Solving Real & Big (Data) Problems Using Hadoop
summary
Shows how Hadoop and its ecosystem solve real limits: a network company processes ~600,000 phone‑home logs/week and requires 40% finished in an 18‑hour window, moves to Hadoop and handles ~7 TB/month growth while turning queries touching 240 billion records from weeks into hours. Describes concrete components and flows — HDFS, MapReduce for ETL, Sqoop ingestion, Flume streaming, Hive/Pig for queries, Impala for interactive SQL, Solr/Zookeeper and HBase for indexing and fast lookups, and operational tradeoffs with Cloudera and Hue.
AI-written summary · about
chapters (15)
- 0:00Introduction
- 1:36What is Hadoop
- 6:44Advanced Analytics
- 9:51Near RealTime
- 13:26Failover
- 14:30Solar Community
- 16:02HBase
- 18:23Two Views
- 19:46Data Silos
- 22:10Typical Use Cases
- 24:16Real World Example
- 28:07Architecture
- 31:20Use Cases
- 35:18Medical Data
- 39:11What to Consider
related talks
51mBIG DATA STORAGE & PROCESSING: Introduction to Hadoop and Its Ecosystem
01h 35mDeep Dive into the Big Data Landscape
19mDemystifying Big Data
24mIntroduction to Hadoop
59mHadoop Essentials for the SQL Server Professional
08mHadoop and Big Data Hype
47mWhat's Ahead for Big Data?
30mScalability Challenges in Big Data Science