Azure Storage: Tables
Author: Ronald Fung
Creation Date: 2 June 2023
Next Modified Date: 2 June 2024
A. Introduction
Azure Table storage is a service that stores non-relational structured data (also known as structured NoSQL data) in the cloud, providing a key/attribute store with a schemaless design. Because Table storage is schemaless, it’s easy to adapt your data as the needs of your application evolve. Access to Table storage data is fast and cost-effective for many types of applications, and is typically lower in cost than traditional SQL for similar volumes of data.
You can use Table storage to store flexible datasets like user data for web applications, address books, device information, or other types of metadata your service requires. You can store any number of entities in a table, and a storage account may contain any number of tables, up to the capacity limit of the storage account.
What is Table storage
Azure Table storage stores large amounts of structured data. The service is a NoSQL datastore which accepts authenticated calls from inside and outside the Azure cloud. Azure tables are ideal for storing structured, non-relational data. Common uses of Table storage include:
Storing TBs of structured data capable of serving web scale applications
Storing datasets that don’t require complex joins, foreign keys, or stored procedures and can be denormalized for fast access
Quickly querying data using a clustered index
Accessing data using the OData protocol and LINQ queries with WCF Data Service .NET Libraries
You can use Table storage to store and query huge sets of structured, non-relational data, and your tables will scale as demand increases.
B. How is it used at Seagen
As a biopharma research company using Microsoft Azure, you can use Azure Table Storage to store and manage large amounts of structured data in the cloud. Here are some ways you can use Azure Table Storage:
Data storage: Azure Table Storage can be used as a highly scalable and cost-effective storage solution for structured data, allowing you to store and manage large amounts of data in the cloud.
Analytics: Azure Table Storage can be used to store data for analytics purposes, allowing you to analyze large volumes of data and gain insights into your biopharma research efforts.
Data integration: Azure Table Storage can be used for data integration tasks, allowing you to move data between systems and perform data integration tasks.
Data processing: Azure Table Storage can be used for data processing tasks, allowing you to process data in the cloud and make data-driven decisions that support your biopharma research goals.
Security: Azure Table Storage provides advanced security features, helping to protect the privacy and security of your data.
Availability: Azure Table Storage provides high availability and disaster recovery capabilities, ensuring that your data is always accessible and protected.
Scalability: Azure Table Storage is highly scalable, allowing you to easily scale up or down as your data needs change.
Durability: Azure Table Storage provides high durability, ensuring that your data is always available and protected.
Overall, by leveraging Azure Table Storage, you can effectively store and manage large amounts of structured data in the cloud, and make data-driven decisions that support your biopharma research goals. By using Azure Table Storage for data storage, analytics, data integration, data processing, security, availability, scalability, and durability, you can effectively manage your research data and make data-driven decisions that support your biopharma research efforts.
C. Features
Azure Table Storage is a cloud-based NoSQL database service that provides a scalable and cost-effective solution for storing and managing large amounts of structured data in the cloud. Here are some of the key features of Azure Table Storage:
Scalability: Azure Table Storage is highly scalable, allowing you to easily store and manage large amounts of structured data in the cloud.
Cost-effectiveness: Azure Table Storage provides a cost-effective solution for storing and managing structured data, allowing you to pay only for the resources you use.
Data processing: Azure Table Storage can be used for data processing tasks, allowing you to process data in the cloud and make data-driven decisions that support your biopharma research goals.
Analytics: Azure Table Storage can be used to store data for analytics purposes, allowing you to analyze large volumes of data and gain insights into your biopharma research efforts.
Security: Azure Table Storage provides advanced security features, helping to protect the privacy and security of your data.
Availability: Azure Table Storage provides high availability and disaster recovery capabilities, ensuring that your data is always accessible and protected.
Durability: Azure Table Storage provides high durability, ensuring that your data is always available and protected.
Data integration: Azure Table Storage can be used for data integration tasks, allowing you to move data between systems and perform data integration tasks.
Performance: Azure Table Storage provides high performance data processing capabilities, allowing you to quickly and efficiently manage your structured data.
Schemaless: Azure Table Storage is schemaless, which means you don’t need to define a schema before storing data. This provides a high level of flexibility when storing and managing data.
Overall, by leveraging Azure Table Storage, you can effectively store and manage large amounts of structured data in the cloud, and make data-driven decisions that support your biopharma research goals. By using Azure Table Storage for scalability, cost-effectiveness, data processing, analytics, security, availability, durability, data integration, performance, and schemaless capabilities, you can effectively manage your research data and make data-driven decisions that support your biopharma research efforts.
D. Where Implemented
E. How it is tested
Testing Azure Table Storage involves verifying that the service is properly configured and that it is effectively storing and managing structured data in the cloud. Here are some steps you can take to test Azure Table Storage:
Verify configuration: Verify that Azure Table Storage is properly configured and integrated with your Azure account and applications and websites.
Test data storage: Test Azure Table Storage by storing and retrieving structured data, ensuring that the service is effectively storing and managing your data in the cloud.
Test data processing: Test Azure Table Storage by processing data in the cloud, ensuring that you can easily process data and make data-driven decisions that support your biopharma research goals.
Test analytics: Test Azure Table Storage by storing data for analytics purposes, ensuring that you can analyze large volumes of data and gain insights into your biopharma research efforts.
Test security: Test Azure Table Storage by verifying that the service provides advanced security features, helping to protect the privacy and security of your data.
Test availability: Test Azure Table Storage by verifying that the service provides high availability and disaster recovery capabilities, ensuring that your data is always accessible and protected.
Test durability: Test Azure Table Storage by verifying that the service provides high durability, ensuring that your data is always available and protected.
Test data integration: Test Azure Table Storage by verifying that the service can be used for data integration tasks, allowing you to move data between systems and perform data integration tasks.
Test scalability: Test Azure Table Storage by verifying that the service is highly scalable, allowing you to easily scale up or down as your data needs change.
Test performance: Test Azure Table Storage by verifying that the service provides high performance data processing capabilities, allowing you to quickly and efficiently manage your structured data.
Overall, testing Azure Table Storage involves verifying that the service is effectively storing and managing structured data in the cloud, and providing powerful data storage, data processing, analytics, security, availability, durability, data integration, scalability, and performance capabilities. By taking these steps, you can ensure that you are effectively using Azure Table Storage to manage your research data and make data-driven decisions that support your biopharma research efforts.
F. 2023 Roadmap
????
G. 2024 Roadmap
????
H. Known Issues
As with any software or service, there may be known issues or limitations with Azure Table Storage that users should be aware of. Here are some of the known issues with Azure Table Storage:
Limited querying capabilities: Azure Table Storage provides limited querying capabilities, which can make it difficult to retrieve specific data or perform complex queries.
Limited indexing capabilities: Azure Table Storage provides limited indexing capabilities, which can make it difficult to efficiently retrieve data.
Limited transaction support: Azure Table Storage provides limited transaction support, which can make it difficult to perform complex operations on data.
Limited geographic replication: Azure Table Storage provides limited geographic replication capabilities, which can impact the availability and durability of your data.
Limited consistency options: Azure Table Storage provides limited consistency options, which can impact the accuracy and reliability of your data.
Limited support for complex data types: Azure Table Storage provides limited support for complex data types, which can make it difficult to store and manage certain types of data.
Limited integration with other Azure services: Azure Table Storage provides limited integration with other Azure services, which can make it difficult to move data between systems and perform data integration tasks.
Scalability limitations: While Azure Table Storage is highly scalable, there may be limitations to how quickly and easily you can scale up or down to meet changing data needs.
Overall, while Azure Table Storage offers a powerful tool for storing and managing structured data in the cloud, users must be aware of these known issues and take steps to mitigate their impact. This may include carefully managing data querying and indexing, carefully configuring the service to meet the specific needs of their data workflows, and carefully monitoring data activity to ensure that the service is effectively managing data. By taking these steps, users can ensure that they are effectively using Azure Table Storage to manage their research data and make data-driven decisions that support their biopharma research efforts.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture