Airflow vs Amazon RDS

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs Amazon RDS: What are the differences?

Introduction:

In the world of data management and processing, Airflow and Amazon RDS are two popular tools that serve different functions. Understanding the key differences between Airflow and Amazon RDS will help users choose the right tool for their specific needs.

Architecture: Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It allows users to define workflows as Directed Acyclic Graphs (DAGs) in Python code. On the other hand, Amazon RDS (Relational Database Service) is a managed database service that simplifies database setup, operation, and scaling. It provides easy access to a variety of database engines like MySQL, PostgreSQL, Oracle, and SQL Server.
Functionality: Airflow focuses on orchestrating complex workflows, automating tasks, and managing dependencies between tasks. It provides a rich set of operators for tasks such as BashOperator, PythonOperator, and more. In contrast, Amazon RDS primarily focuses on providing a cloud-based relational database service with features like automated backups, scalability, and security controls.
Deployment Model: Airflow can be deployed on-premises or in the cloud and offers flexibility in terms of infrastructure choices. Users can choose to run Airflow on platforms like AWS, Google Cloud, or Azure. On the other hand, Amazon RDS is a fully managed service provided by AWS, eliminating the need for users to manage the underlying infrastructure. Users can simply launch an RDS instance and start using it.
Scalability: Airflow offers scalability through distributed execution of tasks across a cluster of worker nodes. This horizontally scalable architecture allows users to handle large workloads and increase throughput as needed. Amazon RDS also offers scalability options through features like Multi-AZ deployments for high availability and Read Replicas for read-heavy workloads.
Cost: Airflow is an open-source tool, which means users can set up and run Airflow workflows without incurring licensing costs. However, users need to consider infrastructure costs for hosting Airflow and managing the cluster. On the other hand, Amazon RDS is a paid service where users pay for the compute and storage resources used by their database instances, along with any additional features utilized.
Integration: Airflow provides seamless integration with various data processing tools and services, making it a preferred choice for building data pipelines. It supports connections to databases, cloud storage services, messaging queues, and more. Amazon RDS integrates well with other AWS services like EC2, S3, and Lambda, enabling users to build robust and scalable applications within the AWS ecosystem.

In Summary, understanding the differences between Airflow and Amazon RDS is crucial for choosing the right tool based on specific requirements.

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Advice on Amazon RDS, Airflow

Phillip

Developer at Coach Align

Mar 18, 2021

Decided

Using on-demand read/write capacity while we scale our userbase - means that we're well within the free-tier on AWS while we scale the business and evaluate traffic patterns.

Using single-table design, which is dead simple using Jeremy Daly's dynamodb-toolbox library

29.3k views29.3k

Comments

Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

294k views294k

Comments

Detailed Comparison

Amazon RDS	Airflow
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.	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.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	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.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
Stacks 16.2K	Stacks 1.7K
Followers 10.8K	Followers 2.8K
Votes 761	Votes 128
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 10 Extensibility Cons 2 Observability is not great when the DAGs exceed 250 2 Open source - provides minimum or no support 2 Running it on kubernetes cluster relatively complex 1 Logical separation of DAGs is not straight forward

What are some alternatives to Amazon RDS, Airflow?

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.

Google Cloud SQL

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

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.

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

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.

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

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.

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Shipyard

Related Comparisons

Airflow vs Amazon RDS: What are the differences?

Introduction:

Architecture: Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It allows users to define workflows as Directed Acyclic Graphs (DAGs) in Python code. On the other hand, Amazon RDS (Relational Database Service) is a managed database service that simplifies database setup, operation, and scaling. It provides easy access to a variety of database engines like MySQL, PostgreSQL, Oracle, and SQL Server.
Functionality: Airflow focuses on orchestrating complex workflows, automating tasks, and managing dependencies between tasks. It provides a rich set of operators for tasks such as BashOperator, PythonOperator, and more. In contrast, Amazon RDS primarily focuses on providing a cloud-based relational database service with features like automated backups, scalability, and security controls.
Deployment Model: Airflow can be deployed on-premises or in the cloud and offers flexibility in terms of infrastructure choices. Users can choose to run Airflow on platforms like AWS, Google Cloud, or Azure. On the other hand, Amazon RDS is a fully managed service provided by AWS, eliminating the need for users to manage the underlying infrastructure. Users can simply launch an RDS instance and start using it.
Scalability: Airflow offers scalability through distributed execution of tasks across a cluster of worker nodes. This horizontally scalable architecture allows users to handle large workloads and increase throughput as needed. Amazon RDS also offers scalability options through features like Multi-AZ deployments for high availability and Read Replicas for read-heavy workloads.
Cost: Airflow is an open-source tool, which means users can set up and run Airflow workflows without incurring licensing costs. However, users need to consider infrastructure costs for hosting Airflow and managing the cluster. On the other hand, Amazon RDS is a paid service where users pay for the compute and storage resources used by their database instances, along with any additional features utilized.
Integration: Airflow provides seamless integration with various data processing tools and services, making it a preferred choice for building data pipelines. It supports connections to databases, cloud storage services, messaging queues, and more. Amazon RDS integrates well with other AWS services like EC2, S3, and Lambda, enabling users to build robust and scalable applications within the AWS ecosystem.

In Summary, understanding the differences between Airflow and Amazon RDS is crucial for choosing the right tool based on specific requirements.

Airflow vs Amazon RDS

Overview