Azure Batch: The Ideal Choice for Autoscaling, In-memory Caching, and Querying External Relational Stores | DP-203 Exam Answer
Question
You need to decide on the technology choice that your team should use for batch processing in Azure.
The requirements demand the technology to meet the following capabilities: Autoscaling In-memory caching of data Query from external relational stores Support for firewall Which of the following techniques would you choose?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect.
Azure Data Lake Analytics does not support Autoscaling and In-memory caching of data.
Option B is incorrect.
Azure Synapse Analytics does not support Autoscaling and Query from external relational stores.
Option C is correct.
HDInsight with Spark supports all the given capabilities: Autoscaling, In-memory caching of data, Query from the external relational store, and support for the firewall.
Option D is incorrect.
Azure Databricks supports firewall when integrated with VNET, Azure Databricks alone can't support the given capabilities.
To know more about batch processing technologies in Azure, please visit the below-given link:
Based on the requirements of autoscaling, in-memory caching of data, query from external relational stores, and support for firewall, the most suitable technology choice for batch processing in Azure is Azure Synapse Analytics.
Here is why:
Autoscaling: Azure Synapse Analytics provides the ability to automatically scale up or down compute resources based on demand. This ensures that processing can handle variable workloads and scale to meet the requirements of the data being processed.
In-memory caching of data: Azure Synapse Analytics also provides the ability to cache frequently accessed data in-memory using its integrated Apache Spark engine. This feature improves query performance and reduces the need to access data from disk.
Query from external relational stores: Azure Synapse Analytics allows querying external relational data stores such as SQL Server, Oracle, and Teradata. This enables integration with existing data sources and eliminates the need to move data to the Synapse Analytics workspace.
Support for firewall: Azure Synapse Analytics also provides support for network isolation through firewall rules. This ensures that data remains secure and only authorized users can access it.
Azure Data Lake Analytics is another technology that can be used for batch processing in Azure, but it does not provide in-memory caching of data or support for querying external relational data stores. Additionally, it is being deprecated in 2021.
Azure HDInsight with Spark and Azure Databricks are also suitable technologies for batch processing in Azure, but they do not provide support for firewall rules.
Therefore, based on the requirements provided, Azure Synapse Analytics is the most suitable choice for batch processing in Azure.
