Scale resources

When preparing for the DP-203 Data Engineering on Microsoft Azure exam, a key topic you’ll encounter is “Scale resources”. Scaling resources in Azure involves increasing or decreasing the capacity of your resources in response to changes in workload. This key Azure feature helps data engineers more efficiently manage resource utilization and hence, cost.

Exploring Subtopics of Scaling Resources

Let’s dive a little further into several subtopics related to scaling resources for successful data engineering:

Azure Autoscale

Autoscale, a built-in feature of Cloud Services, Mobile Services, Virtual Machines, and Websites, helps you achieve greater performance and resource utilization.

Let’s consider an example – Azure Functions. When you create them, they utilize the Consumption Plan for hosting. This plan automatically adds additional capacity as the load on your functions increases.

ScaleController Log: Host threshold 80.00 RPS, host load 157.00 RPS
Processing Capacity: 2 Instances
New Capacity: 3 Instances

Above is an example log that tells the system has increased capacity from 2 to 3 instances due to increased load.

Vertical and Horizontal Scaling

Vertical scaling, also known as “scaling up”, involves increasing the capacity of an existing resource. For example, when you scale up an Azure SQL Database, you increase compute power or storage space.

On the other hand, horizontal scaling, “scaling out”, involves adding more resources to distribute load. This can be adding more instances to an Azure Kubernetes Service (AKS) cluster, or adding more partitions to an Azure Cosmos DB container.

Proportional Scaling in Azure Stream Analytics

Stream Analytics Jobs can scale to process millions of events per second. Here, ‘Streaming Units’ are used to control the scale. By increasing the number of streaming units, you can achieve higher throughput.

For instance, your ASA job processes 1000 events/sec with 6 SU (Streaming Units), if events suddenly spike to 2000 events/sec, scaling up to 12 SU helps keep up with the stream.

Areas like data ingestion, real-time analytics, and data querying and serving are just a few applications where resource scaling comes in handy. Azure provides numerous services like Azure Autoscale, Azure SQL Database, or Azure Stream Analytics to support dynamic and efficient scaling. When taking the DP-203 exam, understanding these concepts will be crucial for your success. Always remember, efficient scaling is key to optimized performance and cost management in Azure and data engineering tasks in general.

Practice Test

True or False: The Azure Stream Analytics job can use multiple streaming units to enable parallel processing and handle a higher load.

• True
• False

Answer: True

Explanation: Azure Stream Analytics job can use multiple streaming units to offer high-throughput and low-latency parallel processing of data streams.

In Azure Synapse Analytics, which resizing method can alter the number of Data Warehousing Units (DWUs)?

• a) Vertical resizing
• b) Horizontal resizing
• c) Stretch Database resizing
• d) In-memory OLTP resizing

Answer: a) Vertical resizing

Explanation: In Azure Synapse Analytics, vertical resizing allows modification of the number of DWUs, adjusting the resources assigned to data warehousing processes.

True or False: Azure Data Factory supports auto-scaling.

• True
• False

Answer: False

Explanation: As of now, Azure Data Factory does not support auto-scaling. Customers need to adjust the pipeline parameters manually based on requirements.

Which of the following services can you use to scale Azure Cosmos DB resources?

• a) Azure Machine Learning
• b) Azure Synapse Analytics
• c) Azure Automation
• d) Azure DevOps

Answer: c) Azure Automation

Explanation: Azure Automation service allows for customization, maintenance, and scaling of resources, including Azure Cosmos DB.

When optimizing the performance of an Azure Cosmos DB database, which of the following modifications does not result in the allocation of more resources?

• a) Increasing the throughput
• b) Provisioning dedicated gateways
• c) Increasing the number of partitions
• d) Reducing the consistency level

Answer: d) Reducing the consistency level

Explanation: Reducing the consistency level does not add more resources, but modifies how the system handles requests and maintains consistency.

True or False: Azure SQL Database offers automatically scaling through Service tiers.

• True
• False

Answer: True

Explanation: Azure SQL Database provides automatic scalability via service tiers, which allow for the adjustment of resources to meet a workload’s demands.

Which of the following Azure services supports scale-out architecture?

• a) Azure App Service
• b) Azure Logic Apps
• c) Azure Batch
• d) All of the above

Answer: d) All of the above

Explanation: All these services support scale-out architecture, which distributes workloads across multiple instances to manage performance and resource utilization.

True or False: The number of streaming units in Azure Stream Analytics can be changed while the job is running.

• True
• False

Answer: False

Explanation: To change the number of streaming units, the job must first be stopped. Modifications cannot be made while the job is running.

In Azure Synapse Analytics, which of the following affect resource allocation?

• a) Workload classification
• b) Workload importance
• c) Resource classes
• d) All of the above

Answer: d) All of the above

Explanation: In Azure Synapse Analytics, resource allocation gets influenced by workload classification, importance and the types of resource classes used.

True or False: Azure Cosmos DB allows for scaling storage and throughput separately.

• True
• False

Answer: True

Explanation: In Azure Cosmos DB, customers can scale storage and throughput independently, as per the application requirements.

In Azure Functions, which plan type allows the scaling of resources with increased demand?

• a) Consumption plan
• b) Premium plan
• c) Dedicated plan
• d) Both a and b

Answer: d) Both a and b

Explanation: Both the Consumption plan and the Premium plan in Azure Functions dynamically scales resources based on the demand.

Which service in Azure does not support autoscaling?

• a) Azure Kubernetes Services
• b) Azure Logic Apps
• c) Azure Virtual Machines
• d) Azure Data Factory

Answer: d) Azure Data Factory

Explanation: As of now, Azure Data Factory does not natively support autoscaling.

Interview Questions

What is the Scale resource in Microsoft Azure?

Scale resources in Microsoft Azure refers to the capability to adjust the capacity to match the demand that a service experiences. Scaling can be done either vertically, by increasing the power, or horizontally, by adding more instances.

What is the difference between vertical scaling and horizontal scaling in Microsoft Azure?

Vertical scaling refers to increasing the capacity of a single resource, such as adding more memory or CPU to a virtual machine. Horizontal scaling, on the other hand, involves adding more instances of a resource to distribute the load evenly.

What is Azure Auto Scaling?

Azure Auto Scaling is a service that automatically adjusts the number of compute resources that your application uses based on the demand pattern, thereby helping you maintain performance and optimize costs.

What components are needed to set up Azure Auto Scaling?

Key components needed in Azure Auto Scaling are Scale set that consists a set of identical VMs and Scaling rules which define the conditions that trigger scaling actions.

When should you consider vertical scaling in Azure?

Vertical scaling should be considered when the demand pattern is consistent and predictable, and the application does not require a high level of fault tolerance or availability.

How does horizontal scaling support high availability in Azure?

Horizontal scaling support high availability by distributing the workload across multiple instances. If one instance fails, the others continue to handle user requests, thereby minimizing downtime.

How can you enable autoscale in Microsoft Azure?

Autoscale in Azure can be enabled in the settings of an Azure Scale Set or App service. You can define the minimum and maximum instances, as well as scale out and scale in rules.

What are some limitations of vertical scaling in Azure?

Vertical scaling is limited by the maximum capacity of a single machine, which may not be sufficient to handle high workloads. Additionally, it can cause downtime during the process of adding more resources.

Which Azure service provides automatic scale up and down capabilities?

Azure Autoscale provides automatic scale up and down capabilities based on user-defined rules and workload patterns.

What is the role of Azure Monitor in scale resources?

Azure Monitor plays a vital role in scaling resources. It collects and analyzes performance data, generating alerts when specific metrics reach certain thresholds. These alerts can trigger autoscale rules.

What is a scale set in Azure?

A scale set in Azure is a group of identical, load-balanced, and autoscaling virtual machines.

How does Azure determine when to scale out or scale in resources?

Azure determines when to scale in or scale out resources based on rules set by the administrator. These rules are typically based on metrics such as CPU utilization, memory usage, or queue length.

Can Azure Autoscale manage the number of VM instances in a Virtual Machine Scale Set?

Yes. Azure Autoscale can manage the number of VM instances in a Virtual Machine Scale Set based on demand.

What is the “cooldown” parameter in Azure Autoscaling?

The “cooldown” parameter in Azure Autoscaling specifies the amount of time to wait after the last scale operation before another can occur. This is to ensure the system is stable before performing another scaling operation.

In Azure Data Factory, where can you adjust scale-out settings to speed up your data movement?

In Azure Data Factory, you can adjust scale-out settings in the Dataflow debug settings pane. By increasing the number of compute units, you can speed up your data movement.