References:
ANALYTICS 1.0 – Business Intelligence, RDBMS & Data Warehousing
- Vertical scaling
- Better results and analysis meant higher processing power & memory
- Complex systems
- Chances of singular failure
- Backup was compulsory
- Storage in RDBMS
- Transformation in business dimensions and facts in Data Warehouse
- Descriptive analytics mainly
ANALYTICS 2.0 – BigData, Hadoop, NoSQL & Spark – In memory computing
Problems with Analytics 1.0
- Costly hardware
- Large amounts of data
- Unstructured data
Solution
- BigData
- Hadoop – Large files
- NoSQL – Small files or less size data
- Horizontal scaling
Problems with BigData
- Querying unstructured data
- Large amount of data for real time processing not batch processing
Solution
- PIG
- HIVE
- Spark – In-memory computing
- Predictive analytics mainly
ANALYTICS 3.0 – Edge Computing, Data Rich Organizations, Real Time Analytics & more
Problems with Analytics 2.0
- Most analysis was retrospective and for past data
- Organization wide data also started getting collected but was unused
- Real time data started to flow in big amounts
Solution
- Data rich organizations
- Use data from organization to build products not just mapped to market but also with own organization
- E.g. Differentiated products in manufacturing to compete with mass economies of scale production
- Edge computing
- Real time processing
- Combined data
- Embedded analytics
- Data discovery
- Cross functional teams
- Moving to Prescriptive & Real Time analytics
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