Have you ever faced difficulties in choosing a proper data store for your application with multiple workloads?
Azure offers a number of managed data storage solutions, each providing different features and capabilities. If your application consists of multiple workloads, evaluate each workload separately. A complete solution may incorporate multiple data stores.
following flowchart will help you to select a Azure managed data store.
- Blob Storage: Blob Storage is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a specific data model or definition, such as text or binary data.
Use Blob Storage for the following needs:
- Serving images or documents directly to a browser.
- Storing files for distributed access.
- Streaming video and audio.
- Writing to log files.
- Storing data for backup and restore, disaster recovery, and archiving.
- Storing data for analysis by an on-premises or Azure-hosted service.
- Data Lake Storage Gen2: Blob Storage supports Data Lake Storage Gen2, Microsoft's enterprise big data analytics solution for the cloud. Data Lake Storage Gen2 offers a hierarchical file system, with the advantages of Blob Storage, including low-cost tiered storage, high availability, strong consistency, and disaster recovery capabilities.
- Azure disk storage: Azure disk storage offers persistent, high-performance block storage to power Azure Virtual Machines. Azure disks are highly durable, secure, and offer the industry's only single-instance service-level agreement (SLA) for virtual machines that use?Premium SSD or Ultra Disk Storage. Azure disks provide high availability with availability sets and Availability Zones for your Azure Virtual Machines fault domains. Azure manages disks as a top-level resource. Azure Resource Manager capabilities are provided, such as Azure role-based access control (Azure RBAC), policy, and tagging by default.
- Azure Files: Azure Files provides fully managed, native SMB file shares, without the need to run a virtual machine. You can mount an Azure Files share as a network drive to any Azure virtual machine or on-premises computer.
- Azure Cosmos DB: Azure Cosmos DB is a fully managed NoSQL database for modern app development. Single-digit millisecond response times, and automatic and instant scalability, guarantee speed at any scale.
- Azure Cosmos DB API: Azure Cosmos DB offers multiple database APIs, which include the Core (SQL) API, API for MongoDB, Cassandra API, Gremlin API, and Table API. By using these APIs, you can model real world data using documents, key-value, graph, and column-family data models. Follow below flow chart to choose a CosmosDB API for your data store.
- Core (SQL) API: Azure Cosmos DB SQL API accounts provide support for querying items using the Structured Query Language (SQL) syntax, one of the most familiar and popular query languages to query JSON objects. This API stores data in document format.
- API for MongoDB: This API is a great choice if you want to use the broader MongoDB ecosystem and skills, without compromising on using Azure Cosmos DB features such as scaling, high availability, geo-replication, multiple write locations, automatic and transparent shard management, transparent replication between operational and analytical stores, and more. This API stores data in a document structure, via BSON format. It is compatible with MongoDB wire protocol; however, it does not use any native MongoDB related code.
- Cassandra API: This API stores data in column-oriented schema. Apache Cassandra offers a highly distributed, horizontally scaling approach to storing large volumes of data while offering a flexible approach to a column-oriented schema. Cassandra API in Azure Cosmos DB aligns with this philosophy to approaching distributed NoSQL databases. Cassandra API is wire protocol compatible with the Apache Cassandra. You should consider Cassandra API if you want to benefit the elasticity and fully managed nature of Azure Cosmos DB and still use most of the native Apache Cassandra features, tools, and ecosystem. This means on Cassandra API you don't need to manage the OS, Java VM, garbage collector, read/write performance, nodes, clusters, etc.
- Gremlin API: The Azure Cosmos DB Gremlin API combines the power of graph database algorithms with highly scalable, managed infrastructure. This API allows users to make graph queries and stores data as edges and vertices. Use this API for scenarios involving dynamic data, data with complex relations, data that is too complex to be modeled with relational databases, and if you want to use the existing Gremlin ecosystem and skills.
- Table API: This API stores data in key/value format. If you are currently using Azure Table storage, you may see some limitations in latency, scaling, throughput, global distribution, index management, low query performance.
- Azure Search: Azure Cognitive Search is the only cloud search service with built-in AI capabilities that enrich all types of information to help you identify and explore relevant content at scale. Use cognitive skills for vision, language and speech or use custom machine learning models to uncover insights from all types of content.
- Time Series Insights: Azure Time Series Insights is a fully managed analytics, storage, and visualization service for managing IoT-scale time-series data in the cloud. It provides massively scalable time-series data storage and enables you to explore and analyze billions of events streaming in from all over the world in seconds.
- Azure Cache for Redis: Azure Cache for Redis provides an in-memory data store based on the?Redis?software. Redis improves the performance and scalability of an application that uses backend data stores heavily. It's able to process large volumes of application requests by keeping frequently accessed data in the server memory, which can be written to and read from quickly. Redis brings a critical low-latency and high-throughput data storage solution to modern applications.
