Airflow聽vs聽Amazon RDS

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Airflow
Airflow

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Amazon RDS
Amazon RDS

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Airflow vs Amazon RDS: What are the differences?

Developers describe Airflow as "A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb". Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. On the other hand, Amazon RDS is detailed as "Set up, operate, and scale a relational database in the cloud". 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.

Airflow and Amazon RDS are primarily classified as "Workflow Manager" and "SQL Database as a Service" tools respectively.

Some of the features offered by Airflow are:

  • Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
  • Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
  • Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.

On the other hand, Amazon RDS provides the following key features:

  • Pre-configured Parameters
  • Monitoring and Metrics
  • Automatic Software Patching

Airflow is an open source tool with 12.9K GitHub stars and 4.71K GitHub forks. Here's a link to Airflow's open source repository on GitHub.

According to the StackShare community, Amazon RDS has a broader approval, being mentioned in 1435 company stacks & 526 developers stacks; compared to Airflow, which is listed in 72 company stacks and 33 developer stacks.

- No public GitHub repository available -

What is Airflow?

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

What is 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.
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Why do developers choose Airflow?
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        What are some alternatives to Airflow and Amazon RDS?
        Luigi
        It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
        Apache NiFi
        An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
        Jenkins
        In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
        AWS Step Functions
        AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
        Apache Beam
        It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
        See all alternatives
        Decisions about Airflow and Amazon RDS
        Tim Specht
        Tim Specht
        鈥嶤o-Founder and CTO at Dubsmash | 13 upvotes 57.3K 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
        Principal Software Engineer at Tophatter | 16 upvotes 373.3K 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.

        See more
        Interest over time
        Reviews of Airflow and Amazon RDS
        No reviews found
        How developers use Airflow and Amazon RDS
        Avatar of Pathwright
        Pathwright uses Amazon RDSAmazon RDS

        While we initially started off running our own Postgres cluster, we evaluated RDS and found it to be an excellent fit for us.

        The failovers, manual scaling, replication, Postgres upgrades, and pretty much everything else has been super smooth and reliable.

        We'll probably need something a little more complex in the future, but RDS performs admirably for now.

        Avatar of AngeloR
        AngeloR uses Amazon RDSAmazon RDS

        We are using RDS for managing PostgreSQL and legacy MSSQL databases.

        Unfortunately while RDS works great for managing the PostgreSQL systems, MSSQL is very much a second class citizen and they don't offer very much capability. Infact, in order to upgrade instance storage for MSSQL we actually have to spin up a new cluster and migrate the data over.

        Avatar of Wirkn Inc.
        Wirkn Inc. uses Amazon RDSAmazon RDS

        Our PostgreSQL servers, where we keep the bulk of Wirkn data, are hosted on the fantastically easy and reliable AWS RDS platform.

        Avatar of Digital2Go
        Digital2Go uses Amazon RDSAmazon RDS

        We use Aurora for our OLTP database, it provides significant speed increases on top of MySQL without the need to manage it

        Avatar of fadingdust
        fadingdust uses Amazon RDSAmazon RDS

        RDS allows us to replicate the development databases locally as well as making it available to CircleCI.

        Avatar of Eugene Ivanchenko
        Eugene Ivanchenko uses AirflowAirflow

        Manage the calculation pipeline and data distribution procedures.

        Avatar of Christopher Davison
        Christopher Davison uses AirflowAirflow

        Used for scheduling ETL jobs

        How much does Airflow cost?
        How much does Amazon RDS cost?
        Pricing unavailable