StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Airflow vs Qubole

Airflow vs Qubole

OverviewDecisionsComparisonAlternatives

Overview

Qubole
Qubole
Stacks36
Followers104
Votes67
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Qubole: What are the differences?

## Key differences between Airflow and Qubole

1. **Architecture**: Airflow is a workflow automation tool that focuses on defining workflows as directed acyclic graphs (DAGs), allowing users to schedule and monitor workflow execution. On the other hand, Qubole is a cloud-native data platform that provides a unified environment for data engineers, analysts, and data scientists to collaborate and work on various big data processing tasks.

2. **Integration with Cloud Providers**: Airflow offers integrations with various cloud providers such as AWS, Google Cloud Platform, and Microsoft Azure, allowing users to easily interact with cloud services within their workflows. In contrast, Qubole is built natively on cloud providers like AWS and provides seamless integration with their underlying services, optimizing performance and scalability for big data processing.

3. **Managed Service vs Open-source**: Airflow is an open-source project maintained by the Apache Software Foundation, which requires users to set up and manage their Airflow instances, databases, and clusters. On the other hand, Qubole is a managed service that takes care of infrastructure provisioning, maintenance, and scaling, enabling users to focus on their data processing tasks without worrying about the underlying infrastructure.

4. **Cost Structure**: Airflow is free to use as an open-source project, but users need to consider the costs associated with setting up and maintaining the infrastructure for Airflow. In contrast, Qubole follows a pricing model based on the resources used, providing a cost-effective solution for organizations requiring a fully managed big data platform without the overhead of infrastructure management.

5. **Advanced Analytics and Machine Learning Capabilities**: Qubole offers advanced analytics and machine learning capabilities through integration with popular libraries and frameworks such as Apache Spark, Apache Hive, and TensorFlow, enabling data scientists to perform complex data processing and model training within the platform. Airflow, while extensible, may require additional customization and integrations to achieve similar capabilities for advanced analytics and machine learning tasks.

6. **Community Support and Ecosystem**: Airflow has a vibrant open-source community and ecosystem of plugins, integrations, and workflows shared by users worldwide, providing a rich set of resources and extensions to enhance and customize the Airflow experience. While Qubole has a growing community and partnerships with key technology vendors, the ecosystem may not be as extensive as Airflow's, potentially limiting the range of available tools and integrations for users.

In Summary, the key differences between Airflow and Qubole lie in their architecture, integration with cloud providers, managed service vs open-source approach, cost structure, advanced analytics and machine learning capabilities, and community support and ecosystem.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Qubole, 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.

294k views294k
Comments

Detailed Comparison

Qubole
Qubole
Airflow
Airflow

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

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.

Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management
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
36
Stacks
1.7K
Followers
104
Followers
2.8K
Votes
67
Votes
128
Pros & Cons
Pros
  • 13
    Simple UI and autoscaling clusters
  • 10
    Feature to use AWS Spot pricing
  • 7
    Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters
  • 7
    Real-time data insights through Spark Notebook
  • 6
    Hyper elastic and scalable
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
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
Integrations
Google Compute Engine
Google Compute Engine
Microsoft Azure
Microsoft Azure
No integrations available

What are some alternatives to Qubole, Airflow?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets 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.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

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.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

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.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase