Azure Data Engineer Associate (DP-203)
1 Design and implement data storage
1-1 Design data storage solutions
1-1 1 Identify data storage requirements
1-1 2 Select appropriate storage types
1-1 3 Design data partitioning strategies
1-1 4 Design data lifecycle management
1-1 5 Design data retention policies
1-2 Implement data storage solutions
1-2 1 Create and configure storage accounts
1-2 2 Implement data partitioning
1-2 3 Implement data lifecycle management
1-2 4 Implement data retention policies
1-2 5 Implement data encryption
2 Design and implement data processing
2-1 Design data processing solutions
2-1 1 Identify data processing requirements
2-1 2 Select appropriate data processing technologies
2-1 3 Design data ingestion strategies
2-1 4 Design data transformation strategies
2-1 5 Design data integration strategies
2-2 Implement data processing solutions
2-2 1 Implement data ingestion
2-2 2 Implement data transformation
2-2 3 Implement data integration
2-2 4 Implement data orchestration
2-2 5 Implement data quality management
3 Design and implement data security
3-1 Design data security solutions
3-1 1 Identify data security requirements
3-1 2 Design data access controls
3-1 3 Design data encryption strategies
3-1 4 Design data masking strategies
3-1 5 Design data auditing strategies
3-2 Implement data security solutions
3-2 1 Implement data access controls
3-2 2 Implement data encryption
3-2 3 Implement data masking
3-2 4 Implement data auditing
3-2 5 Implement data compliance
4 Design and implement data analytics
4-1 Design data analytics solutions
4-1 1 Identify data analytics requirements
4-1 2 Select appropriate data analytics technologies
4-1 3 Design data visualization strategies
4-1 4 Design data reporting strategies
4-1 5 Design data exploration strategies
4-2 Implement data analytics solutions
4-2 1 Implement data visualization
4-2 2 Implement data reporting
4-2 3 Implement data exploration
4-2 4 Implement data analysis
4-2 5 Implement data insights
5 Monitor and optimize data solutions
5-1 Monitor data solutions
5-1 1 Identify monitoring requirements
5-1 2 Implement monitoring tools
5-1 3 Analyze monitoring data
5-1 4 Implement alerting mechanisms
5-1 5 Implement logging and auditing
5-2 Optimize data solutions
5-2 1 Identify optimization opportunities
5-2 2 Implement performance tuning
5-2 3 Implement cost optimization
5-2 4 Implement scalability improvements
5-2 5 Implement reliability improvements
Implement Cost Optimization

Implement Cost Optimization

Key Concepts

Cost Management

Cost management involves tracking and optimizing the costs associated with Azure resources. This includes monitoring resource usage, setting budgets, and optimizing resource allocation. Azure provides tools like Azure Cost Management and Azure Advisor for cost management.

Example: A retail company might use Azure Cost Management to track the costs of its data storage and processing resources. By analyzing usage patterns, the company can identify opportunities to reduce costs, such as resizing underutilized virtual machines.

Analogy: Consider cost management as managing a household budget. You need to track expenses, set limits, and find ways to save money without compromising on essential services.

Resource Utilization

Resource utilization monitoring tracks the consumption of computational, storage, and network resources. This helps in optimizing resource allocation and cost management. Azure provides tools like Azure Monitor and Azure Cost Management for resource utilization monitoring.

Example: A marketing team might monitor the resource utilization of its data processing jobs to ensure that they are not overloading the system and incurring unnecessary costs.

Analogy: Consider resource utilization monitoring as managing the inventory of a warehouse. You need to track the usage of each item (resource) to ensure that you have enough stock (resources) without overstocking (overutilization).

Reserved Instances

Reserved Instances allow you to commit to using Azure resources for a specified term (1 or 3 years) in exchange for significant discounts. This is particularly useful for workloads with predictable usage patterns. Azure provides options for Reserved Instances on virtual machines, SQL databases, and other services.

Example: A financial institution might purchase Reserved Instances for its virtual machines to reduce costs over a three-year period, knowing that its transaction processing workloads are consistent and predictable.

Analogy: Think of Reserved Instances as buying a season pass to a theme park. By committing to multiple visits (resource usage) upfront, you get a discounted rate compared to paying for each visit individually.

Auto-Scaling

Auto-Scaling involves dynamically adjusting the number of resources based on demand. This ensures that you only pay for what you use and helps in managing costs. Azure provides tools like Azure Kubernetes Service (AKS) and Azure Virtual Machine Scale Sets for auto-scaling.

Example: An e-commerce platform might use Azure Auto-Scale to automatically increase the number of application instances during peak shopping periods. Azure Load Balancer ensures that incoming traffic is distributed evenly across these instances.

Analogy: Think of auto-scaling as adjusting the number of lanes on a highway during rush hour. You need to add more lanes (scale up) to handle increased traffic and ensure that traffic is evenly distributed across all lanes (load balancing).

Right-Sizing

Right-sizing involves selecting the appropriate size and type of Azure resources to meet performance requirements while minimizing costs. This includes resizing virtual machines, choosing the right storage tiers, and optimizing network configurations. Azure Advisor provides recommendations for right-sizing.

Example: A retail company might use Azure Advisor to identify underutilized virtual machines and resize them to smaller instances, reducing costs without compromising performance.

Analogy: Consider right-sizing as choosing the right size of a car for your family. You need a vehicle that comfortably fits everyone (meets performance requirements) without being unnecessarily large (incurring higher costs).