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  5. AWS Data Pipeline vs Airflow

AWS Data Pipeline vs Airflow

OverviewDecisionsComparisonAlternatives

Overview

AWS Data Pipeline
AWS Data Pipeline
Stacks94
Followers398
Votes1
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

AWS Data Pipeline vs Airflow: What are the differences?

  1. Configuration: AWS Data Pipeline is a managed service that allows users to orchestrate and automate the movement and transformation of data across various AWS services as well as on-premises data sources. It provides a graphical interface for creating and managing data pipelines, making it easy for users to define the structure and steps of their data processing workflows. On the other hand, Airflow is an open-source platform that enables users to programmatically author, schedule, and monitor workflows. It uses Python code as its configuration, providing more flexibility and control over the data processing tasks.

  2. Workflow Definition: In AWS Data Pipeline, workflows are defined using a visual interface where users can drag and drop different components and connect them to create a pipeline. This makes it easier for users who are not familiar with programming to create complex workflows. Airflow, on the other hand, defines workflows as directed acyclic graphs (DAGs) using Python code. This allows developers to have more flexibility and control over the workflow definition, making it easier to track dependencies, handle error scenarios, and dynamically generate tasks.

  3. Integration with AWS Services: AWS Data Pipeline provides seamless integration with various AWS services, such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon EMR. It offers pre-built connectors to these services, allowing users to easily incorporate them into their data pipelines. Airflow is also capable of integrating with AWS services using Python libraries and third-party plugins. However, users have to manually configure the integration and handle the authentication and access control.

  4. Monitoring and Alerting: AWS Data Pipeline provides a comprehensive monitoring dashboard that allows users to track the status and progress of their pipelines. It also offers built-in email notifications and CloudWatch alarms to alert users about any issues or failures in their pipelines. Airflow, on the other hand, provides a web-based user interface where users can monitor and visualize the status of their workflows. It also supports integration with external monitoring tools such as Grafana and Prometheus for more advanced monitoring and alerting capabilities.

  5. Scalability and Performance: AWS Data Pipeline is a fully managed service that automatically scales resources based on the workload. It can handle large datasets and parallel processing using AWS services like Amazon EMR and AWS Glue. Airflow, being an open-source platform, requires users to manually provision and manage their own infrastructure. Users can scale Airflow horizontally by adding more worker nodes to handle concurrent tasks, but they are responsible for managing the scalability and performance aspects.

  6. Community and Support: AWS Data Pipeline has the advantage of being a managed service provided by AWS, which ensures ongoing support and maintenance. It also has a large user community and extensive documentation. Airflow, being an open-source project, relies on its community for support and maintenance. It has an active developer community and provides comprehensive documentation, but users may have to rely on community forums and discussions for troubleshooting and support.

In Summary, AWS Data Pipeline and Airflow have key differences in their configuration options, workflow definition methods, integration with AWS services, monitoring and alerting capabilities, scalability and performance management, as well as the level of community support provided.

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Advice on AWS Data Pipeline, Airflow

Anonymous
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.

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Detailed Comparison

AWS Data Pipeline
AWS Data Pipeline
Airflow
Airflow

AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.

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.

You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.;Hourly analysis of Amazon S3‐based log data;Daily replication of AmazonDynamoDB data to Amazon S3;Periodic replication of on-premise JDBC database tables into RDS
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
94
Stacks
1.7K
Followers
398
Followers
2.8K
Votes
1
Votes
128
Pros & Cons
Pros
  • 1
    Easy to create DAG and execute it
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 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 AWS Data Pipeline, Airflow?

GitHub Actions

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

Apache Beam

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

Zenaton

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

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

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

Shipyard

na

AWS Snowball Edge

AWS Snowball Edge

AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.

PromptX

PromptX

PromptX is an AI-powered enterprise knowledge and workflow platform that helps organizations search, discover and act on information with speed and accuracy. It unifies data from SharePoint, Google Drive, email, cloud systems and legacy databases into one secure Enterprise Knowledge System. Using generative and agentic AI, users can ask natural language questions and receive context-rich, verifiable answers in seconds. PromptX ingests and enriches content with semantic tagging, entity recognition and knowledge cards, turning unstructured data into actionable insights. With adaptive prompts, collaborative workspaces and AI-driven workflows, teams make faster, data-backed decisions. The platform includes RBAC, SSO, audit trails and compliance-ready AI governance, and integrates with any LLM or external search engine. It supports cloud, hybrid and on-premise deployments for healthcare, public sector, finance and enterprise service providers. PromptX converts disconnected data into trusted and actionable intelligence, bringing search, collaboration and automation into a single unified experience.

iLeap

iLeap

ILeap is a low-code app development platform to build custom apps and automate workflows visually, helping you speed up digital transformation.

AI Autopilot

AI Autopilot

Agentic AI Platform for Intelligent IT Automation built by MSPs for MSPs. Revolutionize your operations with advanced AI agents.

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