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Data Engineering articles and guides

This topic collects articles and guides about Data Engineering, with a practical focus on production data, AI and ML engineering systems.

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Data fabric architecture connects a warehouse, lakehouse, order system and policy documents to reports and AI agents through shared integration and access services, coordinated by catalog, context, lineage and policies.

Architectural guide October 28, 2026 · 15 min read

Data fabric: connecting data, context and access across systems

Explore data fabric architecture through MorrowBasket's reports and agents, and see how shared context and access controls support AI-ready data platforms.

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Sales and Inventory teams own their data products for reporting and forecasting, supported by shared rules and a common platform for publishing, access and monitoring.

Architectural guide October 21, 2026 · 15 min read

Data mesh: organizing teams around the data they deliver

Explore data mesh through MorrowBasket's teams and learn how domain ownership, data products and shared governance support analytics and AI.

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A lakehouse combines Parquet files and a transaction log into managed tables, with separate SQL and ML processing for reports and forecasts, supported by catalog and access controls.

Architectural guide October 14, 2026 · 14 min read

Data Lakehouse: when your data lake can replace a separate warehouse

Explore how data lakehouse architecture adds reliable tables to a data lake, supports analytics and ML, and can replace a separate data warehouse.

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A data lake holds order exports, application events, policy documents and prepared data, with dataset descriptions and a separate engine that reads files and writes outputs.

Architectural guide October 7, 2026 · 14 min read

Data lake: what it takes to turn stored files into useful data

Explore data lake architecture through MorrowBasket's files, formats and processing, and learn what makes stored data useful for analytics and AI.

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A data warehouse sales model connects sales records with products, stores and dates, supported by shared definitions and preserved history.

Architectural guide September 27, 2026 · 13 min read

Data warehouse: where business data gets its meaning

See how a data warehouse gives reports and AI consistent business data, where historical correctness matters, and when a warehouse is enough.

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Data platform architecture connecting source systems to storage, preparation and publishing for analytics and AI, with ownership, history and access responsibilities.

Architectural guide September 23, 2026 · 15 min read

Data platform architectures: what changes when AI needs your data?

Understand warehouses, lakes, lakehouses, mesh and fabric through one business example, and learn what makes a data platform ready for AI.

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Hand-drawn technical comparison of feature tables, transformation libraries, warehouse-first patterns and feature stores for an ML workload

Architectural guide September 2, 2026 · 10 min read

Feature store alternatives: feature tables, transformation libraries and warehouse-first patterns

Feature store alternatives for production ML: when feature tables, shared transformation libraries or warehouse-first patterns are enough, and when a feature store earns its cost.

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Hand-drawn architecture sketch showing offline feature store, online store, metadata contracts and serving boundaries for production machine learning

Architectural guide August 5, 2026 · 14 min read

Feature store architecture: offline, online, metadata and serving boundaries

Design feature store architecture across offline feature stores, online lookup, metadata contracts and feature serving for production ML.

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Hand-drawn technical sketch of source events moving through raw system-of-record data, trusted data, quarantine and published data products

Architectural guide July 28, 2026 · 13 min read

Medallion architecture for engineers: building trusted data products

Medallion architecture for engineers: design raw data, trusted views and published data products with clear contracts, replay paths and ownership.

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Hand-drawn architecture sketch comparing direct feature pipelines with a shared feature store used by multiple models and serving systems

Architectural guide June 27, 2026 · 14 min read

Feature stores: what they are, when you need one, and which use cases justify them

Learn what a feature store does, when ML teams need one, and when versioned feature tables or warehouse-first pipelines are enough.

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