Tag Archives: Data Platforms

East/West vs North/South Promotion Lifecycles: How Modern Financial Services Data Platforms Support Operational Stability and Analytical Freedom Simultaneously

Leave a Reply

This article argues that modern Financial Services (FS) data platforms must deliberately support two distinct but complementary promotion lifecycles. The well known and understood North/South lifecycle provides operational stability, governance, and regulatory safety for customer-facing and auditor-visible systems. In parallel, the East/West lifecycle enables analytical exploration, experimentation, and rapid innovation for data science and analytics teams. By mapping these lifecycles onto layered data architectures (Bronze to Platinum) and introducing clear promotion gates, FS organisations can protect operational integrity while sustaining analytical freedom and innovation.

Continue reading →

Operationalising SCD2 at Scale: Monitoring, Cost Controls, and Governance for a Healthy Bronze Layer

Leave a Reply

This article explains how to operationalise Slowly Changing Dimension Type 2 (SCD2) at scale in the Bronze layer of a medallion architecture, with a focus on highly regulated Financial Services environments. It outlines three critical pillars: monitoring, cost control, and governance, needed to keep historical data trustworthy, performant, and compliant. By tracking growth patterns, preventing meaningless updates, controlling storage and compute costs, and enforcing clear governance, organisations can ensure their Bronze layer remains a reliable audit-grade historical asset rather than an unmanaged data swamp.

Continue reading →

Databricks vs Snowflake vs Microsoft Fabric: Positioning the Future of Enterprise Data Platforms

Leave a Reply

This article extends the Databricks vs Snowflake comparison to include Microsoft Fabric, exploring the platforms’ philosophical roots, architectural approaches, and strategic trade-offs. It positions Fabric not as a direct competitor but as a consolidation play for Microsoft-centric organisations, and introduces Microsoft Purview as the governance layer that unifies divergent estates. Drawing on real enterprise patterns where Databricks underpins engineering, Fabric drives BI adoption, and functional teams risk fragmentation, the piece outlines the “Build–Consume–Govern” model and a phased transition plan. The conclusion emphasises orchestration across platforms, not choosing a single winner, as the path to a governed, AI-ready data estate.

Continue reading →

Databricks vs Snowflake: A Critical Comparison of Modern Data Platforms

Leave a Reply

This article provides a critical, side-by-side comparison of Databricks and Snowflake, drawing on real-world experience leading enterprise data platform teams. It covers their origins, architecture, programming language support, workload fit, operational complexity, governance, AI capabilities, and ecosystem maturity. The guide helps architects and data leaders understand the philosophical and technical trade-offs, whether prioritising AI-native flexibility and open-source alignment with Databricks or streamlined governance and SQL-first simplicity with Snowflake. Practical recommendations, strategic considerations, and guidance by team persona equip readers to choose or combine these platforms to align with their data strategy and talent strengths.

Continue reading →