Introduction to Data Visualisation
summary
Argues cloud data platforms consume, transform, serve data and AI is a consumer, not part of the platform; claims most cloud migrations fix legacy and political failures, not technology. Describes Snowflake + S3 + DBT migrations, heavy logging of ingestion, user queries and cost‑per‑query controls, internal invoicing to departments, and enforcing minimum instance sizes. Reports killing an on‑prem datacenter, moving eight‑digit Oracle license spend to seven‑digit Snowflake and roughly 30% realized cost savings.
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chapters (19)
- 0:00Welcome
- 1:10Motivation for data platforms
- 2:37Myths vs realities of data platforms
- 4:24Legacy systems challenges
- 6:36Why Agile fails for data
- 8:26Political hurdles in data work
- 10:13Reasons for cloud migration
- 12:25End goals and expectations
- 13:30Technical transparency and quality
- 14:35Data mesh pitfalls
- 15:38Agile business involvement
- 17:25Cloud adoption benefits
- 18:49Product owner failures
- 19:56Business use cases
- 21:25Data platform transformation
- 22:08Importance of sponsors
- 23:34Modern platform setup
- 25:47POC and MVP delays
- 26:10Q&A
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