Amazon RDS for Aurora vs Google Cloud SQL: What are the differences?
Amazon RDS for Aurora: MySQL and PostgreSQL compatible relational database with several times better performance. 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; 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.
Amazon RDS for Aurora and Google Cloud SQL can be categorized as "SQL Database as a Service" tools.
Some of the features offered by Amazon RDS for Aurora are:
- High Throughput with Low Jitter
- Push-button Compute Scaling
- Storage Auto-scaling
On the other hand, Google Cloud SQL provides the following key features:
- Familiar Infrastructure
- Flexible Charging
- Security, Availability, Durability
"MySQL compatibility " is the primary reason why developers consider Amazon RDS for Aurora over the competitors, whereas "Fully managed" was stated as the key factor in picking Google Cloud SQL.
StackShare, GoGuardian, and Akoova are some of the popular companies that use Amazon RDS for Aurora, whereas Google Cloud SQL is used by Implisit, Policygenius, and OTOBANK. Amazon RDS for Aurora has a broader approval, being mentioned in 116 company stacks & 30 developers stacks; compared to Google Cloud SQL, which is listed in 71 company stacks and 28 developer stacks.
What is Amazon RDS for Aurora?
What is Google Cloud SQL?
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Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.
Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.
We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!
#SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Managed MySQL clustered database so I dont have to deal with the required infrastructure
Core database for managing users, teams, tests, and result summaries
We moved our database from compose.io to AWS for speed and price.