100 Days of Cloud – Day 62: Azure Database Solutions

Its Day 62 of my 100 Days of Cloud journey, and today I’m starting to look at the different Database Solutions available in Azure. “The Dude” to me to …..

The next 2 posts are going to cover the 2 main offerings – Azure SQL and Azure Cosmos. But first, we need to understand the different types of database that are available to us, how they store their data and the use cases where we would utilize the different database types.

Relational Databases

Lets kick off with Relational Database Management Systems, or RDBMS. These are the traditional model of storing data, and organises the data into 2-dimensional tables which have a series of rows and columns into which the data is stored.

RDBMS Databases follow a schema based model, where the data structure of the schema needs to be defined before any data is written. Any subsequent read or write operations must use the defined schema.

Vendors who use this model provide a version of Structured Query Language (SQL) for retrieving and managing the data. The most common examples of these would be Microsoft SQL, Oracle SQL or PostgreSQL.

RDBMS is useful when data consistency is required, however the downside is that RDBMS cannot easily scale out horizontally.

In Azure, the following RDBMS services are available:

  • Azure SQL Database – this is the full hosted version of SQL Server.
  • Azure Database for MySQL – open source relational database management system. MySQL uses standard SQL commands such as INSERT, DROP, ADD, and UPDATE, etc. The main purpose of MySQL is for e-commerce, data warehouse, and logging applications. Many database-driven websites use MySQL
  • Azure Database for PostgreSQL – this is a highly scalable RDBMS system which is cross-platform and can run on Linux, Windows and MacOS. PostgreSQL can perform complex queries, foreign keys, triggers, updatable views, and transactional integrity.
  • Azure Database for MariaDB – High performance OpenSource relational database based on MySQL. Dynamic columns allow a single DBMS to provide both SQL and NoSQL data handling for different needs. Supports encrypted tables, LDAP authentication and Kerberos.

The main use cases for RDBMS are:

  • Inventory management
  • Order management
  • Reporting database
  • Accounting

Non-Relational Databases

The opposite of relational databases are non-relational database, which is a database that does not use the tabular schema of rows and columns found in most traditional database systems. Instead, non-relational databases use a storage model that is optimized for the specific requirements of the type of data being stored. For example, data may be stored as simple key/value pairs, as JSON documents, or as a graph consisting of edges and vertices.

Because of the varying ways that data can be stored, there are LOADS of different types of non-relational databases.

Lets take a look at the different types of non-relational or NoSQL database.

  • Document Data Stores
Image Credit – Microsoft

A document data store manages a set of named string fields and object data values in an entity that’s referred to as a document. These are typically stored in JSON format, but can also be stored as XML, YAML, JSON, BSON, or even plain text. The fields within these documents are exposed to the storage management system, enabling an application to query and filter data by using the values in these fields. Typically, a document contains the entire data for an entity, and all documents are not required to have the same structure.

The application can retrieve documents by using the document key, which is hashed and is a unique identifier for the document.

From a service perspective, this would be delivered in Azure Cosmos DB.

Examples of use cases would be Product catalogs, Content management or Inventory management.

  • Columnar data stores
Image Credit – Microsoft

A columnar or column-family data store organizes data into columns and rows, which is very similar to a relational database. However, while a column-family database stores the data in tabular data with rows and columns, the columns are divided into groups known as column families. Each column family holds a set of columns that are logically related and are typically retrieved or manipulated as a unit. New columns can be added dynamically, and rows can be empty.

From a service perspective, this would be delivered in Azure Cosmos DB Cassandra API, which is used to store apps written for Apache Cassandra.

Examples of use cases would be Sensor data, Messaging, Social media and Web analytics, Activity monitoring, or Weather and other time-series data.

  • Key/value Data Stores
Image Credit – Microsoft

A key/value store associates each data value with a unique key. Most key/value stores only support simple query, insert, and delete operations. To modify a value (either partially or completely), an application must overwrite the existing data for the entire value. Key/value stores are highly optimized for applications performing simple lookups, but are less suitable if you need to query data across different key/value stores. Key/value stores are also not optimized for querying by value.

From a service perspective, this would be delivered in Azure Cosmos DB Table API or SQL API, Azure Cache for Redis, or Azure Table Storage.

Examples of use cases would be Data caching, Session management, or Product recommendations and ad serving.

  • Graph Databases
Image Credit – Microsoft

A graph database stores two types of information, nodes and edges. Edges specify relationships between nodes. Nodes and edges can have properties that provide information about that node or edge, similar to columns in a table. Edges can also have a direction indicating the nature of the relationship.

Graph databases can efficiently perform queries across the network of nodes and edges and analyze the relationships between entities.

From a service perspective, this would be delivered in Azure Cosmos DB Gremlin API.

Examples of use cases would be Organization charts, Social graphs, and Fraud detection.


And thats a whistle stop tour of the different types of databases available in Azure. There are other options such as Data Lake and Time Series, but I’ll leave those for future posts as they are bigger topics that deserve more attention.

Hope you enjoyed this post, until next time – I feel like going bowling now!

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