Identity Management in the Era of Big Data: The intersection of APIs, Hadoop & IdM
summary
Claims that identity stores are tiny (megabytes–gigabytes) but big data pushes petabytes of raw logs and even 2.0 zetabytes generated in 2011, so dumping everything into Hadoop/Cassandra/HBase breaks old models. Practical tools discussed: Kerberos and Unix users/groups are primitive, Hadoop is batch-only, NoSQL trades variety for scale, Cloudera/Hortonworks rely on Apache Knox. Proposes a data DNA that records per-row context and a row-level security tradeoff between speed, storage and fine-grained access, plus API-driven automation and self-monitoring.
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chapters (9)
- 0:00Intro
- 0:20Identity in the Era of Big Data
- 4:05Data Infrastructure
- 6:50Data Volume
- 10:50Big Data Insights
- 13:03Big Data Applications
- 15:42Big Data Challenges
- 21:10What is Data DNA
- 27:12What do we do
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