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What should you do?
Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
✑ support Python and Scala
✑ compose data storage, movement, and processing services into automated data pipelines
✑ the same tool should be used for the orchestration of both data engineering and data science
✑ support workload isolation and interactive workloads
✑ enable scaling across a cluster of machines
You need to create the environment.
What should you do?
A . Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
B . Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
C . Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
D . Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Answer: B
Explanation:
In Azure Databricks, we can create two different types of clusters.
✑ Standard, these are the default clusters and can be used with Python, R, Scala and SQL
✑ High-concurrency
Azure Databricks is fully integrated with Azure Data Factory.
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