Microsoft Azure Data FundamentalsDescribe how to work with non-relational data on AzureHard
A data analytics team needs to store petabytes of raw sensor data from IoT devices for long-term analysis. The data is ingested continuously, and analysts require a file system-like interface with hierarchical directories for organizing the data. They also need strong consistency for file operations to ensure data integrity during complex analytical workloads. Which Azure non-relational data service is the most suitable?
- AAzure Blob Storage (Standard)
- BAzure Cosmos DB (MongoDB API)
- CAzure Data Lake Storage Gen2
- DAzure Table Storage
Show answer & explanationAnswer & explanation
Correct answer: C. Azure Data Lake Storage Gen2
Azure Data Lake Storage Gen2 is specifically designed for big data analytics workloads. It provides a hierarchical namespace for file system semantics, petabyte-scale storage, and strong consistency for file operations, which are critical for maintaining data integrity during complex analytical processing. It's built on Azure Blob Storage but adds these enhanced capabilities.
Why the other options are wrong
- A. Azure Blob Storage (Standard) lacks the hierarchical namespace and strong consistency for file operations needed for complex analytics workloads on petabyte-scale data.
- B. Azure Cosmos DB (MongoDB API) is a document database, not suitable for storing raw, unstructured petabyte-scale sensor data with file system semantics.
- D. Azure Table Storage is a key-value store, not suitable for file system-like storage of raw sensor data at petabyte scale.
Azure Data Lake Storage Gen2
A set of capabilities dedicated to big data analytics, built on Azure Blob Storage. It provides file system semantics, file-level security, and optimized scale.
- Petabyte-scale storage for big data.
- Hierarchical namespace for file system organization.
- Strong consistency for file operations.
Memory trick: Lakes are vast and organized for data analysis.