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new 78bd4832313 Publishing website 2025/12/31 13:31:28 at commit 1cadc5f
78bd4832313 is described below
commit 78bd483231364a98e94398ec298d006158db96b3
Author: runner <runner@main-runner-vplcm-t7rhj>
AuthorDate: Wed Dec 31 13:31:29 2025 +0000
Publishing website 2025/12/31 13:31:28 at commit 1cadc5f
---
.../case-studies/albertsons/index.html | 42 ++++
website/generated-content/case-studies/index.html | 3 +-
website/generated-content/case-studies/index.xml | 228 +++++++++++++++++++--
website/generated-content/sitemap.xml | 2 +-
4 files changed, 261 insertions(+), 14 deletions(-)
diff --git a/website/generated-content/case-studies/albertsons/index.html
b/website/generated-content/case-studies/albertsons/index.html
new file mode 100644
index 00000000000..5d8f4bdb509
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language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise [...]
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-startups.</p><div class=case-study-list><div class=case-study-card><div
class=case-study-card-img><img src=/images/logos/powered-by/akvelon.png
loading=lazy></i></div><h3 class=case-study-card-title>Secure and Interoperable
Apache Beam Pipelines by Akvelon</h3><p class=case-study-card-description>To
support data privacy and pipeline reusability at scale, Akvelon developed
Beam-based solutions for Protegrity and a major North American credit reporting
company, enabling tokenization with D [...]
+startups.</p><div class=case-study-list><div class=case-study-card><div
class=case-study-card-img><img src=/images/logos/powered-by/albertsons.jpg
loading=lazy></i></div><h3 class=case-study-card-title>Albertsons: Using Apache
Beam for Unified Analytics Ingestion</h3><p
class=case-study-card-description>Apache Beam enabled Albertsons to standardize
ingestion into a resilient and portable framework, delivering 99.9% reliability
at enterprise scale across both real-time signals and core bu [...]
+<img src=/images/arrow-right.svg alt="Go to the case study"></a></div><div
class=case-study-card><div class=case-study-card-img><img
src=/images/logos/powered-by/akvelon.png loading=lazy></i></div><h3
class=case-study-card-title>Secure and Interoperable Apache Beam Pipelines by
Akvelon</h3><p class=case-study-card-description>To support data privacy and
pipeline reusability at scale, Akvelon developed Beam-based solutions for
Protegrity and a major North American credit reporting company [...]
<img src=/images/arrow-right.svg alt="Go to the case study"></a></div><div
class=case-study-card><div class=case-study-card-img><img
src=/images/logos/powered-by/credit-karma.png loading=lazy></i></div><h3
class=case-study-card-title>Credit Karma: Leveraging Apache Beam for Enhanced
Financial Insights</h3><p class=case-study-card-description>With Apache Beam
and Dataflow, Credit Karma achieved a 99% uptime for critical data pipelines, a
significant jump from 80%. This reliability, couple [...]
<img src=/images/arrow-right.svg alt="Go to the case study"></a></div><div
class=case-study-card><div class=case-study-card-img><img
src=/images/logos/powered-by/accenture.png loading=lazy></i></div><h3
class=case-study-card-title>Accenture Baltics' Journey with Apache Beam</h3><p
class=case-study-card-description>Accenture Baltics uses Apache Beam on Google
Cloud to build a robust data processing infrastructure for a sustainable energy
leader.They use Beam to democratize data access, pr [...]
<img src=/images/arrow-right.svg alt="Go to the case study"></a></div><div
class=case-study-card><div class=case-study-card-img><img
src=/images/logos/powered-by/yelp.png loading=lazy></i></div><h3
class=case-study-card-title>Building data abstractions with streaming at
Yelp</h3><p class=case-study-card-description>At Yelp, Apache Beam allows teams
to create custom streaming pipelines using Python, eliminating the need to
switch to Scala or Java. This reduces the learning curve for Pytho [...]
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b/website/generated-content/case-studies/index.xml
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Beam for Unified Analytics
Ingestion</title><link>/case-studies/albertsons/</link><pubD [...]
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+<!-- div with class case-study-opinion is displayed at the top left area of
the case study page -->
+<div class="case-study-opinion">
+<div class="case-study-opinion-img">
+<img src="/images/logos/powered-by/albertsons.jpg"/>
+</div>
+<blockquote class="case-study-quote-block">
+<p class="case-study-quote-text">
+“Apache Beam enabled Albertsons to standardize ingestion into a resilient and
portable framework, delivering 99.9% reliability at enterprise scale across
both real-time signals and core business data.”
+</p>
+<div class="case-study-quote-author">
+<div class="case-study-quote-author-img">
+<img src="/images/case-study/albertsons/utkarshparekh.png">
+</div>
+<div class="case-study-quote-author-info">
+<div class="case-study-quote-author-name">
+Utkarsh Parekh
+</div>
+<div class="case-study-quote-author-position">
+Staff Engineer, Data @ Albertsons
+</div>
+</div>
+</div>
+</blockquote>
+</div>
+<!-- div with class case-study-post is the case study page main content -->
+<div class="case-study-post">
+<h1
id="albertsons-using-apache-beam-for-unified-analytics-ingestion">Albertsons:
Using Apache Beam for Unified Analytics Ingestion</h1>
+<h2 id="context">Context</h2>
+<p>Albertsons Companies is one of the largest retail grocery organizations
in North America, operating over 2,200 stores and serving millions of customers
across physical and digital channels.</p>
+<p>Apache Beam is the foundation of the <strong>internal Unified Data
Ingestion framework</strong>, a standardized enterprise ELT platform that
delivers both streaming and batch data into modern cloud analytics systems. The
framework uses <strong>both Java and Python Beam SDKs, Dataflow Flex
Templates, enabling flexibility across workloads. When a capability is not yet
supported in the Python SDK but is available in the Java SDK, we can seamlessly
leverage Java-based implemen [...]
+<p>This unified architecture reduces duplicated logic, standardizes
governance, and accelerates data enablement across business domains.</p>
+<h2 id="challenges-and-use-cases">Challenges and Use Cases</h2>
+<p>Before Apache Beam, ingestion patterns were fragmented across streaming
and batch pipelines. This led to longer development cycles, inconsistent data
quality, and increased operational overhead.</p>
+<p>The framework’s architecture emphasizes object-oriented principles
including single responsibility, modularity, and separation of concerns. This
enables reusable Beam transforms, configurable IO connectors, and clean
abstractions between orchestration and execution layers.</p>
+<p>Beam enabled:</p>
+<ul>
+<li>Unified development for real-time and scheduled ingestion</li>
+<li>Standardized connectivity to enterprise systems</li>
+<li>Reliable governance and observability baked into pipelines</li>
+</ul>
+<p>The framework supports:</p>
+<ul>
+<li><strong>Real-time streaming analytics</strong> from operational
and digital signals</li>
+<li><strong>Batch ingestion</strong> from mission-critical enterprise
systems</li>
+<li><strong>File-based ingestion</strong> for vendor and financial
datasets</li>
+<li><strong>Legacy MQ ingestion</strong> using JMSIO-based
connectors</li>
+</ul>
+<p>To scale efficiently, the framework features <strong>Apache Airflow
dynamic DAG creation.</strong></p>
+<p>Metadata-driven ingestion jobs generate DAGs automatically at runtime,
and <strong>BashOperator</strong> is used to submit
<strong>Dataflow</strong> jobs for consistent execution, security, and
monitoring.</p>
+<p>Common Beam transforms include Impulse, windowing, grouping, and
batching optimizations.</p>
+<blockquote class="case-study-quote-block case-study-quote-wrapped">
+<p class="case-study-quote-text">
+In Albertsons we utilized Apache Beam to write an in-house framework that
enabled our data engineering teams to create robust data pipelines through a
consistent - single interface. The framework helped reduce the overall
development cycle since we templatized the various data integration patterns.
Having a custom framework gave us flexibility to prioritize and configure
multiple technologies/integration points like Kafka, Files, Managed Queues,
Databases (Oracle, DB2, Azure SQL etc.) an [...]
+</p>
+<div class="case-study-quote-author">
+<div class="case-study-quote-author-img">
+<img src="/images/case-study/albertsons/mohammedjawedkhan.jpeg">
+</div>
+<div class="case-study-quote-author-info">
+<div class="case-study-quote-author-name">
+Mohammed Jawed Khan
+</div>
+<div class="case-study-quote-author-position">
+Principal Data Engineer @ Albertsons
+</div>
+</div>
+</div>
+</blockquote>
+<h2 id="technical-data">Technical Data</h2>
+<p>Apache Beam pipelines operate at enterprise scale:</p>
+<ul>
+<li>Hundreds of production pipelines</li>
+<li>Terabytes of data processed weekly, including thousands of streaming
events per second.</li>
+</ul>
+<p>All ingestion paths adhere to internal security controls and support
<strong>tokenization</strong> for PII and sensitive data protection using
Protegrity.</p>
+<h2 id="results">Results</h2>
+<p>Apache Beam has significantly improved the reliability, reusability, and
speed of Albertsons’ data platforms:</p>
+<div class="table-wrapper"><table>
+<thead>
+<tr>
+<th>Area</th>
+<th>Outcome</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Reliability</td>
+<td><strong>99.9%+ uptime</strong> for data ingestion</td>
+</tr>
+<tr>
+<td>Developer Productivity</td>
+<td>Pipelines created faster via standardized templates</td>
+</tr>
+<tr>
+<td>Operational Efficiency</td>
+<td><strong>Autoscaling</strong> optimizes resource
utilization</td>
+</tr>
+<tr>
+<td>Business Enablement</td>
+<td>Enables <strong>real-time decisioning</strong></td>
+</tr>
+</tbody>
+</table>
+</div>
+<h3 id="business-impact">Business Impact</h3>
+<p>Beam enabled one unified ingestion framework that supports both
streaming and batch workloads - eliminating fragmentation and delivering
trusted signals to analytics.</p>
+<blockquote class="case-study-quote-block case-study-quote-wrapped">
+<p class="case-study-quote-text">
+Integrating Apache Beam into our in-house ELT platform has reduced engineering
effort and operational overhead, while improving efficiency at scale. Teams can
now focus more on delivering business outcomes instead of managing
infrastructure.
+</p>
+<div class="case-study-quote-author">
+<div class="case-study-quote-author-img">
+<img src="/images/case-study/albertsons/vinaydesai.jpeg">
+</div>
+<div class="case-study-quote-author-info">
+<div class="case-study-quote-author-name">
+Vinay Desai
+</div>
+<div class="case-study-quote-author-position">
+Director Engineering @ Albertsons
+</div>
+</div>
+</div>
+</blockquote>
+<blockquote class="case-study-quote-block case-study-quote-wrapped">
+<p class="case-study-quote-text">
+By leveraging Apache Beam into the ACI platform, we achieved a significant
reduction in downtime. The adoption of reusable features further minimized the
risk of production issues.
+</p>
+<div class="case-study-quote-author">
+<div class="case-study-quote-author-img">
+<img src="/images/case-study/albertsons/ankurraj.jpeg">
+</div>
+<div class="case-study-quote-author-info">
+<div class="case-study-quote-author-name">
+Ankur Raj
+</div>
+<div class="case-study-quote-author-position">
+Director , Data Engineering Operations @ Albertsons
+</div>
+</div>
+</div>
+</blockquote>
+<h2 id="infrastructure">Infrastructure</h2>
+<div class="table-wrapper"><table>
+<thead>
+<tr>
+<th>Component</th>
+<th>Detail</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Cloud</td>
+<td>Google Cloud Platform</td>
+</tr>
+<tr>
+<td>Runner</td>
+<td>DataflowRunner</td>
+</tr>
+<tr>
+<td>Beam SDKs</td>
+<td>Java &amp; Python</td>
+</tr>
+<tr>
+<td>Workflow Orchestration</td>
+<td>Apache Airflow with dynamic DAG creation</td>
+</tr>
+<tr>
+<td>Deployment</td>
+<td>BashOperator submits Dataflow jobs</td>
+</tr>
+<tr>
+<td>Sources</td>
+<td>Kafka, JDBC systems, files, MQ, APIs</td>
+</tr>
+<tr>
+<td>Targets</td>
+<td>BigQuery, GCS, Kafka</td>
+</tr>
+<tr>
+<td>Observability</td>
+<td>Centralized logging, alerting, retry patterns</td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Deployment is portable across Dev, QA, and Prod environments.</p>
+<h2 id="beam-community--evolution">Beam Community &amp;
Evolution</h2>
+<p>Beam community resources supported the framework’s growth through:</p>
+<ul>
+<li>Slack &amp; developer channels</li>
+<li>Documentation</li>
+<li>Beam Summit participation</li>
+</ul>
+<!-- case_study_feedback adds feedback buttons -->
+<div class="case-study-feedback" id="case-study-feedback">
+<p class="case-study-feedback-title">Was this information useful?</p>
+<div>
+<button class="btn case-study-feedback-btn"
onclick="sendCaseStudyFeedback(true, 'AlbertsonsCompanies')">Yes</button>
+<button class="btn case-study-feedback-btn"
onclick="sendCaseStudyFeedback(false, 'AlbertsonsCompanies')">No</button>
+</div>
+</div>
+</div>
+<div
class="clear-nav"></div></description></item><item><title>Case-Studies:
Credit Karma: Leveraging Apache Beam for Enhanced Financial
Insights</title><link>/case-studies/creditkarmainsights/</link><pubDate>Sun, 25
May 2025 00:12:00
+0000</pubDate><guid>/case-studies/creditkarmainsights/</guid><description>
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-<!--
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-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--></description></item></channel></rss>
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