Data & Analytics Reading List 05/2024
[…] there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns
Recommended reading to explore the unknown in data & analytics and dive deeper beyond specific technologies. Day by day.
01.05. - Talk Summary: RAG, the bad parts (and the good!)
02.05. - Change Data Capture (CDC) Done Correctly (CDC) - Change Data Capture Best Practices
03.05. - Cloud Architecture Blueprint - Central vs Decentral
04.05. - 10 recommendations for a successful enterprise Data Mesh implementation
05.05. - Pragmatic Techniques: Building a Data-Driven Culture Today
06.05. - What matters to you when choosing a data platform? i.e. Snowflake, Databricks, BigQuery, Redshift
08.05. - How do you build a Center of Excellence for Data, Analytics, and AI!
09.05. - Building A Million Dollar Data Product
10.05. - The Value of Data
11.05. - Synchronizing Organizational Decisions
12.05. - Why companies build data applications
13.05. - Data Pipeline Automation: Benefits, Use Cases & Tools
14.05. - The Semantic Layer Movement: The Rise & Current State
15.05. - YAML developers and the declarative data platforms
16.05. - Data Ownership In Data Mesh
17.05. - The Problem with Data Governance
18.05. - Data Co-pilot and Data Concierge
19.05. - The Future Of Data Storytelling Is Augmented, Not Automated
20.05. - ML & Gen AI for data teams
21.05. - The Self-Service Paradox: When Expanding Data Access Breeds Chaos
22.05. - Data Governance Framework: Pioneering Governance Shift Left
23.05. - A Technical Guide to Data Contract from conceptualisation to implementation
24.05. - The Sisyphean Struggle And The New Era Of Data Infrastructure
25.05. - Apache Hudi: From Zero To One
26.05. - Ten years of Building Open Source Standards: From Parquet to Arrow to OpenLineage
27.05. - Data-as-a-Product and Data-Contract: An evolutionary approach to data maturity
28.05. - How To Build Successful Business Cases as a Data Engineer
29.05. - Data Engineering Design Principles You Should Follow
30.05. - Building a Data Platform in 2024
31.05. - 2024 #15 The Data Evangelist