This is an automated email from the ASF dual-hosted git repository.
github-bot pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/airflow-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 048a9d4 Update asf-site to output generated at 73e3138
048a9d4 is described below
commit 048a9d4ef42b9143dc754201a49f74460cbe953b
Author: mik-laj <[email protected]>
AuthorDate: Thu Sep 3 11:43:32 2020 +0000
Update asf-site to output generated at 73e3138
---
blog/airflow-1.10.10/index.html | 4 +-
blog/airflow-1.10.12/index.html | 4 +-
blog/airflow-1.10.8-1.10.9/index.html | 4 +-
blog/airflow-survey/index.html | 4 +-
blog/announcing-new-website/index.html | 4 +-
blog/apache-airflow-for-newcomers/index.html | 4 +-
.../index.html | 4 +-
.../index.html | 4 +-
.../index.html | 4 +-
.../index.html | 4 +-
.../index.html | 4 +-
.../index.html | 4 +-
index.html | 32 +++---
search/index.html | 4 +-
sitemap.xml | 115 +++++++++++----------
use-cases/adobe/index.html | 4 +-
use-cases/big-fish-games/index.html | 4 +-
use-cases/dish/index.html | 4 +-
use-cases/experity/index.html | 4 +-
use-cases/index.html | 22 ++++
use-cases/index.xml | 38 +++++++
use-cases/onefootball/index.html | 8 +-
use-cases/{onefootball => sift}/index.html | 51 ++++-----
usecase-logos/sift_logo.png | Bin 0 -> 42233 bytes
24 files changed, 202 insertions(+), 132 deletions(-)
diff --git a/blog/airflow-1.10.10/index.html b/blog/airflow-1.10.10/index.html
index 247c1dd..6f7f2b4 100644
--- a/blog/airflow-1.10.10/index.html
+++ b/blog/airflow-1.10.10/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2020-04-09T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Apache Airflow 1.10.10">
<meta itemprop="description" content="We are happy to present Apache Airflow
1.10.10">
<meta itemprop="datePublished" content="2020-04-09T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="1143">
diff --git a/blog/airflow-1.10.12/index.html b/blog/airflow-1.10.12/index.html
index 6a43dbf..f7aee46 100644
--- a/blog/airflow-1.10.12/index.html
+++ b/blog/airflow-1.10.12/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2020-08-25T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Apache Airflow 1.10.12">
<meta itemprop="description" content="We are happy to present Apache Airflow
1.10.12">
<meta itemprop="datePublished" content="2020-08-25T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="911">
diff --git a/blog/airflow-1.10.8-1.10.9/index.html
b/blog/airflow-1.10.8-1.10.9/index.html
index 0f57cde..c163dd3 100644
--- a/blog/airflow-1.10.8-1.10.9/index.html
+++ b/blog/airflow-1.10.8-1.10.9/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2020-02-23T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Apache Airflow 1.10.8 & 1.10.9">
<meta itemprop="description" content="We are happy to present the new 1.10.8
and 1.10.9 releases of Apache Airflow.">
<meta itemprop="datePublished" content="2020-02-23T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="437">
diff --git a/blog/airflow-survey/index.html b/blog/airflow-survey/index.html
index d84a68e..3766a1b 100644
--- a/blog/airflow-survey/index.html
+++ b/blog/airflow-survey/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2019-12-11T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Airflow Survey 2019">
<meta itemprop="description" content="Receiving and adjusting to our users’
feedback is a must. Let’s see who Airflow users are, how they play with it, and
what they miss.">
<meta itemprop="datePublished" content="2019-12-11T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="1775">
diff --git a/blog/announcing-new-website/index.html
b/blog/announcing-new-website/index.html
index ab8a10c..e3240bb 100644
--- a/blog/announcing-new-website/index.html
+++ b/blog/announcing-new-website/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2019-12-11T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="New Airflow website">
<meta itemprop="description" content="We are thrilled about our new website!">
<meta itemprop="datePublished" content="2019-12-11T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="282">
diff --git a/blog/apache-airflow-for-newcomers/index.html
b/blog/apache-airflow-for-newcomers/index.html
index f90ddc3..74085a2 100644
--- a/blog/apache-airflow-for-newcomers/index.html
+++ b/blog/apache-airflow-for-newcomers/index.html
@@ -37,14 +37,14 @@ Authoring Workflow in Apache Airflow. Airflow makes it easy
to author workflows
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2020-08-17T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Apache Airflow For Newcomers">
<meta itemprop="description" content="Apache Airflow is a platform to
programmatically author, schedule, and monitor workflows. A workflow is a
sequence of tasks that processes a set of data. You can think of workflow as
the path that describes how tasks go from being undone to done. Scheduling, on
the other hand, is the process of planning, controlling, and optimizing when a
particular task should be done.
Authoring Workflow in Apache Airflow. Airflow makes it easy to author
workflows using python scripts.">
<meta itemprop="datePublished" content="2020-08-17T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="1070">
diff --git
a/blog/apache-con-europe-2019-thoughts-and-insights-by-airflow-committers/index.html
b/blog/apache-con-europe-2019-thoughts-and-insights-by-airflow-committers/index.html
index 420e617..43f7b30 100644
---
a/blog/apache-con-europe-2019-thoughts-and-insights-by-airflow-committers/index.html
+++
b/blog/apache-con-europe-2019-thoughts-and-insights-by-airflow-committers/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2019-11-22T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="ApacheCon Europe 2019 — Thoughts and Insights
by Airflow Committers">
<meta itemprop="description" content="Here come some thoughts by Airflow
committers and contributors from the ApacheCon Europe 2019. Get to know the ASF
community!">
<meta itemprop="datePublished" content="2019-11-22T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="138">
diff --git a/blog/documenting-using-local-development-environments/index.html
b/blog/documenting-using-local-development-environments/index.html
index 9f96d11..05382bf 100644
--- a/blog/documenting-using-local-development-environments/index.html
+++ b/blog/documenting-using-local-development-environments/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2019-11-22T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Documenting using local development
environment">
<meta itemprop="description" content="The story behind documenting local
development environment of Apache Airflow">
<meta itemprop="datePublished" content="2019-11-22T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="256">
diff --git
a/blog/experience-in-google-season-of-docs-2019-with-apache-airflow/index.html
b/blog/experience-in-google-season-of-docs-2019-with-apache-airflow/index.html
index a49f1ae..cfc3598 100644
---
a/blog/experience-in-google-season-of-docs-2019-with-apache-airflow/index.html
+++
b/blog/experience-in-google-season-of-docs-2019-with-apache-airflow/index.html
@@ -37,14 +37,14 @@ About Me I have been writing tech articles on medium as
well as my blog for the
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2019-12-20T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Experience in Google Season of Docs 2019 with
Apache Airflow">
<meta itemprop="description" content="I came across Google Season of Docs
(GSoD) almost by accident, thanks to my extensive HackerNews and Twitter
addiction. I was familiar with the Google Summer of Code but not with this
program. It turns out it was the inaugural phase. I read the details, and the
process felt a lot like GSoC except that this was about documentation.
About Me I have been writing tech articles on medium as well as my blog for
the past 1.">
<meta itemprop="datePublished" content="2019-12-20T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="1521">
diff --git a/blog/experience-with-airflow-as-an-outreachy-intern/index.html
b/blog/experience-with-airflow-as-an-outreachy-intern/index.html
index cfd9900..daaaed6 100644
--- a/blog/experience-with-airflow-as-an-outreachy-intern/index.html
+++ b/blog/experience-with-airflow-as-an-outreachy-intern/index.html
@@ -37,14 +37,14 @@ Contribution Period The first thing I had to do was choose
a project under an or
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2020-08-30T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Journey with Airflow as an Outreachy Intern">
<meta itemprop="description" content="Outreachy is a program which organises
three months paid internships with FOSS projects for people who are typically
underrepresented in those projects.
Contribution Period The first thing I had to do was choose a project under an
organisation. After going through all the projects I chose “Extending the REST
API of Apache Airflow”, because I had a good idea of what REST API(s) are, so I
thought it would be easier to get started with the contributions.">
<meta itemprop="datePublished" content="2020-08-30T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="532">
diff --git a/blog/implementing-stable-api-for-apache-airflow/index.html
b/blog/implementing-stable-api-for-apache-airflow/index.html
index be30470..1c5d2a1 100644
--- a/blog/implementing-stable-api-for-apache-airflow/index.html
+++ b/blog/implementing-stable-api-for-apache-airflow/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2020-07-19T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Implementing Stable API for Apache Airflow">
<meta itemprop="description" content="An Outreachy intern's progress
report on contributing to Apache Airflow REST API.">
<meta itemprop="datePublished" content="2020-07-19T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="703">
diff --git a/blog/its-a-breeze-to-develop-apache-airflow/index.html
b/blog/its-a-breeze-to-develop-apache-airflow/index.html
index 2047ec2..44f8169 100644
--- a/blog/its-a-breeze-to-develop-apache-airflow/index.html
+++ b/blog/its-a-breeze-to-develop-apache-airflow/index.html
@@ -36,13 +36,13 @@
<meta property="og:image" content="/images/feature-image.png" />
<meta property="article:published_time" content="2019-11-22T00:00:00+00:00" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="It's a "Breeze" to develop Apache
Airflow">
<meta itemprop="description" content="A Principal Software Engineer's
journey to developer productivity. Learn how Jarek and his team speeded up and
simplified Airflow development for the community.">
<meta itemprop="datePublished" content="2019-11-22T00:00:00+00:00" />
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="127">
diff --git a/index.html b/index.html
index 9ff4539..c6b86b2 100644
--- a/index.html
+++ b/index.html
@@ -1226,12 +1226,12 @@ if (!doNotTrack) {
<div id="integrations-container" class="list-items">
- <a class="list-item"
href="/docs/stable/integration.html#azure-microsoft-azure">
+ <a class="list-item"
href="/docs/stable/integration.html#gcp-google-cloud-platform">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Azure Files</span>
+ <span class="box-event__integration--name">Cloud Storage (GCS)</span>
</div>
</div>
@@ -1239,12 +1239,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#software-integrations">
+ <a class="list-item"
href="/docs/stable/integration.html#protocol-integrations">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">MongoDB</span>
+ <span class="box-event__integration--name">Filesystem</span>
</div>
</div>
@@ -1252,12 +1252,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#software-integrations">
+ <a class="list-item"
href="/docs/stable/integration.html#gcp-google-cloud-platform">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Redis</span>
+ <span class="box-event__integration--name">Storage Transfer
Service</span>
</div>
</div>
@@ -1265,12 +1265,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#asf-apache-software-foundation">
+ <a class="list-item"
href="/docs/stable/integration.html#aws-amazon-web-services">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Apache Spark</span>
+ <span class="box-event__integration--name">Amazon SageMaker</span>
</div>
</div>
@@ -1278,12 +1278,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#asf-apache-software-foundation">
+ <a class="list-item"
href="/docs/stable/integration.html#software-integrations">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Apache Sqoop</span>
+ <span class="box-event__integration--name">MongoDB</span>
</div>
</div>
@@ -1291,12 +1291,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#aws-amazon-web-services">
+ <a class="list-item"
href="/docs/stable/integration.html#gcp-google-cloud-platform">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Amazon Redshift</span>
+ <span class="box-event__integration--name">Cloud Data Loss Prevention
(DLP)</span>
</div>
</div>
@@ -1304,12 +1304,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#service-integrations">
+ <a class="list-item"
href="/docs/stable/integration.html#aws-amazon-web-services">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Dingding</span>
+ <span class="box-event__integration--name">Amazon EMR</span>
</div>
</div>
@@ -1317,12 +1317,12 @@ if (!doNotTrack) {
- <a class="list-item"
href="/docs/stable/integration.html#aws-amazon-web-services">
+ <a class="list-item"
href="/docs/stable/integration.html#gcp-google-cloud-platform">
<div class="card">
<div class="box-event box-event__integration">
- <span class="box-event__integration--name">Amazon Kinesis Data
Firehose</span>
+ <span class="box-event__integration--name">Cloud Vision</span>
</div>
</div>
diff --git a/search/index.html b/search/index.html
index b93e313..4cbca79 100644
--- a/search/index.html
+++ b/search/index.html
@@ -35,12 +35,12 @@
<meta property="og:url" content="/search/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Search Results">
<meta itemprop="description" content="">
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="0">
diff --git a/sitemap.xml b/sitemap.xml
index f0e94fa..2cf4022 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -4,217 +4,217 @@
<url>
<loc>/docs/overview/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/docs/tasks/beds/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/docs/tasks/ponycopters/configuring-ponycopters/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/docs/getting-started/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
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</url>
<url>
<loc>/docs/examples/</loc>
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</url>
<url>
<loc>/docs/tasks/ponycopters/launching-ponycopters/</loc>
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</url>
<url>
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</url>
<url>
<loc>/docs/tasks/porridge/</loc>
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</url>
<url>
<loc>/docs/concepts/</loc>
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+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
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</url>
<url>
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</url>
<url>
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</url>
<url>
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</url>
<url>
<loc>/docs/reference/</loc>
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</url>
<url>
<loc>/docs/contribution-guidelines/</loc>
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</url>
<url>
<loc>/docs/</loc>
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</url>
<url>
<loc>/blog/tags/community/</loc>
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+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/experience-with-airflow-as-an-outreachy-intern/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/tags/</loc>
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+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/airflow-1.10.12/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/tags/release/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/apache-airflow-for-newcomers/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/announcements/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/implementing-stable-api-for-apache-airflow/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/tags/rest-api/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/airflow-1.10.10/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/airflow-1.10.8-1.10.9/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/tags/documentation/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/experience-in-google-season-of-docs-2019-with-apache-airflow/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/airflow-survey/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/announcing-new-website/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/tags/survey/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/tags/users/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/apache-con-europe-2019-thoughts-and-insights-by-airflow-committers/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/tags/development/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/documenting-using-local-development-environments/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/its-a-breeze-to-develop-apache-airflow/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/docs/getting-started/example-page/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/docs/reference/parameter-reference/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/docs/tasks/ponycopters/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/use-cases/adobe/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/use-cases/big-fish-games/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/blog/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
@@ -223,62 +223,67 @@
<url>
<loc>/community/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/use-cases/dish/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/ecosystem/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/use-cases/experity/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/install/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/meetups/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/use-cases/onefootball/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/privacy-notice/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/roadmap/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/search/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
+ </url>
+
+ <url>
+ <loc>/use-cases/sift/</loc>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
<url>
<loc>/use-cases/</loc>
- <lastmod>2020-08-30T12:04:33+02:00</lastmod>
+ <lastmod>2020-09-03T04:39:51-07:00</lastmod>
</url>
</urlset>
\ No newline at end of file
diff --git a/use-cases/adobe/index.html b/use-cases/adobe/index.html
index 2727c6d..e6a0e9b 100644
--- a/use-cases/adobe/index.html
+++ b/use-cases/adobe/index.html
@@ -35,12 +35,12 @@
<meta property="og:url" content="/use-cases/adobe/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Adobe">
<meta itemprop="description" content="What was the problem? Modern big data
platforms need sophisticated data pipelines connecting to many backend services
enabling complex workflows. These workflows need to be deployed, monitored, and
run either on regular schedules or triggered by external events. Adobe
Experience Platform component services architected and built an orchestration
service to enable their users to author, schedule, and monitor complex
hierarchical (including sequential a [...]
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="251">
diff --git a/use-cases/big-fish-games/index.html
b/use-cases/big-fish-games/index.html
index ab6b70b..898addf 100644
--- a/use-cases/big-fish-games/index.html
+++ b/use-cases/big-fish-games/index.html
@@ -35,12 +35,12 @@
<meta property="og:url" content="/use-cases/big-fish-games/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Big Fish Games">
<meta itemprop="description" content="What was the problem? The main challenge
is the lack of standardized ETL workflow orchestration tools. PowerShell and
Python-based ETL frameworks built in-house are currently used for scheduling
and running analytical workloads. However, there is no web UI through which we
can monitor these workflows and it requires additional effort to maintain this
framework. These scheduled jobs based on external dependencies are not well
suited to modern Big Data [...]
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="336">
diff --git a/use-cases/dish/index.html b/use-cases/dish/index.html
index 9dc510f..e3249b0 100644
--- a/use-cases/dish/index.html
+++ b/use-cases/dish/index.html
@@ -35,12 +35,12 @@
<meta property="og:url" content="/use-cases/dish/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Dish">
<meta itemprop="description" content="What was the problem? We faced
increasing complexity managing lengthy crontabs with scheduling being an issue,
this required carefully planning timing due to resource constraints, usage
patterns, and especially custom code needed for retry logic. In the last case,
having to verify success of previous jobs and/or steps prior to running the
next. Furthermore, time to results is important, but we were increasingly
relying on buffers for processing, wher [...]
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="249">
diff --git a/use-cases/experity/index.html b/use-cases/experity/index.html
index de9d432..ee4124c 100644
--- a/use-cases/experity/index.html
+++ b/use-cases/experity/index.html
@@ -36,13 +36,13 @@ How did Apache Airflow help to solve this problem?
Ultimately we decided flexibl
<meta property="og:url" content="/use-cases/experity/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Experity">
<meta itemprop="description" content="What was the problem? We had to deploy
our complex, flagship app to multiple nodes in multiple ways. This required
tasks to communicate across Windows nodes and coordinate timing perfectly. We
did not want to buy an expensive enterprise scheduling tool and needed ultimate
flexibility.
How did Apache Airflow help to solve this problem? Ultimately we decided
flexible, multi-node, DAG capable tooling was key and airflow was one of the
few tools that fit that bill.">
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="191">
diff --git a/use-cases/index.html b/use-cases/index.html
index 0607fdf..b9d6f5b 100644
--- a/use-cases/index.html
+++ b/use-cases/index.html
@@ -536,6 +536,28 @@ if (!doNotTrack) {
</a>
+ <a href="/use-cases/sift/" class="list-item">
+
+
+
+<div class="card">
+ <div class="box-event box-event__case-study hoverable-icon">
+ <div class="box-event__case-study--logo">
+
+ <img src="/usecase-logos/sift_logo.png" alt="Sift logo" />
+
+ </div>
+ <p class="box-event__case-study--quote"
+ >Airflow helped us to define and organize our ML pipeline
dependencies, and empowered us to introduce new, diverse batch …</p>
+
+
+<button class="btn-hollow btn-blue bodytext__medium--cerulean-blue" >Learn
more</button>
+
+ </div>
+</div>
+
+ </a>
+
</div>
<div class="show-more-button">
diff --git a/use-cases/index.xml b/use-cases/index.xml
index 87a68d9..db70030 100644
--- a/use-cases/index.xml
+++ b/use-cases/index.xml
@@ -147,5 +147,43 @@
</description>
</item>
+ <item>
+ <title>Use-Cases: Sift</title>
+ <link>/use-cases/sift/</link>
+ <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
+
+ <guid>/use-cases/sift/</guid>
+ <description>
+
+
+
+
+<h5 id="what-was-the-problem">What was the problem?</h5>
+
+<p>At Sift, we’re constantly training machine learning models that feed
into the core of Sift’s Digital Trust &amp; Safety platform. The platform
gives our customers a way to discern suspicious online behavior from
trustworthy behavior, allowing our customers to protect their online
transactions, maintain the integrity of their content platforms, and keep their
users’ accounts secure. To make this possible, we’ve built model training
pipelines that consist of hundreds of steps [...]
+
+<p>When we built these workflows, we found that we needed a centralized
way to organize the interactions between the many steps in each workflow. But
before Airflow, we didn’t have an easy way to express those dependencies. And
as we added steps to the workflows, it became increasingly difficult to
coordinate their dependencies and keep ML experiments in sync.</p>
+
+<p>It soon became clear that we needed a way to orchestrate both the
scheduled execution of our jobs and the dependencies between steps of not only
a single workflow, but of multiple workflows. We needed a way to dynamically
create several experimental ML workflows at once that could each have their own
code, dependencies, and tasks. Additionally, we needed a way to be able to
monitor the status of tasks, and re-run or restart tasks from any given point
in a workflow with ease.</p>
+
+<h5 id="how-did-apache-airflow-help-to-solve-this-problem">How
did Apache Airflow help to solve this problem?</h5>
+
+<p>Airflow makes it easy to clearly define the interactions between
various jobs, expanding the scope of what we can do in our model training
pipelines. We now have the ability to schedule and coordinate all jobs while
managing the dependencies between them using DAGs. Each of our main workflows,
including our model training pipeline and ETL pipelines, has its own DAG code
that manages its tasks’ dependencies and the execution schedule for the
pipeline. We even define dependencies [...]
+
+<p>As part of our custom Airflow setup, we’ve built out a separate
Airflow ecosystem for short-lived experimental DAGs as well, so that we can
test changes to our jobs or run separate model training pipelines in isolation.
Using deployment scripts that edit our DAGs when we upload them to Airflow, the
same code that powers an existing DAG can be deployed in a separate, isolated
environment with experimental edits. This means that each experiment can have
its own isolated code, runn [...]
+
+<p>Finally, Airflow has given us the ability to manage our tasks’
successes and failures through its user interface. Airflow allows us to track
our tasks’ failures, duration, history, and logs in one central UI, and that
same UI also allows us to easily retry single tasks, branches of a DAG, or
entire DAGs.</p>
+
+<h5 id="what-are-the-results">What are the results?</h5>
+
+<p>Airflow initially gave us a way to solve our existing problems: we
used Airflow to replace rigid crons with well-defined DAG dependencies, to
build isolated ML experiments using short-lived DAGs, and to track our
pipelines’ successes and failures.</p>
+
+<p>But even after that, Airflow helped us to grow beyond those initial
challenges, and expanded the scope of what we could feasibly tackle. Airflow
not only made it easier to manage our ever-expanding ML pipelines, but also
allowed us to create entirely new pipelines, ranging from workflows that back
up our production data to complex ETL pipelines that transform data into
experimentation-ready formats.</p>
+
+<p>Airflow also allowed us to support a more diverse toolset. Shell
scripts, Java, Python, Jupyter notebooks, and more - all of these can be
managed from an Airflow DAG, allowing developers to utilize our data to test
new ideas, generate insights, and improve our models with ease.</p>
+
+ </description>
+ </item>
+
</channel>
</rss>
diff --git a/use-cases/onefootball/index.html b/use-cases/onefootball/index.html
index 864b025..edc6acf 100644
--- a/use-cases/onefootball/index.html
+++ b/use-cases/onefootball/index.html
@@ -36,13 +36,13 @@ On top of that, new data tools appear each month: third
party data sources, clou
<meta property="og:url" content="/use-cases/onefootball/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
<meta itemprop="name" content="Onefootball">
<meta itemprop="description" content="What was the problem? With millions of
daily active users, managing the complexity of data engineering at Onefootball
is a constant challenge. Lengthy crontabs, multiplication of custom API
clients, erosion of confidence in the analytics served, increasing heroism
(“only one person can solve this issue”). Those are the challenges
that most teams face unless they consciously invest in their tools and
processes.
On top of that, new data tools appear each month: third party data sources,
cloud providers solutions, different storage technologies… Managing all
those integrations is costly and brittle, especially for small data engineering
teams that are trying to do more with less.">
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
<meta itemprop="wordCount" content="294">
@@ -469,10 +469,10 @@ if (!doNotTrack) {
<div class="pager">
- <a >
+ <a href="/use-cases/sift/">
-<button class="btn-hollow btn-blue bodytext__medium--cerulean-blue"
disabled>Previous</button>
+<button class="btn-hollow btn-blue bodytext__medium--cerulean-blue"
>Previous</button>
</a>
<a href="/use-cases/experity/">
diff --git a/use-cases/onefootball/index.html b/use-cases/sift/index.html
similarity index 87%
copy from use-cases/onefootball/index.html
copy to use-cases/sift/index.html
index 864b025..8ce1b03 100644
--- a/use-cases/onefootball/index.html
+++ b/use-cases/sift/index.html
@@ -29,21 +29,19 @@
<meta name="msapplication-TileImage" content="/favicons/ms-icon-144x144.png">
<meta name="theme-color" content="#ffffff">
-<title>Onefootball | Apache Airflow</title><meta property="og:title"
content="Onefootball" />
-<meta property="og:description" content="What was the problem? With millions
of daily active users, managing the complexity of data engineering at
Onefootball is a constant challenge. Lengthy crontabs, multiplication of custom
API clients, erosion of confidence in the analytics served, increasing heroism
(“only one person can solve this issue”). Those are the challenges
that most teams face unless they consciously invest in their tools and
processes.
-On top of that, new data tools appear each month: third party data sources,
cloud providers solutions, different storage technologies… Managing all
those integrations is costly and brittle, especially for small data engineering
teams that are trying to do more with less." />
+<title>Sift | Apache Airflow</title><meta property="og:title" content="Sift" />
+<meta property="og:description" content="What was the problem? At Sift, we’re
constantly training machine learning models that feed into the core of Sift’s
Digital Trust & Safety platform. The platform gives our customers a way to
discern suspicious online behavior from trustworthy behavior, allowing our
customers to protect their online transactions, maintain the integrity of their
content platforms, and keep their users’ accounts secure. To make this
possible, we’ve built model tra [...]
<meta property="og:type" content="article" />
-<meta property="og:url" content="/use-cases/onefootball/" />
+<meta property="og:url" content="/use-cases/sift/" />
<meta property="og:image" content="/images/feature-image.png" />
-<meta property="article:modified_time" content="2020-08-30T12:04:33+02:00"
/><meta property="og:site_name" content="Apache Airflow" />
-<meta itemprop="name" content="Onefootball">
-<meta itemprop="description" content="What was the problem? With millions of
daily active users, managing the complexity of data engineering at Onefootball
is a constant challenge. Lengthy crontabs, multiplication of custom API
clients, erosion of confidence in the analytics served, increasing heroism
(“only one person can solve this issue”). Those are the challenges
that most teams face unless they consciously invest in their tools and
processes.
-On top of that, new data tools appear each month: third party data sources,
cloud providers solutions, different storage technologies… Managing all
those integrations is costly and brittle, especially for small data engineering
teams that are trying to do more with less.">
+<meta property="article:modified_time" content="2020-09-03T04:39:51-07:00"
/><meta property="og:site_name" content="Apache Airflow" />
+<meta itemprop="name" content="Sift">
+<meta itemprop="description" content="What was the problem? At Sift, we’re
constantly training machine learning models that feed into the core of Sift’s
Digital Trust & Safety platform. The platform gives our customers a way to
discern suspicious online behavior from trustworthy behavior, allowing our
customers to protect their online transactions, maintain the integrity of their
content platforms, and keep their users’ accounts secure. To make this
possible, we’ve built model traini [...]
-<meta itemprop="dateModified" content="2020-08-30T12:04:33+02:00" />
-<meta itemprop="wordCount" content="294">
+<meta itemprop="dateModified" content="2020-09-03T04:39:51-07:00" />
+<meta itemprop="wordCount" content="641">
@@ -51,9 +49,8 @@ On top of that, new data tools appear each month: third party
data sources, clou
<meta name="twitter:card" content="summary_large_image"/>
<meta name="twitter:image" content="/images/feature-image.png"/>
-<meta name="twitter:title" content="Onefootball"/>
-<meta name="twitter:description" content="What was the problem? With millions
of daily active users, managing the complexity of data engineering at
Onefootball is a constant challenge. Lengthy crontabs, multiplication of custom
API clients, erosion of confidence in the analytics served, increasing heroism
(“only one person can solve this issue”). Those are the challenges
that most teams face unless they consciously invest in their tools and
processes.
-On top of that, new data tools appear each month: third party data sources,
cloud providers solutions, different storage technologies… Managing all
those integrations is costly and brittle, especially for small data engineering
teams that are trying to do more with less."/>
+<meta name="twitter:title" content="Sift"/>
+<meta name="twitter:description" content="What was the problem? At Sift, we’re
constantly training machine learning models that feed into the core of Sift’s
Digital Trust & Safety platform. The platform gives our customers a way to
discern suspicious online behavior from trustworthy behavior, allowing our
customers to protect their online transactions, maintain the integrity of their
content platforms, and keep their users’ accounts secure. To make this
possible, we’ve built model tr [...]
<script type="application/javascript">
@@ -436,10 +433,10 @@ if (!doNotTrack) {
<div class="quote">
- <p class="quote--text">Airflow is extensible enough for any business to
define the custom operators they need. Airflow can help you in your DataOps
journey: viewing analytics as code, monitoring, reusing components, being a
catalyst of team interactions.</p>
- <p class="quote--author">Louis Guitton</p>
+ <p class="quote--text">Airflow helped us to define and organize our ML
pipeline dependencies, and empowered us to introduce new, diverse batch
processes at increasing scale.</p>
+ <p class="quote--author">Handong Park</p>
- <img src="/usecase-logos/onefootball-logo.svg" alt="Onefootball logo"
class="quote--logo" />
+ <img src="/usecase-logos/sift_logo.png" alt="Sift logo"
class="quote--logo" />
</div>
@@ -449,19 +446,27 @@ if (!doNotTrack) {
<h5 id="what-was-the-problem">What was the problem?</h5>
-<p>With millions of daily active users, managing the complexity of data
engineering at Onefootball is a constant challenge. Lengthy crontabs,
multiplication of custom API clients, erosion of confidence in the analytics
served, increasing heroism (“only one person can solve this
issue”). Those are the challenges that most teams face unless they
consciously invest in their tools and processes.</p>
+<p>At Sift, we’re constantly training machine learning models that feed into
the core of Sift’s Digital Trust & Safety platform. The platform gives our
customers a way to discern suspicious online behavior from trustworthy
behavior, allowing our customers to protect their online transactions, maintain
the integrity of their content platforms, and keep their users’ accounts
secure. To make this possible, we’ve built model training pipelines that
consist of hundreds of steps in MapRedu [...]
-<p>On top of that, new data tools appear each month: third party data sources,
cloud providers solutions, different storage technologies… Managing all
those integrations is costly and brittle, especially for small data engineering
teams that are trying to do more with less.</p>
+<p>When we built these workflows, we found that we needed a centralized way to
organize the interactions between the many steps in each workflow. But before
Airflow, we didn’t have an easy way to express those dependencies. And as we
added steps to the workflows, it became increasingly difficult to coordinate
their dependencies and keep ML experiments in sync.</p>
+
+<p>It soon became clear that we needed a way to orchestrate both the scheduled
execution of our jobs and the dependencies between steps of not only a single
workflow, but of multiple workflows. We needed a way to dynamically create
several experimental ML workflows at once that could each have their own code,
dependencies, and tasks. Additionally, we needed a way to be able to monitor
the status of tasks, and re-run or restart tasks from any given point in a
workflow with ease.</p>
<h5 id="how-did-apache-airflow-help-to-solve-this-problem">How did Apache
Airflow help to solve this problem?</h5>
-<p>Airflow had been on our radar for a while until one day we took the leap.
We used the DAG paradigm to migrate the pipelines running on crontabs. We
benefited from the community Hooks and Operators to remove parts of our code,
or to refactor the API clients specific to our business. We use the alerts,
SLAs and the web UI to regain confidence in our analytics. We use our airflow
internal PRs as catalysts for team discussion and to challenge our technical
designs.</p>
+<p>Airflow makes it easy to clearly define the interactions between various
jobs, expanding the scope of what we can do in our model training pipelines. We
now have the ability to schedule and coordinate all jobs while managing the
dependencies between them using DAGs. Each of our main workflows, including our
model training pipeline and ETL pipelines, has its own DAG code that manages
its tasks’ dependencies and the execution schedule for the pipeline. We even
define dependencies betwe [...]
+
+<p>As part of our custom Airflow setup, we’ve built out a separate Airflow
ecosystem for short-lived experimental DAGs as well, so that we can test
changes to our jobs or run separate model training pipelines in isolation.
Using deployment scripts that edit our DAGs when we upload them to Airflow, the
same code that powers an existing DAG can be deployed in a separate, isolated
environment with experimental edits. This means that each experiment can have
its own isolated code, running in [...]
-<p>We have DAGs orchestrating SQL transformations in our data warehouse, but
also DAGs that are orchestrating functions ran against our Kubernetes cluster
both for training Machine Learning models and sending daily analytics
emails.</p>
+<p>Finally, Airflow has given us the ability to manage our tasks’ successes
and failures through its user interface. Airflow allows us to track our tasks’
failures, duration, history, and logs in one central UI, and that same UI also
allows us to easily retry single tasks, branches of a DAG, or entire DAGs.</p>
<h5 id="what-are-the-results">What are the results?</h5>
-<p>The learning curve was steep but in about 100 days we were able to
efficiently use Airflow to manage the complexity of our data engineering. We
currently have 17 DAGs (adding on average 1 per week), we have 2 contributions
on apache/airflow, we have 7 internal hooks and operators and are planning to
add more as our migration efforts continue.</p>
+<p>Airflow initially gave us a way to solve our existing problems: we used
Airflow to replace rigid crons with well-defined DAG dependencies, to build
isolated ML experiments using short-lived DAGs, and to track our pipelines’
successes and failures.</p>
+
+<p>But even after that, Airflow helped us to grow beyond those initial
challenges, and expanded the scope of what we could feasibly tackle. Airflow
not only made it easier to manage our ever-expanding ML pipelines, but also
allowed us to create entirely new pipelines, ranging from workflows that back
up our production data to complex ETL pipelines that transform data into
experimentation-ready formats.</p>
+
+<p>Airflow also allowed us to support a more diverse toolset. Shell scripts,
Java, Python, Jupyter notebooks, and more - all of these can be managed from an
Airflow DAG, allowing developers to utilize our data to test new ideas,
generate insights, and improve our models with ease.</p>
</div>
</div>
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disabled>Previous</button>
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+ <a href="/use-cases/onefootball/">
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- <a
href=https://github.com/apache/airflow-site/edit/master/landing-pages/site/content/en/use-cases/onefootball.md>
+ <a
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