https://github.com/apache/airflow
airflow apache apache-airflow automation dag data-engineering data-integration data-orchestrator data-pipelines data-science elt etl machine-learning mlops orchestration python scheduler workflow workflow-engine workflow-orchestration
Score: 39.405216771844536
Last synced: about 13 hours ago
JSON representation
Repository metadata:
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
- Host: GitHub
- URL: https://github.com/apache/airflow
- Owner: apache
- License: apache-2.0
- Created: 2015-04-13T18:04:58.000Z (over 11 years ago)
- Default Branch: main
- Last Pushed: 2026-09-06T18:43:01.000Z (3 days ago)
- Last Synced: 2026-09-06T22:31:32.097Z (3 days ago)
- Topics: airflow, apache, apache-airflow, automation, dag, data-engineering, data-integration, data-orchestrator, data-pipelines, data-science, elt, etl, machine-learning, mlops, orchestration, python, scheduler, workflow, workflow-engine, workflow-orchestration
- Language: Python
- Homepage: https://airflow.apache.org/
- Size: 671 MB
- Stars: 46,754
- Watchers: 789
- Forks: 17,776
- Open Issues: 2,178
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
- Security: .github/SECURITY.md
- Governance: GOVERNANCE.md
- Notice: NOTICE
- Agents: AGENTS.md
- Claude: CLAUDE.md
Owner metadata:
- Name: The Apache Software Foundation
- Login: apache
- Email:
- Kind: organization
- Description:
- Website: https://www.apache.org/
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/47359?v=4
- Repositories: 3147
- Last Synced at: 2026-09-06T00:28:19.829Z
- Profile URL: https://github.com/apache
Committers metadata
Last synced: about 18 hours ago
Total Commits: 39,432
Total Committers: 4,269
Avg Commits per committer: 9.237
Development Distribution Score (DDS): 0.872
Commits in past year: 8,138
Committers in past year: 826
Avg Commits per committer in past year: 9.852
Development Distribution Score (DDS) in past year: 0.901
| Name | Commits | |
|---|---|---|
| Jarek Potiuk | j****k@p****m | 5044 |
| Kaxil Naik | k****k@a****g | 2081 |
| Maxime Beauchemin | m****n@a****g | 1029 |
| Amogh Desai | a****9@g****m | 952 |
| Ash Berlin-Taylor | a****b@f****m | 947 |
| Kamil Breguła | m****j | 821 |
| Jed Cunningham | 6****m | 806 |
| Jens Scheffler | 9****l | 726 |
| Elad Kalif | 4****l | 693 |
| Tzu-ping Chung | u****r@g****m | 677 |
| Daniel Standish | 1****h | 675 |
| Ephraim Anierobi | s****4@g****m | 675 |
| Brent Bovenzi | b****t@a****o | 617 |
| Pierre Jeambrun | p****n@g****m | 604 |
| Vincent | 9****k | 594 |
| Dependabot [bot] | 4****] | 558 |
| Wei Lee | w****x@g****m | 546 |
| Andrey Anshin | A****n@t****s | 535 |
| GPK | g****n@g****m | 428 |
| Bolke de Bruin | b****e@x****l | 422 |
| Hussein Awala | h****n@a****r | 366 |
| Tomek Urbaszek | t****k@a****g | 326 |
| Jeremiah Lowin | j****n@a****g | 276 |
| Jason(Zhe-You) Liu | 6****6 | 264 |
| Shahar Epstein | 6****1 | 258 |
| D. Ferruzzi | f****i@a****m | 252 |
| Niko Oliveira | o****s@a****m | 250 |
| Bugra Ozturk | b****3 | 232 |
| Kacper Muda | m****r@g****m | 232 |
| Guan Ming(Wesley) Chiu | 1****g | 196 |
| and 4239 more... | ||
Issue and Pull Request metadata
Last synced: about 15 hours ago
Total issues: 5,679
Total pull requests: 25,147
Average time to close issues: 4 months
Average time to close pull requests: 9 days
Total issue authors: 2,387
Total pull request authors: 1,949
Average comments per issue: 3.33
Average comments per pull request: 1.69
Merged pull request: 16,589
Bot issues: 5
Bot pull requests: 1,418
Past year issues: 396
Past year pull requests: 2,352
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 8 days
Past year issue authors: 244
Past year pull request authors: 517
Past year average comments per issue: 2.33
Past year average comments per pull request: 1.45
Past year merged pull request: 791
Past year bot issues: 2
Past year bot pull requests: 312
Top Issue Authors
- potiuk (294)
- eladkal (145)
- bbovenzi (138)
- atul-astronomer (125)
- kaxil (123)
- vatsrahul1001 (92)
- amoghrajesh (92)
- dstandish (89)
- tirkarthi (81)
- jedcunningham (79)
- Lee-W (72)
- pierrejeambrun (71)
- bugraoz93 (65)
- jscheffl (57)
- rawwar (54)
Top Pull Request Authors
- potiuk (3,014)
- amoghrajesh (929)
- dependabot[bot] (851)
- jscheffl (807)
- kaxil (775)
- Taragolis (609)
- Lee-W (599)
- gopidesupavan (571)
- github-actions[bot] (564)
- vincbeck (556)
- dstandish (550)
- jedcunningham (531)
- pierrejeambrun (528)
- bbovenzi (502)
- eladkal (483)
Top Issue Labels
- kind:bug (3,179)
- needs-triage (2,208)
- area:core (2,148)
- kind:feature (1,248)
- area:providers (925)
- good first issue (844)
- kind:meta (635)
- area:UI (611)
- pending-response (316)
- area:API (282)
- kind:documentation (251)
- stale (195)
- area:helm-chart (192)
- Stale Bug Report (175)
- provider:cncf-kubernetes (152)
- provider:google (142)
- area:Scheduler (132)
- priority:medium (131)
- affected_version:3.0 (129)
- priority:high (129)
- provider:amazon-aws (118)
- testing status (117)
- affected_version:3.0.0beta (113)
- area:logging (109)
- area:webserver (80)
- area:dynamic-task-mapping (80)
- AIP-84 (74)
- affected_version:3.0.0rc (74)
- area:task-execution-interface-aip72 (72)
- AIP-38 (71)
Top Pull Request Labels
- area:providers (7,546)
- area:dev-tools (4,617)
- kind:documentation (3,139)
- area:UI (2,621)
- area:API (1,896)
- provider:google (1,387)
- area:webserver (1,138)
- area:task-sdk (1,025)
- changelog:skip (954)
- stale (920)
- provider:amazon-aws (867)
- area:Scheduler (811)
- provider:cncf-kubernetes (793)
- area:CLI (749)
- type:bug-fix (734)
- backport-to-v3-0-test (655)
- full tests needed (634)
- area:production-image (607)
- area:serialization (607)
- area:helm-chart (607)
- dependencies (583)
- provider:fab (583)
- area:system-tests (494)
- provider:amazon (481)
- javascript (435)
- area:core-operators (409)
- area:logging (390)
- provider:openlineage (385)
- provider:microsoft-azure (380)
- area:DAG-processing (328)
Package metadata
- Total packages: 100
-
Total downloads:
- conda: 10,382,217 total
- pypi: 75,768,908 last-month
- Total docker downloads: 535,500,860
- Total dependent packages: 336 (may contain duplicates)
- Total dependent repositories: 1,142 (may contain duplicates)
- Total versions: 2,845
- Total maintainers: 6
- Total advisories: 23
pypi.org: apache-airflow-providers-google
Provider package apache-airflow-providers-google for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-google/22.4.0
- Licenses: Apache-2.0
- Latest release: 22.4.0 (published 17 days ago)
- Last Synced: 2026-09-09T09:07:03.671Z (about 19 hours ago)
- Versions: 224
- Dependent Packages: 25
- Dependent Repositories: 374
- Downloads: 10,640,504 Last month
- Docker Downloads: 25,389,957
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.03%
- Downloads: 0.219%
- Average: 0.334%
- Docker downloads count: 0.49%
- Dependent packages count: 0.493%
- Dependent repos count: 0.759%
- Maintainers (3)
-
Advisories:
- Apache Airflow Google provider allows path traversal through GCS object names
- Apache Airflow providers-google's `ComputeEngineSSHHook` disables SSH host-key verification by default
- Apache Airflow Google Provider Improper Input Validation vulnerability
- Apache Airflow Google Provider Improper Input Validation vulnerability
pypi.org: apache-airflow-providers-common-sql
Provider package apache-airflow-providers-common-sql for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-common-sql/2.1.1
- Licenses: Apache-2.0
- Latest release: 2.1.1 (published 17 days ago)
- Last Synced: 2026-09-09T09:21:19.428Z (about 18 hours ago)
- Versions: 156
- Dependent Packages: 43
- Dependent Repositories: 189
- Downloads: 30,507,999 Last month
- Docker Downloads: 358,901,187
-
Rankings:
- Forks count: 0.056%
- Downloads: 0.072%
- Stargazers count: 0.129%
- Average: 0.342%
- Dependent packages count: 0.352%
- Docker downloads count: 0.368%
- Dependent repos count: 1.077%
- Maintainers (3)
- Advisories:
pypi.org: apache-airflow-providers-amazon
Provider package apache-airflow-providers-amazon for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-amazon/9.35.1
- Licenses: Apache-2.0
- Latest release: 9.35.1 (published 11 days ago)
- Last Synced: 2026-09-09T11:17:52.074Z (about 16 hours ago)
- Versions: 244
- Dependent Packages: 27
- Dependent Repositories: 184
- Downloads: 6,446,625 Last month
- Docker Downloads: 25,171,403
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Downloads: 0.196%
- Average: 0.389%
- Dependent packages count: 0.474%
- Docker downloads count: 0.506%
- Dependent repos count: 1.115%
- Maintainers (3)
-
Advisories:
- Apache Airflow Amazon provider: Prevent unauthorized access to team-scoped secrets in AWS Secrets Manager and SSM Parameter Store backends
- Apache Airflow AWS Auth Manager has Host Header Injection Leading to SAML Authentication Bypass
- Apache Airflow AWS Provider Generates Error Message Containing Sensitive Information
pypi.org: apache-airflow-providers-cncf-kubernetes
Provider package apache-airflow-providers-cncf-kubernetes for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/10.21.1
- Licenses: Apache-2.0
- Latest release: 10.21.1 (published 17 days ago)
- Last Synced: 2026-09-08T23:43:16.482Z (1 day ago)
- Versions: 233
- Dependent Packages: 28
- Dependent Repositories: 124
- Downloads: 8,882,646 Last month
- Docker Downloads: 25,197,427
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Downloads: 0.222%
- Average: 0.469%
- Docker downloads count: 0.506%
- Dependent packages count: 0.701%
- Dependent repos count: 1.341%
- Maintainers (5)
-
Advisories:
- Apache Airflow CNCF Kubernetes provider: JWT Token Exposure in KubernetesExecutor Command-Line Arguments
- Apache Airflow CNCF Kubernetes provider, Apache Airflow: Kubernetes configuration file saved without encryption in the Metadata and logged as plain text in the Triggerer service
- Apache Airflow CNCF Kubernetes Provider: KubernetesPodOperator RCE via connection configuration
pypi.org: apache-airflow-providers-docker
Provider package apache-airflow-providers-docker for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-docker/4.5.9
- Licenses: Apache-2.0
- Latest release: 4.5.9 (published about 1 month ago)
- Last Synced: 2026-09-09T05:18:27.880Z (about 22 hours ago)
- Versions: 149
- Dependent Packages: 13
- Dependent Repositories: 64
- Downloads: 960,679 Last month
- Docker Downloads: 25,387,894
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.03%
- Downloads: 0.474%
- Docker downloads count: 0.493%
- Average: 0.638%
- Dependent packages count: 0.962%
- Dependent repos count: 1.852%
- Maintainers (3)
- Advisories:
pypi.org: apache-airflow-providers-microsoft-mssql
Provider package apache-airflow-providers-microsoft-mssql for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-microsoft-mssql/4.7.1
- Licenses: Apache-2.0
- Latest release: 4.7.1 (published 17 days ago)
- Last Synced: 2026-09-09T11:18:21.347Z (about 16 hours ago)
- Versions: 104
- Dependent Packages: 9
- Dependent Repositories: 27
- Downloads: 2,780,661 Last month
- Docker Downloads: 25,138,207
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Downloads: 0.453%
- Docker downloads count: 0.516%
- Average: 0.906%
- Dependent packages count: 1.611%
- Dependent repos count: 2.815%
- Maintainers (3)
- Advisories:
pypi.org: apache-airflow-providers-apache-spark
Provider package apache-airflow-providers-apache-spark for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-apache-spark/6.3.2
- Licenses: Apache-2.0
- Latest release: 6.3.2 (published 17 days ago)
- Last Synced: 2026-09-09T07:03:47.833Z (about 21 hours ago)
- Versions: 135
- Dependent Packages: 6
- Dependent Repositories: 69
- Downloads: 739,774 Last month
- Docker Downloads: 3,688
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Downloads: 0.604%
- Average: 0.972%
- Dependent packages count: 1.611%
- Dependent repos count: 1.787%
- Docker downloads count: 1.789%
- Maintainers (3)
- Advisories:
pypi.org: apache-airflow-providers-apache-beam
Provider package apache-airflow-providers-apache-beam for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-apache-beam/6.2.3
- Licenses: Apache-2.0
- Latest release: 6.2.3 (published 6 months ago)
- Last Synced: 2026-09-08T14:32:48.797Z (1 day ago)
- Versions: 112
- Dependent Packages: 4
- Dependent Repositories: 46
- Downloads: 279,228 Last month
- Docker Downloads: 2,282
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Downloads: 0.703%
- Average: 1.192%
- Docker downloads count: 2.089%
- Dependent packages count: 2.156%
- Dependent repos count: 2.164%
- Maintainers (3)
pypi.org: apache-airflow-providers-elasticsearch
Provider package apache-airflow-providers-elasticsearch for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-elasticsearch/6.9.0
- Licenses: Apache-2.0
- Latest release: 6.9.0 (published about 1 month ago)
- Last Synced: 2026-09-09T09:12:51.734Z (about 19 hours ago)
- Versions: 155
- Dependent Packages: 3
- Dependent Repositories: 23
- Downloads: 319,957 Last month
- Docker Downloads: 25,170,205
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Docker downloads count: 0.507%
- Downloads: 1.157%
- Average: 1.338%
- Dependent repos count: 3.05%
- Dependent packages count: 3.271%
- Maintainers (3)
- Advisories:
pypi.org: apache-airflow-providers-facebook
Provider package apache-airflow-providers-facebook for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-facebook/3.9.5
- Licenses: Apache-2.0
- Latest release: 3.9.5 (published 3 months ago)
- Last Synced: 2026-09-08T20:11:16.998Z (1 day ago)
- Versions: 77
- Dependent Packages: 5
- Dependent Repositories: 16
- Downloads: 89,615 Last month
- Docker Downloads: 371
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Average: 1.49%
- Downloads: 1.528%
- Dependent packages count: 1.611%
- Docker downloads count: 2.089%
- Dependent repos count: 3.668%
- Maintainers (3)
pypi.org: apache-airflow-providers-singularity
Provider package apache-airflow-providers-singularity for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-singularity/3.9.4
- Licenses: Apache-2.0
- Latest release: 3.9.4 (published 3 months ago)
- Last Synced: 2026-09-07T20:42:03.920Z (2 days ago)
- Versions: 64
- Dependent Packages: 3
- Dependent Repositories: 12
- Downloads: 57,530 Last month
- Docker Downloads: 371
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Downloads: 1.714%
- Average: 1.896%
- Docker downloads count: 2.095%
- Dependent packages count: 3.271%
- Dependent repos count: 4.252%
- Maintainers (3)
pypi.org: apache-airflow-providers-apache-drill
Provider package apache-airflow-providers-apache-drill for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-apache-drill/3.3.3
- Licenses: Apache-2.0
- Latest release: 3.3.3 (published 3 months ago)
- Last Synced: 2026-09-09T09:07:09.391Z (about 19 hours ago)
- Versions: 85
- Dependent Packages: 3
- Dependent Repositories: 3
- Downloads: 56,928 Last month
- Docker Downloads: 25,137,868
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.029%
- Docker downloads count: 0.517%
- Downloads: 1.687%
- Average: 2.449%
- Dependent packages count: 3.271%
- Dependent repos count: 9.173%
- Maintainers (4)
- Advisories:
conda-forge.org: 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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org/
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-07-24T20:36:33.003Z (about 2 months ago)
- Versions: 42
- Dependent Packages: 120
- Dependent Repositories: 3
- Downloads: 1,941,830 Total
-
Rankings:
- Forks count: 0.18%
- Stargazers count: 0.504%
- Dependent packages count: 0.639%
- Average: 4.832%
- Dependent repos count: 18.005%
pypi.org: apache-airflow-backport-providers-jira
Backport provider package apache-airflow-backport-providers-jira for Apache Airflow
- Homepage: https://airflow.apache.org/
- Documentation: https://airflow.apache.org/docs/
- Licenses: Apache License 2.0
- Latest release: 2021.3.17 (published over 5 years ago)
- Last Synced: 2026-09-06T22:57:54.886Z (3 days ago)
- Versions: 11
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 225 Last month
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.03%
- Average: 9.109%
- Dependent packages count: 10.141%
- Downloads: 13.836%
- Dependent repos count: 21.526%
- Maintainers (3)
pypi.org: apache-airflow-backport-providers-apache-hive
Backport provider package apache-airflow-backport-providers-apache-hive for Apache Airflow
- Homepage: https://airflow.apache.org/
- Documentation: https://airflow.apache.org/docs/
- Licenses: Apache License 2.0
- Latest release: 2021.3.3 (published over 5 years ago)
- Last Synced: 2026-09-06T22:56:55.560Z (3 days ago)
- Versions: 13
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 62 Last month
-
Rankings:
- Forks count: 0.014%
- Stargazers count: 0.03%
- Average: 9.285%
- Dependent packages count: 10.141%
- Downloads: 14.712%
- Dependent repos count: 21.526%
- Maintainers (3)
conda-forge.org: apache-airflow-providers-imap
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-03-14T16:14:15.651Z (6 months ago)
- Versions: 8
- Dependent Packages: 2
- Dependent Repositories: 1
- Downloads: 229,943 Total
-
Rankings:
- Forks count: 0.18%
- Stargazers count: 0.504%
- Average: 11.083%
- Dependent packages count: 19.561%
- Dependent repos count: 24.088%
anaconda.org: 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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:35:53.144Z (about 2 months ago)
- Versions: 5
- Dependent Packages: 38
- Dependent Repositories: 3
- Downloads: 5,713 Total
-
Rankings:
- Forks count: 0.668%
- Dependent packages count: 0.699%
- Stargazers count: 1.124%
- Average: 12.234%
- Dependent repos count: 46.446%
pypi.org: apache-airflow-providers-keycloak
Provider package apache-airflow-providers-keycloak for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-keycloak/0.9.0
- Licenses: Apache-2.0
- Latest release: 0.9.0 (published 17 days ago)
- Last Synced: 2026-09-09T09:56:07.283Z (about 18 hours ago)
- Versions: 40
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 135,109 Last month
-
Rankings:
- Forks count: 0.066%
- Stargazers count: 0.167%
- Dependent packages count: 8.696%
- Average: 14.484%
- Dependent repos count: 49.007%
- Maintainers (1)
- Advisories:
anaconda.org: apache-airflow-providers-http
- Homepage: https://airflow.apache.org/
- Licenses: Apache-2.0
- Latest release: 4.0.0 (published about 4 years ago)
- Last Synced: 2026-08-15T14:40:28.382Z (26 days ago)
- Versions: 1
- Dependent Packages: 2
- Dependent Repositories: 1
-
Rankings:
- Forks count: 0.669%
- Stargazers count: 1.125%
- Average: 18.373%
- Dependent packages count: 20.45%
- Dependent repos count: 51.248%
anaconda.org: apache-airflow-providers-imap
- Homepage: https://airflow.apache.org/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-04-02T15:47:44.950Z (5 months ago)
- Versions: 1
- Dependent Packages: 2
- Dependent Repositories: 1
-
Rankings:
- Forks count: 0.668%
- Stargazers count: 1.124%
- Average: 18.385%
- Dependent packages count: 20.474%
- Dependent repos count: 51.276%
anaconda.org: apache-airflow-providers-apache-hdfs
- Homepage: https://airflow.apache.org/
- Licenses: Apache-2.0
- Latest release: 3.0.1 (published about 4 years ago)
- Last Synced: 2026-07-24T20:35:45.126Z (about 2 months ago)
- Versions: 1
- Dependent Packages: 1
- Dependent Repositories: 0
- Downloads: 688 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 20.037%
- Dependent packages count: 20.494%
- Dependent repos count: 57.694%
conda-forge.org: airflow-with-webhdfs
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.2.4 (published over 4 years ago)
- Last Synced: 2026-04-01T16:14:03.245Z (5 months ago)
- Versions: 31
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 370,851 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-async
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-08-30T20:31:10.869Z (10 days ago)
- Versions: 41
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 727,935 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-amazon
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 6.0.0 (published almost 4 years ago)
- Last Synced: 2026-04-01T16:11:01.728Z (5 months ago)
- Versions: 18
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 157,865 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-azure_blob_storage
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-04-03T01:26:51.696Z (5 months ago)
- Versions: 18
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-datadog
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-03-24T04:07:40.645Z (6 months ago)
- Versions: 6
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 39,456 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-flask_oauth
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-03-24T04:09:20.568Z (6 months ago)
- Versions: 5
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 63,665 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-zendesk
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.0.0 (published about 4 years ago)
- Last Synced: 2026-04-01T16:10:34.990Z (5 months ago)
- Versions: 7
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 40,376 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-hive
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:35:58.057Z (about 2 months ago)
- Versions: 6
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 79,864 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-sentry
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-07-24T20:36:28.872Z (about 2 months ago)
- Versions: 27
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 680,632 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-statsd
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-04-01T16:11:26.123Z (5 months ago)
- Versions: 41
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 798,898 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-mysql
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.2.1 (published almost 4 years ago)
- Last Synced: 2026-04-01T15:11:05.813Z (5 months ago)
- Versions: 13
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-deprecated-api
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-07-24T20:35:56.022Z (about 2 months ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 323,074 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-singularity
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-03-11T03:29:37.793Z (6 months ago)
- Versions: 7
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-docker
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.2.0 (published almost 4 years ago)
- Last Synced: 2026-03-24T04:07:39.413Z (6 months ago)
- Versions: 17
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 106,062 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-emr
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:35:47.172Z (about 2 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 178,680 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-azure-mgmt-containerinstance
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.11 (published about 6 years ago)
- Last Synced: 2026-07-24T20:35:52.538Z (about 2 months ago)
- Versions: 11
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 75,926 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-sftp
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.1.0 (published almost 4 years ago)
- Last Synced: 2026-03-24T04:07:17.096Z (6 months ago)
- Versions: 16
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 90,387 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-azure_cosmos
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-04-01T16:09:21.467Z (5 months ago)
- Versions: 16
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 129,717 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-cloudant
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:36:59.335Z (about 2 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 179,320 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-atlas
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.2.4 (published over 4 years ago)
- Last Synced: 2026-04-03T01:26:52.017Z (5 months ago)
- Versions: 17
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-github
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 2.1.0 (published about 4 years ago)
- Last Synced: 2026-04-01T16:09:26.260Z (5 months ago)
- Versions: 3
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 31,833 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-hdfs
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.11 (published about 6 years ago)
- Last Synced: 2026-09-06T02:27:46.374Z (4 days ago)
- Versions: 15
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 130,265 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-docker
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.11 (published about 6 years ago)
- Last Synced: 2026-07-24T20:36:50.084Z (about 2 months ago)
- Versions: 15
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 131,437 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-papermill
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-04-01T16:13:44.327Z (5 months ago)
- Versions: 9
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 55,626 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-apache-pinot
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.2.1 (published almost 4 years ago)
- Last Synced: 2026-03-24T04:08:34.539Z (6 months ago)
- Versions: 9
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 50,783 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-druid
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:35:57.999Z (about 2 months ago)
- Versions: 18
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 158,004 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-trino
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.1.0 (published almost 4 years ago)
- Last Synced: 2026-03-24T04:09:13.696Z (6 months ago)
- Versions: 12
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 73,975 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-telegram
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-03-27T16:04:32.404Z (6 months ago)
- Versions: 7
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 46,742 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-apache-sqoop
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-03-24T04:09:01.050Z (6 months ago)
- Versions: 8
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 36,888 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-apache-druid
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.2.1 (published almost 4 years ago)
- Last Synced: 2026-03-14T13:29:48.782Z (6 months ago)
- Versions: 14
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 67,463 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-hashicorp
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-04-01T16:13:31.462Z (5 months ago)
- Versions: 5
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 65,086 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-leveldb
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-08-28T15:29:04.253Z (13 days ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 501,207 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-google_auth
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-04-01T15:11:03.760Z (5 months ago)
- Versions: 22
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-postgres
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-03-14T13:24:20.758Z (6 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 184,558 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-pandas
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published almost 4 years ago)
- Last Synced: 2026-03-24T04:08:43.586Z (6 months ago)
- Versions: 15
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 501,396 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-celery
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:36:29.981Z (about 2 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 307,054 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-presto
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.0.1 (published about 4 years ago)
- Last Synced: 2026-04-01T16:11:40.886Z (5 months ago)
- Versions: 12
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 71,539 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-celery
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-04-01T16:11:03.642Z (5 months ago)
- Versions: 7
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 72,894 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-jdbc
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.2.1 (published almost 4 years ago)
- Last Synced: 2026-04-01T16:13:11.128Z (5 months ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 53,785 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-crypto
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-04-03T01:26:51.548Z (5 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-discord
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-03-24T04:09:23.926Z (6 months ago)
- Versions: 7
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 43,463 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-kubernetes
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-03-16T02:09:20.130Z (6 months ago)
- Versions: 18
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 158,282 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-microsoft-mssql
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.2.1 (published almost 4 years ago)
- Last Synced: 2026-07-24T20:35:46.702Z (about 2 months ago)
- Versions: 11
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 64,459 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-cncf-kubernetes
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.4.0 (published almost 4 years ago)
- Last Synced: 2026-08-25T21:29:38.698Z (15 days ago)
- Versions: 20
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 143,053 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-qds
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-03-24T04:08:58.377Z (6 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 177,828 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-apache-hive
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.0.1 (published almost 4 years ago)
- Last Synced: 2026-09-03T17:29:42.166Z (6 days ago)
- Versions: 16
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 233,789 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-grpc
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.0.0 (published about 4 years ago)
- Last Synced: 2026-04-01T16:11:36.337Z (5 months ago)
- Versions: 7
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 42,009 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-google
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 8.3.0 (published almost 4 years ago)
- Last Synced: 2026-03-24T04:06:39.558Z (6 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 126,221 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-cassandra
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:36:19.917Z (about 2 months ago)
- Versions: 18
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-vertica
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-07-24T20:36:58.903Z (about 2 months ago)
- Versions: 19
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 180,308 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-apache-beam
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.0.0 (published about 4 years ago)
- Last Synced: 2026-08-28T15:29:03.538Z (13 days ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 75,119 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-neo4j
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 3.1.0 (published about 4 years ago)
- Last Synced: 2026-08-28T15:29:08.586Z (13 days ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 56,627 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-opsgenie
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 4.0.0 (published about 4 years ago)
- Last Synced: 2026-08-28T15:28:17.709Z (13 days ago)
- Versions: 9
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 45,341 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: apache-airflow-providers-asana
- Homepage: https://github.com/apache/airflow/
- Licenses: Apache-2.0
- Latest release: 2.0.1 (published about 4 years ago)
- Last Synced: 2026-08-28T15:29:03.457Z (13 days ago)
- Versions: 3
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 28,148 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-snowflake
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-08-28T15:29:06.977Z (13 days ago)
- Versions: 5
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 64,138 Total
-
Rankings:
- Forks count: 0.165%
- Stargazers count: 0.469%
- Average: 21.459%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
conda-forge.org: airflow-with-mongo
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 1.10.15 (published over 5 years ago)
- Last Synced: 2026-03-24T04:09:21.511Z (6 months ago)
- Versions: 18
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 158,162 Total
-
Rankings:
- Forks count: 0.799%
- Stargazers count: 1.103%
- Average: 21.776%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
anaconda.org: airflow-with-hdfs
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:35:50.891Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 575 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-ssh
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:36:23.332Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-github_enterprise
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:35:24.166Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,271 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-mysql
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:36:28.989Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-async
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-03-22T19:11:08.781Z (6 months ago)
- Versions: 4
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,887 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-deprecated-api
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-04-01T13:27:19.352Z (5 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,182 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-webhdfs
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:35:56.297Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 566 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-apache-atlas
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:36:54.574Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,447 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-password
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-04-02T15:47:43.854Z (5 months ago)
- Versions: 4
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-mssql
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:35:48.557Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 573 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-google_auth
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:36:00.281Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,203 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-databricks
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:36:07.025Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 581 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-statsd
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:35:33.340Z (about 2 months ago)
- Versions: 4
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,786 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-druid
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-03-22T19:11:07.206Z (6 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 593 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-leveldb
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:36:28.985Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,186 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-azure_blob_storage
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-04-08T09:54:53.341Z (5 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-qds
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:36:38.835Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 583 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-jenkins
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-03-24T04:11:38.479Z (6 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 550 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-celery
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:36:25.659Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-emr
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: http://airflow.apache.org
- Licenses: Apache 2.0
- Latest release: 1.10.12 (published almost 6 years ago)
- Last Synced: 2026-07-24T20:35:57.157Z (about 2 months ago)
- Versions: 2
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
anaconda.org: airflow-with-cgroups
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 line utilities make 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. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- Homepage: https://airflow.apache.org
- Licenses: Apache-2.0
- Latest release: 2.4.3 (published over 3 years ago)
- Last Synced: 2026-07-24T20:35:56.525Z (about 2 months ago)
- Versions: 4
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,870 Total
-
Rankings:
- Forks count: 0.744%
- Stargazers count: 1.217%
- Average: 24.865%
- Dependent packages count: 39.804%
- Dependent repos count: 57.694%
pypi.org: pano-apache-airflow
Programmatically author, schedule and monitor data pipelines
- Homepage: https://panoramichillscapital.com/
- Documentation: https://pano-apache-airflow.readthedocs.io/
- Licenses: Apache License 2.0
- Latest release: 2.7.0.dev0 (published over 3 years ago)
- Last Synced: 2026-09-08T14:33:19.258Z (1 day ago)
- Versions: 1
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 28 Last month
-
Rankings:
- Forks count: 0.094%
- Stargazers count: 0.253%
- Dependent packages count: 9.906%
- Average: 29.897%
- Dependent repos count: 55.752%
- Downloads: 83.481%
pypi.org: apache-airflow-providers-fab
Provider package apache-airflow-providers-fab for Apache Airflow
- Homepage:
- Documentation: https://airflow.apache.org/docs/apache-airflow-providers-fab/3.8.1
- Licenses: Apache-2.0
- Latest release: 3.8.1 (published 11 days ago)
- Last Synced: 2026-09-09T09:09:29.770Z (about 19 hours ago)
- Versions: 123
- Dependent Packages: 2
- Dependent Repositories: 0
- Downloads: 13,871,338 Last month
-
Rankings:
- Dependent packages count: 10.111%
- Average: 38.411%
- Dependent repos count: 66.71%
- Maintainers (1)
- Advisories:
Dependencies
- ${PYTHON_BASE_IMAGE} latest build
- scratch latest build