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AZ-305 Data Storage solutions

April 28, 2025•867 words•5 min read•
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AZ-305 Azure Solutions Architect Expert

Azure Recommendations for Data Storage Solutions

Below are the recommended Azure solutions for data storage as aligned with the AZ-305 exam objectives.

Design Data Storage Solutions

1. Relational Data Storage

Recommend a solution for storing relational data

  • Azure SQL Database: Fully managed, scalable relational database with built-in intelligence and high availability.
  • Azure SQL Managed Instance: Near 100% compatibility with SQL Server for lift-and-shift scenarios, offering full instance scope and VNet integration.
  • SQL Server on Azure VMs: For legacy applications requiring full OS/database control or features like SQL Server Reporting Services (SSRS).
  • Azure Database for PostgreSQL/MySQL/MariaDB: Managed open-source databases for MySQL, PostgreSQL, or MariaDB workloads.

Recommend a database service tier and compute tier

  • Service Tiers:
    • General Purpose: Balanced compute and storage for most workloads.
    • Hyperscale: Scalable storage (up to 100 TB) for read-heavy apps.
    • Business Critical: Low-latency, high-availability for mission-critical apps.
  • Compute Tiers:
    • Provisioned: Fixed vCores/memory for predictable workloads.
    • Serverless: Auto-scales compute based on demand, cost-effective for intermittent workloads.

Recommend a solution for database scalability

  • Vertical Scaling: Adjust vCores/storage dynamically via the Azure portal.
  • Horizontal Scaling: Use read replicas (Hyperscale tier) for read-heavy workloads.
  • Elastic Pools: Share resources across databases to optimize costs.

Recommend a solution for data protection

  • Backups: Automated backups with 7–35-day retention.
  • Encryption: Transparent Data Encryption (TDE) and Always Encrypted.
  • Security: Microsoft Defender for SQL detects and alerts on threats.
  • Geo-Replication: Failover groups for cross-region redundancy.

2. Semi-Structured and Unstructured Data Storage

Recommend a solution for storing semi-structured data

  • Azure Cosmos DB: Globally distributed, multi-model database with schema-agnostic storage (JSON, XML).
  • Azure Table Storage: NoSQL key-value store for flexible schemas and OData queries.
  • Azure Data Lake Storage Gen2: Unified storage for analytics on semi-structured data (e.g., Parquet, JSON).

Recommend a solution for storing unstructured data

  • Azure Blob Storage: Optimized for massive unstructured data (images, videos, logs).
  • Azure File Shares: Fully managed SMB/NFS file shares for legacy apps requiring file system semantics.
  • Data Lake Storage Gen2: Combines Blob Storage scalability with file system semantics for analytics.

Recommend a data storage solution to balance features, performance, and costs

  • Access Tiers:
    • Hot/Cool/Archive: Optimize costs based on access frequency.
  • Lifecycle Management: Automate tier transitions/deletion rules.
  • Performance Tiers:
    • Standard: General-purpose.
    • Premium: High IOPS for latency-sensitive apps (e.g., Azure File Shares Premium).

Recommend a data solution for protection and durability

  • Redundancy:
    • Geo-Redundant Storage (GRS): 6 copies across regions.
    • Zone-Redundant Storage (ZRS): 3 copies across zones.
  • Encryption: AES-256 encryption at rest and in transit.
  • Versioning/Soft Delete: Protect against accidental deletion.

3. Data Integration and Analysis

Recommend a solution for data integration

  • Azure Data Factory: Serverless ETL/ELT with 90+ connectors for hybrid workflows.
    • Key Features:
      • Code-free pipeline design.
      • Integration with Azure Synapse, Databricks, and on-premises systems.
    • Use Cases: Migrate SSIS packages, orchestrate data lakes.

Recommend a solution for data analysis

  • Azure Synapse Analytics: Unified analytics with SQL and Spark pools for big data.
  • Azure Databricks: Collaborative Apache Spark platform for AI/ML.
  • HDInsight: Managed Hadoop/Spark clusters for open-source analytics.
  • Azure Stream Analytics: Real-time processing for IoT/telemetry data with integration into Event Hubs, IoT Hub, and Power BI.

Summary Table

RequirementRecommended Solution(s)Key Features/Citations
Relational Data StorageAzure SQL Database, SQL Managed Instance, SQL Server on VMs, PostgreSQL/MySQLHyperscale for scalability, Serverless for cost, Defender for protection
Semi-Structured DataCosmos DB, Table Storage, Data Lake Gen2Schema-agnostic, OData queries
Unstructured DataBlob Storage, Azure File Shares, Data Lake Gen2Lifecycle management, tiered storage, SMB/NFS support
Data IntegrationAzure Data Factory90+ connectors, SSIS migration
Data AnalysisSynapse Analytics, Databricks, HDInsight, Stream AnalyticsUnified analytics, Spark optimization, real-time processing
Protection/DurabilityGRS/ZRS redundancy, Azure Key VaultEncryption, versioning, geo-failover

Summarised with Perplexity.

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© 2026 Andrei Bodea

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