DataEngineering Hub

This cluster covers the engineering side of data — pipelines, modeling, transformation, and the catalog layer that turns raw data into something usable. The focus is the operational and architectural patterns; modeling and analysis are adjacent topics.

Strategy and Lifecycle

• Data Maturity Lifecycle — A structural roadmap from fragmented silos to Data Mesh.
• Shift Left Data Engineering — Moving data quality upstream via contracts.

Pipeline design

• DataPipelineDesign — Sources, transforms, sinks; idempotency and observability
• EtlVsElt — When transform belongs early vs. late
• MapReduceParadigm — The paradigm that defined the batch era
• DbtAndAnalyticsEngineering — dbt as transformation tool, the analytics-engineering role

Vertical-Specific Pipelines

• Fintech Data Ingestion Blueprint — Ingesting, normalizing, and storing third-party financial data

Modeling

• Data Modeling Fundamentals — Star, snowflake, dimensional, the fact-and-dim mental model
• NoSQL Database Types — When and why to move beyond relational
• Jsonb In Postgresql — Handling semi-structured data in a relational engine
• Master Data Management — MDM as the discipline; tools as the implementation

Catalogs and metadata

• Data Catalog Tools — DataHub, Amundsen, Atlan; what they actually do
• Data Lake Architecture — Organizing massive unstructured datasets

Adjacent clusters

• Cloud Platforms Hub — Where pipelines and warehouses run
• DevOps and SRE Hub — Operating data pipelines