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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Amazon RDS for Aurora vs Azure SQL Database

Amazon RDS for Aurora vs Azure SQL Database

OverviewComparisonAlternatives

Overview

Amazon Aurora
Amazon Aurora
Stacks806
Followers744
Votes55
Azure SQL Database
Azure SQL Database
Stacks585
Followers502
Votes13

Amazon RDS for Aurora vs Azure SQL Database: What are the differences?

Introduction

Amazon RDS for Aurora and Azure SQL Database are both widely used managed relational database services that offer high performance, scalability, and availability. However, they have distinct differences that make them suitable for different use cases. Here are the key differences between Amazon RDS for Aurora and Azure SQL Database:

  1. Scalability Differences: While both Amazon RDS for Aurora and Azure SQL Database provide options for scaling, their approaches differ. Aurora offers a unique database architecture called a "cluster volume" that allows for automatic scaling of both read and write operations. On the other hand, Azure SQL Database can be scaled vertically by changing the pricing tier or horizontally by using elastic pools to manage multiple databases with shared resources.

  2. Compatibility and Language Support: Amazon RDS for Aurora is compatible with MySQL and PostgreSQL, offering support for the corresponding database engines. In contrast, Azure SQL Database is based on Microsoft SQL Server, providing full compatibility with existing SQL Server applications. It also supports multiple programming languages, including .NET, Java, Node.js, Python, and more.

  3. Data Replication: Aurora uses a distributed storage system that replicates data across multiple availability zones within a region, providing high durability and availability. It also offers an option for cross-region replication for disaster recovery. Azure SQL Database utilizes a combination of synchronous and asynchronous replication depending on the configuration, ensuring data redundancy and availability.

  4. Backup and Restore: Amazon RDS for Aurora automatically takes backups of the database and transaction logs, allowing point-in-time recovery for up to 35 days. Additionally, it provides fast database cloning capabilities for creating full copies of the database. Azure SQL Database also has automated backup features, including long-term retention, but the duration may vary based on the pricing tier.

  5. High Availability: Aurora is designed to provide high availability with a replication mechanism that automatically handles failover scenarios. It can recover from the availability zone failure by promoting a replica as the new primary instance. Azure SQL Database offers high availability by automatically replicating the databases across multiple servers within a region, minimizing downtime in case of failures.

  6. Pricing Model: Amazon RDS for Aurora uses a pay-as-you-go pricing model based on the instance type, storage, and data transfer usage. Additionally, there are pricing options for reserved instances that offer cost savings for long-term commitments. Azure SQL Database also follows a pay-as-you-go model with pricing based on the performance tier, storage, and data transfer. It also provides reserved capacity options for cost optimization.

In summary, Amazon RDS for Aurora and Azure SQL Database have key differences in terms of scalability approaches, compatibility, data replication mechanisms, backup and restore capabilities, high availability solutions, and pricing models. Understanding these distinctions is crucial when selecting the appropriate managed database service for your specific requirements.

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Detailed Comparison

Amazon Aurora
Amazon Aurora
Azure SQL Database
Azure SQL Database

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
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Statistics
Stacks
806
Stacks
585
Followers
744
Followers
502
Votes
55
Votes
13
Pros & Cons
Pros
  • 14
    MySQL compatibility
  • 12
    Better performance
  • 10
    Easy read scalability
  • 9
    Speed
  • 7
    Low latency read replica
Cons
  • 2
    Vendor locking
  • 1
    Rigid schema
Pros
  • 6
    Managed
  • 4
    Secure
  • 3
    Scalable
Integrations
PostgreSQL
PostgreSQL
MySQL
MySQL
No integrations available

What are some alternatives to Amazon Aurora, Azure SQL Database?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Amazon RDS

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

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