12 Questions
What is the primary purpose of using Azure Data Factory for data migration?
To easily scale up processing power and move data in a serverless manner with high performance, resilience, and scalability.
Which of the following is NOT a key characteristic of Azure Data Factory's data migration capabilities?
Ability to fully utilize the network and storage bandwidth to achieve the highest volume of data movement throughput.
Which of the following data sources can Azure Data Factory be used to migrate from?
Both (a) and (b).
What is the primary pricing model for using Azure Data Factory for data migration?
Pay-as-you-go based on the amount of data processed.
Which of the following is NOT a key benefit of using Azure Data Factory for data migration?
Ability to automatically scale up and down processing power as needed.
Which of the following scenarios is Azure Data Factory NOT well-suited for?
Real-time data streaming and analytics from IoT devices to Azure.
Which of the following statements about Azure Data Factory is NOT true?
It can only perform one-time historical data loads.
Which of the following is NOT a key consideration when choosing between online and offline data migration?
Cost of the migration tool
According to the information provided, which of the following statements is correct about using Azure Data Factory for online migration?
It can achieve both historical data loading and incremental feeds end-to-end through a single tool.
What is the purpose of the table shown in the text?
To calculate the expected migration time for different data sizes and network bandwidths.
Which of the following statements is true about the self-hosted Integration Runtime (IR) in Azure Data Factory?
It is required for moving data for data lake and warehouse endpoints inside Azure Virtual Network (VNet) or behind a firewall.
Which of the following security features is NOT mentioned in the text as being provided by Azure Data Factory?
Role-based access control (RBAC) for fine-grained access management
Study Notes
Azure Data Factory for Data Migration
- The primary purpose of using Azure Data Factory for data migration is to integrate and migrate data from various sources to different destinations, such as cloud-based storage, on-premises storage, or SaaS applications.
Key Characteristics of Azure Data Factory
- A key characteristic of Azure Data Factory's data migration capabilities is scalability, allowing it to handle large volumes of data.
Supported Data Sources
- Azure Data Factory can be used to migrate data from various sources, including on-premises storage, cloud-based storage, and SaaS applications.
Pricing Model
- The primary pricing model for using Azure Data Factory for data migration is based on the number of pipeline runs, data flows, and data integration units (DIUs) consumed.
Benefits of Azure Data Factory
- A key benefit of using Azure Data Factory for data migration is its ability to handle complex data transformations and integrations.
Limitations of Azure Data Factory
- Azure Data Factory is not well-suited for real-time data migration scenarios that require low latency and high throughput.
Integration Runtime (IR)
- The self-hosted Integration Runtime (IR) in Azure Data Factory allows users to lift and shift existing data integration workloads to the cloud.
- It provides a secure and managed way to run data integration workloads on-premises or in virtual networks.
Security Features
- Azure Data Factory provides various security features, including data encryption, authentication, and authorization, to ensure the secure migration of data.
- It does not provide two-factor authentication as a security feature.
Test your knowledge on using Azure Data Factory to migrate data from data lakes or data warehouses to Azure. Explore topics like data movement, data science, real-time analytics, and reporting. Get familiar with Microsoft Fabric for enterprise analytics solutions.
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