffs.
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chapters (8)
- 0:00Big Data introduction
- 2:12Traditional database problems
- 6:12MapReduce and clusters
- 10:08HDFS and data distribution
- 14:08Analytics and vertical solutions
- 18:59Architecture and interfaces
- 22:15Real-time data processing
- 25:55Practical implementations
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