Learn: Data

Topics

Find answers to...

  • What is data science, data analytics, data exploitation?
  • What is a database, data warehouse, data lake?
  • How is data produced, where is it produced, how is it ingested?
  • What is end-to-end data observability and monitoring?
  • What is Extract, Transform, Load (ETL)? What is traditional vs modern ETL?
  • What is Extract, Load, Transform (ELT)? How does it differ from ETL?
  • How mature is the organization in the collection, manipulation, exploitation of data?
  • Where are the data silos in our organization and how did they come to be?
  • What are our business needs for data (e.g. latency, scale, security)?
  • What does the data tell us about our current business performance?
  • How can we improve our customer experience based on the data?
  • How can we design and implement a scalable data pipeline to ingest and process large volumes of structured and unstructured data from multiple sources?
  • What is a typical design of a cloud-native stack to derive business intelligence?

Objectives

  • Know where we truly are amid (self created) hype on data science, transformation, AI
  • Know what's possible to ingest, ETL, exploit data, vs whats not in the organization
  • Understand the link between business needs, data collection, analysis, storytelling, arriving at actionable insights
  • Learn about modern cloud-based stacks that let you build a scalable data pipeline
  • Get exposed to efficiency, cost optimization, ROI for designing and implementing data architectures
  • Understand the needs and implementation of data governance and data security

My Observations

Stop creating a new data lake to succeed previous data lakes and warehouses!

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