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](DataMaturityLifecycle) — A structural roadmap from fragmented silos to Data Mesh.

- [Shift Left Data Engineering](ShiftLeftDataEngineering) — Moving data quality upstream via contracts.

Pipeline design

- [DataPipelineDesign](DataPipelineDesign) — Sources, transforms, sinks; idempotency and observability

- [EtlVsElt](EtlVsElt) — When transform belongs early vs. late

- [MapReduceParadigm](MapReduceParadigm) — The paradigm that defined the batch era

- [DbtAndAnalyticsEngineering](DbtAndAnalyticsEngineering) — dbt as transformation tool, the analytics-engineering role

Vertical-Specific Pipelines

- [Fintech Data Ingestion Blueprint](FintechDataIngestionBlueprint) — Ingesting, normalizing, and storing third-party financial data

Modeling

- [Data Modeling Fundamentals](DataModelingFundamentals) — Star, snowflake, dimensional, the fact-and-dim mental model

- [NoSQL Database Types](NoSqlDatabaseTypes) — When and why to move beyond relational

- [Jsonb In Postgresql](JsonbInPostgresql) — Handling semi-structured data in a relational engine

- [Master Data Management](MasterDataManagement) — MDM as the discipline; tools as the implementation

Catalogs and metadata

- [Data Catalog Tools](DataCatalogTools) — DataHub, Amundsen, Atlan; what they actually do

- [Data Lake Architecture](DataLakeArchitecture) — Organizing massive unstructured datasets

Adjacent clusters

- [Cloud Platforms Hub](CloudPlatformsHub) — Where pipelines and warehouses run

- [DevOps and SRE Hub](DevOpsAndSreHub) — Operating data pipelines