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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Apache Aurora vs Google Cloud SQL

Apache Aurora vs Google Cloud SQL

OverviewComparisonAlternatives

Overview

Google Cloud SQL
Google Cloud SQL
Stacks555
Followers580
Votes46
Apache Aurora
Apache Aurora
Stacks69
Followers96
Votes0

Apache Aurora vs Google Cloud SQL: What are the differences?

What is Apache Aurora? An Apcahe Mesos framework for scheduling jobs, originally developed by Twitter. Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.

What is Google Cloud SQL? Store and manage data using a fully-managed, relational MySQL database. MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.

Apache Aurora can be classified as a tool in the "Cluster Management" category, while Google Cloud SQL is grouped under "SQL Database as a Service".

Some of the features offered by Apache Aurora are:

  • Deployment and scheduling of jobs
  • The abstraction a “job” to bundle and manage Mesos tasks
  • A rich DSL to define services

On the other hand, Google Cloud SQL provides the following key features:

  • Familiar Infrastructure
  • Flexible Charging
  • Security, Availability, Durability

Apache Aurora is an open source tool with 616 GitHub stars and 230 GitHub forks. Here's a link to Apache Aurora's open source repository on GitHub.

Policygenius, Implisit, and OTOBANK are some of the popular companies that use Google Cloud SQL, whereas Apache Aurora is used by Twitter, Oscar Health, and Chartbeat. Google Cloud SQL has a broader approval, being mentioned in 112 company stacks & 100 developers stacks; compared to Apache Aurora, which is listed in 7 company stacks and 17 developer stacks.

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Detailed Comparison

Google Cloud SQL
Google Cloud SQL
Apache Aurora
Apache Aurora

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.

Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed
Deployment and scheduling of jobs;The abstraction a “job” to bundle and manage Mesos tasks;A rich DSL to define services;Health checking;Failure domain diversity;Instant provisioning
Statistics
Stacks
555
Stacks
69
Followers
580
Followers
96
Votes
46
Votes
0
Pros & Cons
Pros
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Encryption at rest and transit
No community feedback yet
Integrations
No integrations available
Apache Mesos
Apache Mesos
Vagrant
Vagrant

What are some alternatives to Google Cloud SQL, Apache Aurora?

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.

Amazon Aurora

Amazon Aurora

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.

Nomad

Nomad

Nomad is a cluster manager, designed for both long lived services and short lived batch processing workloads. Developers use a declarative job specification to submit work, and Nomad ensures constraints are satisfied and resource utilization is optimized by efficient task packing. Nomad supports all major operating systems and virtualized, containerized, or standalone applications.

Apache Mesos

Apache Mesos

Apache Mesos is a cluster manager that simplifies the complexity of running applications on a shared pool of servers.

ClearDB

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

Azure SQL Database

Azure SQL Database

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.

DC/OS

DC/OS

Unlike traditional operating systems, DC/OS spans multiple machines within a network, aggregating their resources to maximize utilization by distributed applications.

Mesosphere

Mesosphere

Mesosphere offers a layer of software that organizes your machines, VMs, and cloud instances and lets applications draw from a single pool of intelligently- and dynamically-allocated resources, increasing efficiency and reducing operational complexity.

Gardener

Gardener

Many Open Source tools exist which help in creating and updating single Kubernetes clusters. However, the more clusters you need the harder it becomes to operate, monitor, manage and keep all of them alive and up-to-date. And that is exactly what project Gardener focuses on.

YARN Hadoop

YARN Hadoop

Its fundamental idea is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM).

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