In this episode we deep-dive into the Platinum Layer [1] The challenges data teams have when analysts need to move quickly while data engineers need to maintain stability over time.[2] Gold Layer vs. Platinum layer[3] Governance and quality aspects
In this episode we discuss the the emergence of the Platinum Layer on top of existing data architectures:[1] The Medallion architecture [2] Rigidity and stability vs. agility and speed in analytics [3] Scenarios where speed and agility beat stability [4] The Platinum Layer
In this episode we discuss the differences between data warehouse and data lake:[1] Defining what are data warehouse and data lake[2] What are the tradeoffs organizations need to consider when choosing one or the other and how to choose between them[3] 2025 trends in the warehouse/data lake space
In this episode we discuss the data architecture patterns:
In this episode we discuss the analytics data development tech stack:
[1] Overview of the analytics data development process
[2] The components of the analytics data development tech stack
[3] Best of breed vs. best of suite approaches
In this episode we discuss:
[1] What is data lineage and why it's important
[2] Real-world example of data lineage in action
[3] What is column-level lineage
In this episode we talk about data validation: [1] What is data validation and why it's important [2] Types of data validation [3] Where does data validation fit in the overall data development process [4] Data validation and observability
In this episode we discuss CI/CD in software development and in data development: [1] What is CI/CD - definition and benefits [2] CI/CD in software development [3] CI/CD in data development
In this episode we discuss the different roles in a typical data analytics team, and their responsibilities:
In this episode we talk about: [1] What is data transformation [2] Examples of data transformation [3] Benefits of implementing data transformation [4] Common pitfalls [5] Simple example of real life data transformation
