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Data Solutions & Architecture

A personal reference library covering modern data platform patterns, architectures, and solution design for Solution Architects working across cloud and on-premises data ecosystems. Each page focuses on the concepts, trade-offs, and talking points that matter in customer conversations — not implementation cookbooks.


Why This Exists

Data architecture is one of the most fragmented domains in enterprise IT. Customers are simultaneously managing legacy warehouses, cloud lakehouses, real-time streaming pipelines, and AI/ML workloads — often with overlapping tooling and unclear ownership.

This section helps SAs answer:

  • What is the right architectural pattern for this customer's data problem?
  • How do the major data platform components fit together — ingestion, storage, processing, serving?
  • What are the key trade-offs between approaches (e.g. lakehouse vs. warehouse, batch vs. streaming)?
  • How do I frame a data modernization story without overpromising?
  • What does "good" look like at each layer of the data stack?

Available References

Topic Description
Data Architecture Patterns Lakehouse, data warehouse, data mesh, data fabric — when to use each and how to position them
Data Ingestion & Pipelines Batch, micro-batch, and streaming ingestion patterns — ETL vs. ELT, CDC, and pipeline tooling landscape
Data Storage Layers Raw, curated, and serving zones; Delta Lake, Parquet, Iceberg; medallion architecture deep-dive
Data Governance & Cataloging Unity Catalog, data lineage, access control, data quality, and metadata management patterns
Real-Time & Streaming Kafka, Kinesis, Pub/Sub, Spark Streaming — architectures for low-latency data and event-driven systems
Data Serving & Consumption Semantic layers, SQL endpoints, data APIs, BI connectivity, and ML feature serving patterns

This is a living reference — pages will be expanded as new patterns and platforms become relevant. Each follows the same structure: what it is → why it matters → architecture → key trade-offs → SA talking points.