Provisioning a Databricks Cluster for Data Analytics and ETL on Azure
Question
Miguel is a Data Engineer and working on the setup of a Databricks Cluster on Azure.
The requirement is the cluster can be paused and restarted as required.
He's building data analytics, data science platform with Azure Databricks with Extract, Transform and Load (ETL) features.
What kind of Databricks cluster can he provision?
Answers
Explanations
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A. B. C. D.Correct Answer: D.
Miguel can provision a Multi-Node Cluster with Databricks on Azure that can be paused and restarted as required.
Databricks is a cloud-based, big data processing platform that offers a managed Apache Spark cluster service. Azure Databricks provides a fully managed Spark cluster service that enables customers to process large amounts of data at a high speed, with capabilities such as real-time stream processing, machine learning, and interactive SQL.
A Multi-Node Cluster is a type of Databricks cluster that consists of multiple nodes, each with its own CPU, RAM, and storage. Multi-Node Clusters are designed to handle large-scale data processing and analytical workloads. These clusters can be scaled up or down based on the workload requirements and can be paused and restarted as needed, making them ideal for use cases where processing needs fluctuate.
Job Clusters, on the other hand, are designed for running a single Spark job or a set of related jobs. They are typically short-lived and are not intended for long-running data analytics or machine learning workloads.
Single Node Clusters are intended for development and testing purposes, where a single node is sufficient to run the workload.
All-purpose clusters are a hybrid of multi-node and job clusters that can be used for a wide range of workloads. However, they do not provide the ability to pause and restart the cluster, making them less suitable for use cases where processing needs fluctuate.
In summary, for Miguel's use case, a Multi-Node Cluster is the best option because it provides the scalability needed for large-scale data processing and analytical workloads, and the ability to pause and restart the cluster as needed.
