AZ-305 Data Storage solutions

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

Summarised with Perplexity.