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in repository https://gitbox.apache.org/repos/asf/incubator-training.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 7a3160c Site checkin for project Training: Website
7a3160c is described below
commit 7a3160c7c6c7e2c3b3cbb512cb1925cf4b1b1f25
Author: jenkins <[email protected]>
AuthorDate: Tue Dec 3 23:15:56 2024 +0000
Site checkin for project Training: Website
---
dependencies.html | 12 ++---
.../airflow/WEB-INF/classes/images/AirflowLogo.png | Bin 0 -> 55786 bytes
presentations/airflow/images/AirflowLogo.png | Bin 0 -> 55786 bytes
presentations/airflow/index.html | 52 ++++++++++++---------
presentations/training/demo/images/dot-example.svg | 34 +++++++-------
presentations/training/demo/images/nwdiag-test.png | Bin 23371 -> 23329 bytes
presentations/training/demo/index.html | 2 +-
7 files changed, 54 insertions(+), 46 deletions(-)
diff --git a/dependencies.html b/dependencies.html
index b90fb07..8edf9e8 100644
--- a/dependencies.html
+++ b/dependencies.html
@@ -743,8 +743,8 @@
</tr>
<tr class="a">
<td align="left">training-content-apache-airflow-1.0.0-SNAPSHOT.war</td>
- <td align="right">2.9 MB</td>
- <td align="right">212</td>
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+ <td align="right">214</td>
<td align="right">0</td>
<td align="right">0</td>
<td align="center">-</td>
@@ -923,8 +923,8 @@
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<tr class="a">
<td align="right">20</td>
- <td align="right">204.3 MB</td>
- <td align="right">4442</td>
+ <td align="right">204.4 MB</td>
+ <td align="right">4444</td>
<td align="right">7</td>
<td align="right">3</td>
<td align="center">1.8</td>
@@ -932,8 +932,8 @@
</tr>
<tr class="b">
<td align="right">compile: 20</td>
- <td align="right">compile: 204.3 MB</td>
- <td align="right">compile: 4442</td>
+ <td align="right">compile: 204.4 MB</td>
+ <td align="right">compile: 4444</td>
<td align="right">compile: 7</td>
<td align="right">compile: 3</td>
<td align="center">1.8</td>
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diff --git a/presentations/airflow/index.html b/presentations/airflow/index.html
index 0c6aca8..35b5552 100644
--- a/presentations/airflow/index.html
+++ b/presentations/airflow/index.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html lang="en"><head><meta charset="utf-8" /><meta
name="viewport" content="width=device-width, initial-scale=1.0,
maximum-scale=1.0, user-scalable=no, minimal-ui" /><title>What is Apache
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href="css/c3.min.css" rel="stylesheet" /><script src="js/d3.min.js"
charset="utf-8"></script><script src="js/c3.min.js"></script><link rel= [...]
+<!DOCTYPE html><html lang="en"><head><meta charset="utf-8" /><meta
name="viewport" content="width=device-width, initial-scale=1.0,
maximum-scale=1.0, user-scalable=no, minimal-ui" /><title>Apache
Airflow</title><meta name="description" content="Introduction to Apache
Airflow" /><meta name="keywords" content="Apache, Airflow" /><meta
name="author" content="Your Name" /><link rel="stylesheet"
href="reveal.js-5.1.0/dist/reset.css" /><link rel="stylesheet"
href="reveal.js-5.1.0/dist/reveal.c [...]
float: right
}
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ul.byline li + li {
margin-top: 0.25em;
}
-</style></head><body><div class="header"><div class="left"></div><div
class="right"></div></div><div class="reveal"><div class="slides"><section
id="_what_is_apache_airflow"><h2>What is Apache Airflow?</h2><div
class="slide-content"><div class="paragraph"><p>Airflow is a platform to
programmatically author, schedule and monitor
workflows.</p></div></div></section>
-<section id="_airflow_principles"><h2>Airflow Principles</h2><div
class="slide-content"><div class="paragraph"><p>Airflow is built on following
principles:</p></div>
-<div
class="ulist"><ul><li><p>Scalable</p></li><li><p>Dynamic</p></li><li><p>Extensible</p></li><li><p>Elegant</p></li></ul></div></div></section>
-<section id="_airflow_principle_scalable"><h2>Airflow Principle:
Scalable</h2><div class="slide-content"><div class="paragraph"><p>Airflow has a
modular architecture and uses a message queue to orchestrate an arbitrary
number of workers. Airflow is ready to scale to
infinity.</p></div></div></section>
-<section id="_airflow_principle_dynamic"><h2>Airflow Principle:
Dynamic</h2><div class="slide-content"><div class="paragraph"><p>Airflow
pipelines are defined in Python, allowing for dynamic pipeline generation. This
allows for writing code that instantiates pipelines
dynamically.</p></div></div></section>
-<section id="_airflow_principle_extensible"><h2>Airflow Principle:
Extensible</h2><div class="slide-content"><div class="paragraph"><p>Easily
define your own operators and extend libraries to fit the level of abstraction
that suits your environment.</p></div></div></section>
-<section id="_airflow_principle_elegant"><h2>Airflow Principle:
Elegant</h2><div class="slide-content"><div class="paragraph"><p>Airflow
pipelines are lean and explicit. Parametrization is built into its core using
the powerful Jinja templating engine.</p></div></div></section>
-<section id="_airflow_features"><h2>Airflow Features</h2><div
class="slide-content"><div class="paragraph"><p>Apache Airflow provides
following features:</p></div>
+</style><link rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css";
/><link rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/v4-shims.min.css";
/></head><body><div class="header"><div class="left"></div><div
class="right"></div></div><div class="reveal"><div class="slides"><section
id="apache-airflow"><h2>Apache Airflow</h2><div class="slide-content"><div
class="imageblock"><img src="images/AirflowLogo.png" [...]
+<aside class="notes"><div class="paragraph"><p>Introduce yourself, and your
role in the Apache Airflow community.</p></div></aside></div></section>
+<section id="what-is-apache-airflow"><h2>What is Apache Airflow?</h2><div
class="slide-content"><div class="paragraph"><p>Airflow is a platform to
programmatically author, schedule and monitor
workflows.</p></div></div></section>
+<section id="airflow-principles"><h2>Airflow Principles</h2><div
class="slide-content"><div class="paragraph"><p>Airflow is built on following
principles:</p></div>
+<div
class="ulist"><ul><li><p>Scalable</p></li><li><p>Dynamic</p></li><li><p>Extensible</p></li><li><p>Elegant</p></li></ul></div>
+<aside class="notes"><div class="paragraph"><p>Each principle has a slide
following that explains it, so you don’t need
+to explain this here.</p></div></aside></div></section>
+<section id="airflow-principle-scalable"><h2>Airflow Principle:
Scalable</h2><div class="slide-content"><div class="paragraph"><p>Airflow has a
modular architecture and uses a message queue to orchestrate an arbitrary
number of workers. Airflow is ready to scale to
infinity.</p></div></div></section>
+<section id="airflow-principle-dynamic"><h2>Airflow Principle:
Dynamic</h2><div class="slide-content"><div class="paragraph"><p>Airflow
pipelines are defined in Python, allowing for dynamic pipeline generation. This
allows for writing code that instantiates pipelines
dynamically.</p></div></div></section>
+<section id="airflow-principle-extensible"><h2>Airflow Principle:
Extensible</h2><div class="slide-content"><div class="paragraph"><p>Easily
define your own operators and extend libraries to fit the level of abstraction
that suits your environment.</p></div></div></section>
+<section id="airflow-principle-elegant"><h2>Airflow Principle:
Elegant</h2><div class="slide-content"><div class="paragraph"><p>Airflow
pipelines are lean and explicit. Parametrization is built into its core using
the powerful Jinja templating engine.</p></div></div></section>
+<section id="airflow-features"><h2>Airflow Features</h2><div
class="slide-content"><div class="paragraph"><p>Apache Airflow provides
following features:</p></div>
<div class="ulist"><ul><li><p>Pure Python</p></li><li><p>Useful
UI</p></li><li><p>Robust Integrations</p></li><li><p>Easy to
Use</p></li><li><p>Open Source</p></li></ul></div></div></section>
-<section id="_airflow_feature_pure_python"><h2>Airflow Feature: Pure
Python</h2><div class="slide-content"><div class="paragraph"><p>No more
command-line or XML black-magic! Use standard Python features to create your
workflows, including date time formats for scheduling and loops to dynamically
generate tasks. This allows you to maintain full flexibility when building your
workflows.</p></div></div></section>
-<section id="_airflow_feature_useful_ui"><h2>Airflow Feature: Useful
UI</h2><div class="slide-content"><div class="paragraph"><p>Monitor, schedule
and manage your workflows via a robust and modern web application. No need to
learn old, cron-like interfaces. You always have full insight into the status
and logs of completed and ongoing tasks.</p></div></div></section>
-<section id="_airflow_feature_robust_integrations"><h2>Airflow Feature: Robust
Integrations</h2><div class="slide-content"><div class="paragraph"><p>Airflow
provides many plug-and-play operators that are ready to execute your tasks on
Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other
third-party services. This makes Airflow easy to apply to current
infrastructure and extend to next-gen technologies.</p></div></div></section>
-<section id="_airflow_feature_easy_to_use"><h2>Airflow Feature: Easy to
Use</h2><div class="slide-content"><div class="paragraph"><p>Anyone with Python
knowledge can deploy a workflow. Apache Airflow does not limit the scope of
your pipelines; you can use it to build ML models, transfer data, manage your
infrastructure, and more.</p></div></div></section>
-<section id="_airflow_feature_open_source"><h2>Airflow Feature: Open
Source</h2><div class="slide-content"><div class="paragraph"><p>Wherever you
want to share your improvement you can do this by opening a PR. It’s simple as
that, no barriers, no prolonged procedures. Airflow has many active users who
willingly share their experiences. Have any questions? Check out our buzzing
slack.</p></div></div></section>
-<section id="_airflow_integrations"><h2>Airflow Integrations</h2><div
class="slide-content"><div class="paragraph"><p>Airflow supports following main
integrations:</p></div>
-<div class="ulist"><ul><li><p>Apache Sqoop</p></li><li><p>Google Cloud
Pub/Sub</p></li><li><p>Amazon CloudWatch Logs</p></li><li><p>Google Kubernetes
Engine</p></li><li><p>Google Machine learning</p></li><li><p>Amazon
Athena</p></li></ul></div></div></section>
-<section id="_airflow_providers_packages"><h2>Airflow Providers
packages</h2><div class="slide-content"><div class="paragraph"><p>Airflow has
providers packages include integrations with third party integrations. They are
updated independently of the Apache Airflow core. Some of these are:</p></div>
-<div
class="ulist"><ul><li><p>Airbyte</p></li><li><p>Amazon</p></li><li><p>Apache
Beam</p></li><li><p>Apache
Cassandra</p></li><li><p>JIRA</p></li></ul></div></div></section>
-<section id="_airflow_docker_stack"><h2>Airflow Docker stack</h2><div
class="slide-content"><div class="paragraph"><p>Airflow has an official
Dockerfile and Docker image published in DockerHub as a convenience package for
installation. You can extend and customize the image according to your
requirements and use it in your own deployments.</p></div></div></section>
-<section id="_further_sources"><h2>Further Sources</h2><div
class="slide-content"><div class="paragraph"><p>Refer official documents on
Apache Airflow here:</p></div>
+<section id="airflow-feature-pure-python"><h2>Airflow Feature: Pure
Python</h2><div class="slide-content"><div class="paragraph"><p>No more
command-line or XML black-magic! Use standard Python features to create your
workflows, including date time formats for scheduling and loops to dynamically
generate tasks. This allows you to maintain full flexibility when building your
workflows.</p></div></div></section>
+<section id="airflow-feature-useful-ui"><h2>Airflow Feature: Useful
UI</h2><div class="slide-content"><div class="paragraph"><p>Monitor, schedule
and manage your workflows via a robust and modern web application. No need to
learn old, cron-like interfaces. You always have full insight into the status
and logs of completed and ongoing tasks.</p></div></div></section>
+<section id="airflow-feature-robust-integrations"><h2>Airflow Feature: Robust
Integrations</h2><div class="slide-content"><div class="paragraph"><p>Airflow
provides many plug-and-play operators that are ready to execute your tasks on
Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other
third-party services. This makes Airflow easy to apply to current
infrastructure and extend to next-gen technologies.</p></div></div></section>
+<section id="airflow-feature-easy-to-use"><h2>Airflow Feature: Easy to
Use</h2><div class="slide-content"><div class="paragraph"><p>Anyone with Python
knowledge can deploy a workflow. Apache Airflow does not limit the scope of
your pipelines; you can use it to build ML models, transfer data, manage your
infrastructure, and more.</p></div></div></section>
+<section id="airflow-feature-open-source"><h2>Airflow Feature: Open
Source</h2><div class="slide-content"><div class="paragraph"><p>Wherever you
want to share your improvement you can do this by opening a PR. It’s simple as
that, no barriers, no prolonged procedures. Airflow has many active users who
willingly share their experiences. Have any questions? Check out our buzzing
slack.</p></div>
+<aside class="notes"><div class="paragraph"><p>Where is the Slack? How does
one join?</p></div></aside></div></section>
+<section id="airflow-integrations"><h2>Airflow Integrations</h2><div
class="slide-content"><div class="paragraph"><p>Airflow supports following main
integrations:</p></div>
+<div class="ulist"><ul><li><p>Apache Sqoop</p></li><li><p>Google Cloud
Pub/Sub</p></li><li><p>Amazon CloudWatch Logs</p></li><li><p>Google Kubernetes
Engine</p></li><li><p>Google Machine learning</p></li><li><p>Amazon
Athena</p></li></ul></div>
+<aside class="notes"><div class="paragraph"><p>Ensure that this list reflects
the latest state of the art.</p></div></aside></div></section>
+<section id="airflow-providers-packages"><h2>Airflow Providers
packages</h2><div class="slide-content"><div class="paragraph"><p>Airflow has
providers packages include integrations with third party integrations. They are
updated independently of the Apache Airflow core. Some of these are:</p></div>
+<div
class="ulist"><ul><li><p>Airbyte</p></li><li><p>Amazon</p></li><li><p>Apache
Beam</p></li><li><p>Apache Cassandra</p></li><li><p>JIRA</p></li></ul></div>
+<aside class="notes"><div class="paragraph"><p>This section should be
periodically updated to reflect the current state
+of the project.</p></div></aside></div></section>
+<section id="airflow-docker-stack"><h2>Airflow Docker stack</h2><div
class="slide-content"><div class="paragraph"><p>Airflow has an official
Dockerfile and Docker image published in DockerHub as a convenience package for
installation. You can extend and customize the image according to your
requirements and use it in your own deployments.</p></div></div></section>
+<section id="further-sources"><h2>Further Sources</h2><div
class="slide-content"><div class="paragraph"><p>Refer official documents on
Apache Airflow here:</p></div>
<div class="ulist"><ul><li><p><strong>Airflow Documentation</strong>: <a
href="https://airflow.apache.org/docs/";
class="bare">https://airflow.apache.org/docs/</a></p></li><li><p><strong>Airflow
Usecases</strong>: <a href="https://airflow.apache.org/use-cases/";
class="bare">https://airflow.apache.org/use-cases/</a></p></li></ul></div></div></section></div></div><div
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b/presentations/training/demo/images/dot-example.svg
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/></head><body><div class="header"><div class="left"></div><div
class="right"></div></div><div class="reveal"><div class="slides"><section
id="example-presentation"><h2>Example Presentation</h2><div
class="slide-content"><div class="paragraph"><p>Doc Writer <<a hr [...]
-2024-10-30
+2024-12-03
:revnumber: 1.0.0-SNAPSHOT
:example-caption!:</p></div></div></section>
<section id="introduction"><h2>Introduction</h2><div
class="slide-content"><div class="ulist"><ul><li class="fragment"><p>This is a
presentation for an example project.</p></li><li class="fragment"><p>This
project does something.</p></li><li class="fragment"><p>We just haven’t
decided what that is yet.</p></li></ul></div></div></section>
