Your company has assigned you the task of choosing the right batch processing solution from Azure for your new product.
There are certain capabilities which you are looking for.
They are Auto-scaling capability In-memory caching for data Azure AD based authentication Which of the following services is the best choice?
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answer: E
Here we should compare the abilities and eliminate the ones without those capabilities in the list.
Azure Data Lake Analytics and Azure Synapse don't have the auto-scaling capability.
And In -memory caching ability is not available for HDInsight with Hive.
Finally, HDInsight with Spark doesn't support Azure AD authentication.
So, Azure databricks is a suitable choice here.
Options A and Bare incorrect: Azure Data Lake Analytics and Azure Synapse don't have the auto-scaling capability.
Option C is incorrect: HDInsight with Spark doesn't support Azure AD authentication.
Option D is incorrect: In-memory caching ability is not available for HDInsight with Hive.
Option E is correct: It has all the capabilities listed.
To know more, please refer to the docs below:
Out of the options provided, the best choice for a batch processing solution with auto-scaling capability, in-memory caching for data, and Azure AD-based authentication is Azure Synapse.
Azure Synapse is a fully managed analytics service that brings together big data and data warehousing, with the ability to perform data integration, exploration, and batch and streaming data processing. It is built on Apache Spark and offers a unified experience with a workspace that allows for easy collaboration between data engineers, data scientists, and business analysts.
Here's how Azure Synapse meets the requirements:
Auto-scaling capability: Azure Synapse has auto-scaling built into its architecture. This means that as the workload increases, the system automatically scales up to accommodate the increased demand. When the workload decreases, it scales down to save costs. This allows for better resource utilization and ensures that the system is always performing optimally.
In-memory caching for data: Azure Synapse uses Apache Spark, which has a built-in feature for in-memory caching called Spark Cache. Spark Cache can be used to cache frequently accessed data in memory, allowing for faster access times and reducing the need for repeated reads from disk.
Azure AD-based authentication: Azure Synapse integrates with Azure Active Directory (Azure AD) for authentication and access control. This allows for centralized management of user accounts and access policies, ensuring that only authorized users can access the system.
Azure Data Lake Analytics (A) is a batch processing service that uses U-SQL, a SQL-like language for querying and processing data. While it has auto-scaling capability, it does not have in-memory caching or Azure AD-based authentication.
HDInsight with Spark (C) and Azure Databricks (E) both use Apache Spark for batch processing and have auto-scaling and in-memory caching capabilities. However, neither offers Azure AD-based authentication out-of-the-box.
HDInsight with Hive (D) is a batch processing service that uses Apache Hive for querying and processing data. While it has auto-scaling capability, it does not have in-memory caching or Azure AD-based authentication.