Azure Real-Time and Batch Analytics Flashcards
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Read the first 6 Azure Real-Time and Batch Analytics flashcards as text
What Azure Synapse Analytics feature enables querying operational Azure Cosmos DB data using Spark or SQL without impacting the transactional workload?
Answer: Synapse Link for Azure Cosmos DB
Azure Synapse Link for Cosmos DB automatically syncs data to the Synapse analytical store, allowing Spark and SQL queries without any ETL or impact on Cosmos DB throughput.
In Azure Databricks, what does the term 'cluster auto-scaling' mean?
Answer: Dynamically adding or removing worker nodes based on workload demand
Auto-scaling in Databricks dynamically adjusts the number of worker nodes between configured min and max values based on current job demands.
Which file format is recommended for storing large analytical datasets in Azure Data Lake Storage for maximum query performance with columnar compression?
Answer: Parquet
Parquet is a columnar storage format that provides efficient compression and encoding, enabling fast analytical queries by reading only required columns.
What is the key difference between Azure Event Hubs and Azure Service Bus for data platform scenarios?
Answer: Event Hubs is optimized for high-throughput event streaming; Service Bus is for reliable message queuing with ordering guarantees
Event Hubs is designed for telemetry streaming at millions of events/second, while Service Bus provides ordered, reliable messaging with features like dead-lettering for transactional scenarios.
In Azure Synapse Analytics, which pool type should you use for interactive, ad-hoc SQL queries over data in ADLS Gen2 without pre-provisioning compute?
Answer: Serverless SQL pool
The Synapse serverless SQL pool enables pay-per-query T-SQL analytics over ADLS Gen2 files without provisioning or managing dedicated compute resources.
Which Azure service enables you to build real-time dashboards and reports from streaming data ingested through Azure Event Hubs?
Answer: Power BI with streaming datasets
Power BI streaming datasets accept real-time data pushes (e.g., from Stream Analytics) and update dashboards live without data being stored first.
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