Tag Archives: Data Platforms

Edge Systems Are a Feature: Why OLTP, CRM, and Low-Latency Stores Must Exist

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Modern data platforms often treat operational systems as legacy constraints to be eliminated. This article argues the opposite. Transactional systems, CRM platforms, and low-latency decision stores exist because some decisions must be made synchronously, locally, and with authority. These “edge systems” are not architectural debt but purpose-built domains of control. A mature data platform does not replace them or centralise authority falsely; it integrates with them honestly, preserving their decisions, context, and evolution over time.

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Authority, Truth, and Belief in Financial Services Data Platforms

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Financial services data architectures often fail by asking the wrong question: “Which system is the system of record?” This article argues that regulated firms operate with multiple systems of authority, while truth exists outside systems altogether. What data platforms actually manage is institutional belief: what the firm believed at a given time, based on available evidence. By separating authority, truth, and belief, firms can build architectures that preserve history, explain disagreement, and withstand regulatory scrutiny through accountable, reconstructable decision-making.

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Eventual Consistency in Regulated Financial Services Data Platforms

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In regulated financial services, eventual consistency is often treated as a technical weakness to be minimised or hidden. This article argues the opposite: eventual consistency is the only honest and defensible consistency model in a multi-system, regulator-supervised institution. Regulators do not require instantaneous agreement: they require explainability, reconstructability, and reasonableness at the time decisions were made. By treating eventual consistency as an explicit architectural and regulatory contract, firms can bound inconsistency, preserve historical belief, and strengthen audit defensibility rather than undermine it.

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Why UK Financial Services Data Platforms Must Preserve Temporal Truth for Regulatory Compliance

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A Regulatory Perspective (2025–2026). UK Financial Services regulation in 2025–2026 increasingly requires firms to demonstrate not just what is true today, but what was known at the time decisions were made. Across Consumer Duty, s166 reviews, AML/KYC, model risk, and operational resilience, regulators expect deterministic reconstruction of historical belief, supported by traceable evidence. This article explains where that requirement comes from, why traditional current-state platforms fail under scrutiny, and why preserving temporal truth inevitably drives architectures that capture change over time as a foundational control, not a technical preference.

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Temporal RAG: Retrieving “State as Known on Date X” for LLMs in Financial Services

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This article explains why standard Retrieval-Augmented Generation (RAG) silently corrupts history in Financial Services by answering past questions with present-day truth. It introduces Temporal RAG: a regulator-defensible retrieval pattern that conditions every query on an explicit as_of timestamp and retrieves only from Point-in-Time (PIT) slices governed by SCD2 validity, precedence rules, and repair policies. Using concrete implementation patterns and audit reconstruction examples, it shows how to make LLM retrieval reproducible, evidential, and safe for complaints, remediation, AML, and conduct-risk use cases.

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

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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.

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

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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.

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

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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.

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Databricks vs Snowflake: A Critical Comparison of Modern Data Platforms

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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.

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