Microsoft Certified Azure Data Engineer Associate · Free Practice Question Medium
Question 28
Knowing now the different concepts of spark it is imperative to understand how it fits in with the different Data services on Azure.
Which of the following is best described by:
“Organizations that don’t have existing spark implementations yet, get the functionality to spin up a spark cluster to meet data engineering needs without the overhead of the other Spark Platforms. Data Engineers, Data scientist, Data Platform Experts, and Data Analyst can come together within Synapse Analytics where the Spark cluster is spun up quickly to meet the needs. It provides scale in an efficient way for Spark Clusters and integrates with the one stop shop Data warehousing platform of Synapse.”
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A
HDI
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B
Azure Databricks
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C
Spark Pools in Azure Synapse Analytics
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D
Apache Spark
Reveal correct answer
Correct answer: C
Explanation
There are two concepts within Apache Spark Pools in Azure Synapse Analytics, namely Spark pools and Spark Instances. In short, they do the following:
Spark Pools:
Exists as Metadata
Creates a Spark Instance
No costs associated with creating Pool
Permissions can be applied
Best practices
Spark Instances:
Created when connected to Spark Pool, Session, or Job
Multiple users can have access
Reusable
Knowing now the different concepts of spark it is imperative to understand how it fits in with the different Data services on Azure. Below is a table where "the when to use what" is outlined:
Spark Pools in Azure Synapse Analytics: Spark in Azure Synapse Analytics is a capability of Spark embedded in Azure Synapse Analytics in which organizations that don’t have existing spark implementations yet, get the functionality to spin up a spark cluster to meet data engineering needs without the overhead of the other Spark Platforms listed. Data Engineers, Data scientist, Data Platform Experts, and Data Analyst can come together within Synapse Analytics where the Spark cluster is spun up quickly to meet the needs. It provides scale in an efficient way for Spark Clusters and integrates with the one stop shop Data warehousing platform of Synapse.
Apache Spark: Apache Spark is an open-source memory optimized system for managing big data workloads, which is used when you want a spark engine for big data processing or data science where you don’t mind that there is no SLA provided. Usually it is of interest of Open Source Professionals and the reason for Apache spark is to overcome the limitations of what was known as SMP systems for big data workloads.
HDI: HDI is an implementation by Microsoft of Open Source Spark, managed on the Azure Platform. You can use HDI for a spark environment when you are aware of the benefits of Apache Spark in its OSS form, but you want a SLA. Usually this of interest of Open Source Professionals needing an SLA as well as Data Platform experts experienced with Microsoft.
Azure Databricks: Azure Databricks is a managed Spark as a Service propriety Solution that provides an end to end data engineering/data science platform as a solution. Azure Databricks is of interest for Data Engineers and Data Scientists, working on big data projects daily because it provides the whole platform in which you have the ability to create and manage the big data/data science pipelines/projects all on one platform.
A. HDI (Azure HDInsight) is a cloud-based service that makes it easy to process and analyze large data sets using popular open-source frameworks such as Apache Hadoop, Spark, Hive, and HBase. While HDInsight supports Apache Spark, it does not provide the same level of integration and efficiency for spinning up Spark clusters within Synapse Analytics as described in the scenario.
B. Azure Databricks is an Apache Spark-based analytics platform optimized for Azure that provides collaborative and interactive data science and engineering capabilities. While Azure Databricks offers advanced features for data processing and analytics, it is a separate platform from Synapse Analytics and does not offer the same level of integration and efficiency for spinning up Spark clusters within the Synapse environment.
C. Spark Pools in Azure Synapse Analytics allow organizations without existing Spark implementations to quickly spin up a Spark cluster within Synapse Analytics. This enables Data Engineers, Data Scientists, Data Platform Experts, and Data Analysts to collaborate and meet their data engineering needs without the overhead of other Spark platforms. It provides efficient scalability for Spark clusters and integrates seamlessly with the data warehousing platform of Synapse.
D. Apache Spark is a powerful open-source distributed computing system that provides a unified analytics engine for big data processing. While it is a key component in many data engineering and analytics workflows, it does not specifically offer the seamless integration and scalability features described in the scenario.
Discussion
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