Big Data: from mammoth to elephant
summary
Describes a production stack that moved from slow RDBMS ETL to Hadoop/HBase with Solr and Kafka for streaming, claiming ETL that once took weeks now runs in hours and ingesting billions of records per month. Uses Spark for batch analytics, Storm (or Spark Streaming) for low‑latency pipelines, and Kafka so multiple consumers can replay streams without extra copies. HBase holds primary data while Solr stores document IDs for search, achieving multi‑millisecond to low‑hundreds‑millisecond visibility.
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chapters (11)
- 0:00The necessity of Big Data
- 1:41Big Data system architecture
- 4:23Challenges in data processing
- 8:09Why choose Hadoop?
- 12:47Real-time system evolution
- 16:11Indexing and search solutions
- 19:47Real-time data streaming
- 22:28Reactive system principles
- 26:23Best practices for transition
- 30:21Q&A and technical discussion
- 49:20Final interaction workflow
