Azure Popular Storage options
Azure Blob Storage: Optimized for storing massive amounts of unstructured data such as text or binary data.
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Uses
Storing files for distributed access
Streaming video and audio
Storing data for backup and restore, disaster recovery, and archiving
Storing data for analysis by an on-premises or Azure-hosted service
Tiers
Cool: For data that is infrequently accessed and stored for at least 30 days.
Hot: For data that is accessed frequently.
Archive: For data that is rarely accessed and stored for at least 180 days with flexible latency requirements.
Types
Append Blobs: Optimized for append operations, such as logging.
Block Blobs: Efficient for uploading large amounts of data.
Page Blobs: Efficient for random read/write operations and storing virtual machine (VM) disks.
Azure Files:Fully managed file shares in the cloud that are accessible via the SMB and NFS protocols.
Uses
File sharing between multiple virtual machines
Storing configuration files, logs, and other application data
Replacing or supplementing on-premises file servers
Migrating legacy applications that rely on traditional file shares
Features
Integration with Active Directory (AD) for access control
Supports both SMB and NFS protocols
Azure Disk Storage: High-performance, durable block storage for use with Azure Virtual Machines.
Types
Ultra Disk: High throughput and low latency for mission-critical applications.
Premium SSD: High-performance SSD-based storage.
Standard SSD: Cost-effective SSD storage for less critical workloads.
Standard HDD: Affordable and reliable storage for backup, non-critical data, and infrequently accessed data.
Uses
High-performance workloads such as databases (SQL, NoSQL) and big data analytics
Persistent storage for VMs
Business-critical applications requiring high IOPS and low latency
Azure Table Storage: A NoSQL key-value store for rapid development using massive semi-structured datasets.
Uses
Storing structured, non-relational data
High-throughput and low-latency access to large datasets
Storing user data for web applications
Storing device data for IoT applications
Features
Cost-effective storage for large volumes of data
Schemaless design, allowing for flexible data structures
Azure Queue Storage: A message queuing service for storing large numbers of messages that can be accessed from anywhere in the world.
Uses
Decoupling components in cloud applications to improve reliability and scalability
Managing asynchronous processing and task scheduling
Building distributed and scalable systems
Features
Guarantees delivery of messages in the queue
Supports large message sizes and high throughput
Azure Data Lake Storage (ADLS): A scalable and secure data lake service built on top of Azure Blob Storage designed to handle large volumes of data from various sources and support big data analytics workloads.
Uses
Big Data Analytics: Aggregate and analyze large datasets for insights.
Machine Learning: Store and process data for training models.
IoT Data Storage: Collect and analyze data from IoT devices.
Data Archiving: Store historical data cost-effectively.
Data Ingestion and Processing: Ingest and transform data from various sources.
Features
Scalability: Supports petabytes of data, automatically scales with workload.
Security: Encrypts data at rest and in transit, with fine-grained access control.
Integration: Seamlessly integrates with Azure Data Factory, Azure Databricks, and Azure HDInsight.
High Performance: Optimized for analytics with low-latency data access.
Cost-Effective: Flexible pricing, tiered storage for cost optimization.
Data Management: Hierarchical namespace, automated data lifecycle policies.
Interoperability: Supports open data formats, provides robust SDKs and APIs.