7 decks to go beyond Azure fundamentals. Cloud patterns, modern data architectures, industrialized machine learning and event-driven architectures — the topics of an Azure data architect. This guide approaches these topics from the angle of choices and implementation specific to Azure.
This guide covers the major themes of Azure data architecture. It is not aligned with a single official certification but with the skills expected of a senior data architect or data engineer on the Azure platform. For a vendor-neutral explanation of the underlying concepts (Data Lake, Data Mesh, governance, MLOps), see the "Further Reading" section below.
Lambda, Kappa, Medallion, microservices, event-driven — key design patterns applicable on Azure and across cloud ecosystems.
Data Lake, Lakehouse, Data Mesh, Data Products. How these data organization paradigms translate concretely into Azure services.
Positioning and comparing Databricks, Azure Synapse Analytics and Microsoft Fabric: when to choose what, and why.
Microsoft Purview, Data Catalog, data lineage, classification and governance policies in an Azure architecture.
Event Hubs, Service Bus, Stream Analytics, Azure IoT Hub — real-time and event-driven architectures on Azure.
Azure Machine Learning, MLflow, Responsible AI, model industrialization and the ML lifecycle in production.
Each deck addresses a key domain of Azure data architecture. The suggested order moves from cross-cutting concepts to the most specialized topics.
Key cloud and data design patterns: Lambda, Kappa, Medallion, CQRS, Event Sourcing, Saga, Strangler Fig. Architectural references applicable regardless of the cloud platform, illustrated with Azure examples.
Classic Data Lake vs modern Lakehouse architectures on Azure. ADLS Gen2, Delta Lake, file formats (Parquet, Avro, ORC), partitioning and read optimization strategies.
Comparing and positioning the three major Azure analytics platforms. When to use Databricks (ML, complex data engineering), Synapse (DWH, analytical SQL) or Microsoft Fabric (unified SaaS platform). 55 cards on a topic that comes up often in professional Azure contexts.
Microsoft Purview for Data Catalog, automatic classification and data lineage. Data Mesh and Data Products as an organizational paradigm. Governance challenges in a large Azure organization.
Azure Event Hubs, Service Bus, Event Grid, Stream Analytics, Azure IoT Hub. Event-driven architecture, delivery guarantees, at-least-once vs exactly-once, and real-time processing patterns.
Azure Machine Learning Studio, MLflow on Azure, AutoML, ML pipelines, production model monitoring, Responsible AI and Azure AI Services. ML industrialization on the Azure platform.
Developing an architect's mindset: how to weigh performance, cost, maintainability and scalability. Practical Azure architecture decision cases and a methodology for structuring a recommendation.
This guide is dense and technical. Allow 6 to 8 weeks of regular review for solid understanding. Unlike Azure Fundamentals, the goal is not to memorize service names — it is to understand the trade-offs.
Decks 1 and 2: Cloud Architecture Patterns then Data Lake & Lakehouse. These two decks establish the cross-cutting concepts the others build on. Don't move to deck 3 until you feel comfortable with Delta Lake and Medallion concepts.
Decks 3 and 4: Databricks/Synapse/Fabric then Governance/Purview. Deck 3 is the most relevant in professional contexts — take time to understand each platform's use cases. FSRS will automatically schedule reviews of decks 1 and 2.
Decks 5, 6 and 7: Streaming/IoT, MLOps, Trade-offs. The last deck is designed to be studied at the end of the guide — it puts all learned concepts into practice by forcing you to choose between valid architectures.
This guide targets profiles who already understand cloud fundamentals (Azure Fundamentals level or equivalent experience). It is not suitable for complete beginners on Azure. If you are new to Azure, start with the Azure Fundamentals guide (8 decks, 355 cards) before tackling this one.
This guide is not aligned with a single Microsoft certification. It covers topics present in several advanced certifications: Azure Data Engineer (DP-203), Azure AI Engineer (AI-102) and Azure Solutions Architect (AZ-305). It is mainly designed to build Azure data architecture skills in a professional context — it does not replace official training or hands-on labs.
Recommended order: Patterns → Data Lake & Lakehouse → Databricks/Synapse/Fabric → Governance/Purview → Streaming/IoT → MLOps → Trade-offs. This order moves from the general to the specific and from the conceptual to the practical. The last deck (Trade-offs) is designed to be approached once the first 6 are mastered.
That is exactly what deck 3 covers (55 cards). In short: Databricks excels for complex data engineering and ML; Azure Synapse Analytics has historically been strong on analytical SQL and DWH; Microsoft Fabric is Microsoft's new unified SaaS platform. The choice depends on your existing stack, skills and priority use cases.
This guide covers data concepts (Data Lake, Data Mesh, governance, MLOps) from the angle of choices and implementation specific to Azure — which Azure service to use, and why. The Data & AI cluster (see "Further Reading") explains these same concepts in a vendor-neutral way, independent of any cloud provider. The two are complementary.
Allow 6 to 8 weeks with 20 to 30 minutes per day. This guide is denser than Azure Fundamentals — the 343 cards cover technical topics that often require progressive anchoring. The FSRS algorithm adapts the pace to your memory and schedules reviews automatically.
50 cards on core cloud patterns — the foundation of any Azure architect. Post-Azure Fundamentals level. Immediate access, no credit card required.
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