Using Open Source BI in the Real World
summary
Open source BI proves usable but uneven: databases and reporting reach production while OLAP, ETL and advanced analytics lag; deployments often handle modest warehouses — 81% under 1TB — and many projects stumble — 51% fail evaluations — usually from immature tools or lack of resources. Real deployments show concrete stacks and metrics: nightly ETL from 3,000 disparate sources into a 180 GB database with 700 GB of documents serving ~4,500 users; Hadoop/MapReduce drive high-scale ad use.
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chapters (4)
- 0:00Start
- 16:59Lower cost Ease of integration / open... Reduced vendor dependence Deployment flexibility Access to the source code
- 23:113000+ disparate data sources 4500 users Complex organizational structures / hierarchy Multi tenant environment Segregated data bases per individual organization Same data used for various business functions Granular data
- 33:40Use analytics to design and test advertising models using only relevant dimensions Gather and determine business requirements before embarking on the journey Build an infrastructure plan that will support the data collection and analytics platform
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