Azure Databricks Flashcards
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Read the first 7 Azure Databricks flashcards as text
Azure Databricks is primarily built on which open-source distributed computing framework?
Answer: Apache Spark
Azure Databricks is an analytics platform built on Apache Spark, which provides distributed data processing capabilities.
What collaborative feature do Azure Databricks workspaces provide for data teams?
Answer: Interactive notebooks supporting multiple languages
Azure Databricks provides interactive notebooks that support multiple languages (Python, Scala, SQL, R) and allow data engineers, data scientists, and analysts to collaborate.
Which data storage layer within Azure Databricks provides ACID transaction support and versioning for data lakes?
Answer: Delta Lake
Delta Lake is an open-source storage layer in Azure Databricks that brings ACID transactions, versioning, and schema enforcement to data lakes.
What is an Azure Databricks cluster?
Answer: A set of computation resources used to run notebooks and jobs
An Azure Databricks cluster is a set of computation resources (VMs) that run Apache Spark workloads including notebooks, jobs, and streaming applications.
Which programming languages are natively supported in Azure Databricks notebooks?
Answer: Python, Scala, SQL, and R
Azure Databricks notebooks natively support Python, Scala, SQL, and R, allowing data professionals from different backgrounds to collaborate in the same workspace.
How does Azure Databricks integrate with Azure Data Lake Storage Gen2?
Answer: It can mount ADLS Gen2 as a file system or access it directly via Spark
Azure Databricks can mount Azure Data Lake Storage Gen2 as a file system or access it directly via Spark APIs using service principal or managed identity authentication.
What is the primary advantage of using Azure Databricks for large-scale data processing compared to a single-node approach?
Answer: Ability to process data in parallel across multiple nodes
Azure Databricks distributes data processing across multiple cluster nodes in parallel using Apache Spark, enabling it to handle large-scale datasets much faster than single-node solutions.