This article provides an overview of the Azure database solutions described in Azure Architecture Center.
Azure Database solutions include both traditional relational database management system (RDBMS) and big data solutions.
RDBMS workloads include online transaction processing (OLTP) and online analytical processing (OLAP). Data from multiple sources in the organization can be consolidated into a data warehouse. You can use an extract, transform, and load (ETL) process to move and transform the source data.
A big data architecture is designed to handle the ingestion, processing, and analysis of large or complex data. Big data solutions typically involve a large amount of non-relational data, which traditional RDBMS systems aren’t well suited to store. This type of database is referred to as NoSQL (not only SQL).
This article provides resources to learn about Azure databases. It outlines paths to implement the architectures that meet your needs, and best practices to keep in mind as you design your solutions.
There are many architectures for you to draw from to address your database needs. We also provide solution ideas for you to build on, which include links to all the components you need.
Learn about databases on Azure
If you’re new to databases on Azure, the best place to start is Microsoft Learn. On this free online training platform, you’ll find videos and tutorials that offer hands-on learning. Microsoft Learn offers learning paths that are based on your job role, such as developer or data analyst.
Here are some Learn modules you might find useful:
- Choose a data storage approach in Azure
- Design your migration to Azure
- Deploy Azure SQL Database
- Explore Azure database and analytics services
- Secure your Azure SQL Database
Path to production
To find options helpful for dealing with relational data, consider these resources:
- To learn about resources for gathering data from multiple sources and how to and apply data transformations within the data pipeline, see Extract, transform, and load (ETL).
- To learn about Online analytical processing (OLAP), which organizes large business databases and supports complex analysis, see Online analytical processing.
- Online transaction processing systems record business interactions as they occur. For more information, see Online transaction processing (OLTP).
A non-relational database doesn’t use the tabular schema of rows and columns. For more information, see Non-relational data and NoSQL.
To learn about data lakes, which hold a large amount of data in its native, raw format, see Data lakes.
A big data architecture can handle ingestion, processing, and analysis of data that is too large or too complex for traditional database systems.
- For more information, see Big data architectures.
- To learn about designing a system that scales well as data grows, see Build a scalable system for massive data.
- To learn more about Azure Databricks, an Apache Spark–based analytics service for big data analytics and AI solutions, see Monitoring Azure Databricks.
A hybrid cloud is an IT environment that combines public cloud and on-premises datacenters. For more information, see Extend on-premises data solutions to the cloud.
Azure Cosmos DB is a fully managed NoSQL database service for modern app development. For more information, see Azure Cosmos DB resource model.
To learn about the options for transferring data to and from Azure, see Transfer data to and from Azure.
Review these best practices when designing your solutions.
|Data management patterns||Data management is the key element of cloud applications. It influences most quality attributes.|
|Transactional Outbox pattern with Azure Cosmos DB||Learn how to use the Transactional Outbox pattern for reliable messaging and guaranteed delivery of events.|
|Distribute your data globally with Azure Cosmos DB||To achieve low latency and high availability, some applications need to be deployed in datacenters that are close to their users.|
|Use the best data store for the job||Pick the storage technology that is the best fit for your data and how it will be used.|
|Security in Azure Cosmos DB||Security best practices help prevent, detect, and respond to database breaches.|
|Secure data solutions||Address concerns around increased accessibility to data in the cloud and how to secure it.|
|Continuous backup with point-in-time restore in Azure Cosmos DB||Learn about Azure Cosmos DB’s point-in-time restore feature.|
|Achieve high availability with Cosmos DB||Cosmos DB provides multiple features and configuration options to achieve high availability.|
|High availability for Azure SQL Database and SQL Managed Instance||The database shouldn’t be a single point of failure in your architecture.|
Azure SQL Database security baselines
Security is a vital part of any database solution.
- Azure security baseline for Azure SQL Database
- Azure security baseline for Azure Database Migration Service
There are many options for technologies to use with Azure Databases. These articles help you choose the best technologies for your needs.
- Choose an analytical data store in Azure
- Choose a data analytics technology in Azure
- Choose a batch processing technology in Azure
- Choose a big data storage technology in Azure
- Choose a data pipeline orchestration technology in Azure
- Choose a real-time message ingestion technology in Azure
- Choose a search data store in Azure
- Choose a stream processing technology in Azure
Stay current with databases
Refer to Azure updates to keep current with Azure Databases technology.
These architectures use database technologies.
- SQL Managed Instance with customer-managed keys
- Optimized storage with logical data classification
- Globally distributed applications using Cosmos DB
Here are some other resources:
- Adatum Corporation scenario for data management and analytics in Azure
- Lamna Healthcare scenario for data management and analytics in Azure
- Optimize administration of SQL Server instances
- Processing free-form text for search
- Relecloud scenario for data management and analytics in Azure
- Working with CSV and JSON files for data solutions
These solution ideas are some of the example approaches that you can adapt to your needs.
- Data cache
- Enterprise data warehouse
- Loan credit risk and default modeling
- Mining equipment monitoring
- Multi-region web app with private connectivity to database
- Serverless apps using Cosmos DB
Similar database products
If you’re familiar with Amazon Web Services (AWS) or Google Cloud Platform (GCP), refer to the following comparisons: