Amazon Athena vs Amazon RDS for Aurora

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Amazon Athena
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Amazon Athena vs Amazon RDS for Aurora: What are the differences?

Developers describe Amazon Athena as "Query S3 Using SQL". Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. On the other hand, Amazon RDS for Aurora is detailed as "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.

Amazon Athena can be classified as a tool in the "Big Data Tools" category, while Amazon RDS for Aurora is grouped under "SQL Database as a Service".

"Use SQL to analyze CSV files" is the primary reason why developers consider Amazon Athena over the competitors, whereas "MySQL compatibility " was stated as the key factor in picking Amazon RDS for Aurora.

Medium, StackShare, and Zumba are some of the popular companies that use Amazon RDS for Aurora, whereas Amazon Athena is used by Auto Trader, Zola, and Twilio SendGrid. Amazon RDS for Aurora has a broader approval, being mentioned in 121 company stacks & 31 developers stacks; compared to Amazon Athena, which is listed in 50 company stacks and 18 developer stacks.

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What is Amazon Athena?

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

What is Amazon RDS for 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.
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Why do developers choose Amazon Athena?
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    What are some alternatives to Amazon Athena and Amazon RDS for Aurora?
    Presto
    Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    Amazon Redshift
    Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    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.
    Spectrum
    The community platform for the future.
    See all alternatives
    Decisions about Amazon Athena and Amazon RDS for Aurora
    Tim Specht
    Tim Specht
    ‎Co-Founder and CTO at Dubsmash · | 13 upvotes · 50K views
    atDubsmashDubsmash
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    Redis
    Redis
    Amazon DynamoDB
    Amazon DynamoDB
    Amazon RDS
    Amazon RDS
    Heroku
    Heroku
    PostgreSQL
    PostgreSQL
    #PlatformAsAService
    #Databases
    #NosqlDatabaseAsAService
    #SqlDatabaseAsAService

    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

    See more
    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail · | 16 upvotes · 267.4K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
    Memcached
    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    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.

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    Interest over time
    Reviews of Amazon Athena and Amazon RDS for Aurora
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    How developers use Amazon Athena and Amazon RDS for Aurora
    Avatar of Secumail
    Secumail uses Amazon RDS for AuroraAmazon RDS for Aurora

    Managed MySQL clustered database so I dont have to deal with the required infrastructure

    Avatar of RedLine13
    RedLine13 uses Amazon RDS for AuroraAmazon RDS for Aurora

    Core database for managing users, teams, tests, and result summaries

    Avatar of Yaakov Gesher
    Yaakov Gesher uses Amazon RDS for AuroraAmazon RDS for Aurora

    We moved our database from compose.io to AWS for speed and price.

    Avatar of Bùi Thanh
    Bùi Thanh uses Amazon RDS for AuroraAmazon RDS for Aurora
    • Performance, HA and Scalable.
    • AutoScale replicas.
    How much does Amazon Athena cost?
    How much does Amazon RDS for Aurora cost?
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