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README.md
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README.md
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@ -678,57 +678,58 @@ An updated and organized reading list for illustrating the patterns of scalable,
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## Intelligence
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* [Big Data](https://insights.sei.cmu.edu/sei_blog/2017/05/reference-architectures-for-big-data-systems.html)
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* [Data Platform at Uber](https://eng.uber.com/uber-big-data-platform/)
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* [Data Platform at BMW](https://www.unibw.de/code/events-u/jt-2018-workshops/ws3_bigdata_vortrag_widmann.pdf)
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* [Data Platform at Netflix](https://www.youtube.com/watch?v=CSDIThSwA7s)
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* [Data Platform at Flipkart](https://blog.flipkart.tech/overview-of-flipkart-data-platform-20c6d3e9a196)
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* [Data Platform at Coupang](https://medium.com/coupang-tech/evolving-the-coupang-data-platform-308e305a9c45)
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* [Data Platform at DoorDash](https://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/)
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* [Data Platform at Khan Academy](http://engineering.khanacademy.org/posts/khanalytics.htm)
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* [Data Infrastructure at Airbnb](https://medium.com/airbnb-engineering/data-infrastructure-at-airbnb-8adfb34f169c)
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* [Data Infrastructure at LinkedIn](https://www.infoq.com/presentations/big-data-infrastructure-linkedin)
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* [Data Infrastructure at GO-JEK](https://blog.gojekengineering.com/data-infrastructure-at-go-jek-cd4dc8cbd929)
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* [Data Ingestion Infrastructure at Pinterest](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754)
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* [Data Analytics Architecture at Pinterest](https://medium.com/@Pinterest_Engineering/behind-the-pins-building-analytics-f7b508cdacab)
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* [Data Orchestration Service at Spotify](https://engineering.atspotify.com/2022/03/why-we-switched-our-data-orchestration-service/)
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* [Big Data Processing (2 parts) at Spotify](https://labs.spotify.com/2017/10/23/big-data-processing-at-spotify-the-road-to-scio-part-2/)
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* [Big Data Processing at Uber](https://cdn.oreillystatic.com/en/assets/1/event/160/Big%20data%20processing%20with%20Hadoop%20and%20Spark%2C%20the%20Uber%20way%20Presentation.pdf)
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* [Analytics Pipeline at Lyft](https://cdn.oreillystatic.com/en/assets/1/event/269/Lyft_s%20analytics%20pipeline_%20From%20Redshift%20to%20Apache%20Hive%20and%20Presto%20Presentation.pdf)
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* [Analytics Pipeline at Grammarly](https://tech.grammarly.com/blog/building-a-versatile-analytics-pipeline-on-top-of-apache-spark)
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* [Analytics Pipeline at Teads](https://medium.com/teads-engineering/give-meaning-to-100-billion-analytics-events-a-day-d6ba09aa8f44)
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* [ML Data Pipelines for Real-Time Fraud Prevention at PayPal](https://www.infoq.com/presentations/paypal-ml-fraud-prevention-2018)
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* [Big Data Analytics and ML Techniques at LinkedIn](https://cdn.oreillystatic.com/en/assets/1/event/269/Big%20data%20analytics%20and%20machine%20learning%20techniques%20to%20drive%20and%20grow%20business%20Presentation%201.pdf)
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* [Self-Serve Reporting Platform on Hadoop at LinkedIn](https://cdn.oreillystatic.com/en/assets/1/event/137/Building%20a%20self-serve%20real-time%20reporting%20platform%20at%20LinkedIn%20Presentation%201.pdf)
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* [Privacy-Preserving Analytics and Reporting at LinkedIn](https://engineering.linkedin.com/blog/2019/04/privacy-preserving-analytics-and-reporting-at-linkedin)
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* [Analytics Platform for Tracking Item Availability at Walmart](https://medium.com/walmartlabs/how-we-build-a-robust-analytics-platform-using-spark-kafka-and-cassandra-lambda-architecture-70c2d1bc8981)
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* [HALO: Hardware Analytics and Lifecycle Optimization at Facebook](https://code.fb.com/data-center-engineering/hardware-analytics-and-lifecycle-optimization-halo-at-facebook/)
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* [RBEA: Real-time Analytics Platform at King](https://techblog.king.com/rbea-scalable-real-time-analytics-king/)
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* [AresDB: GPU-Powered Real-time Analytics Engine at Uber](https://eng.uber.com/aresdb/)
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* [AthenaX: Streaming Analytics Platform at Uber](https://eng.uber.com/athenax/)
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* [Delta: Data Synchronization and Enrichment Platform at Netflix](https://medium.com/netflix-techblog/delta-a-data-synchronization-and-enrichment-platform-e82c36a79aee)
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* [Keystone: Real-time Stream Processing Platform at Netflix](https://medium.com/netflix-techblog/keystone-real-time-stream-processing-platform-a3ee651812a)
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* [Databook: Turning Big Data into Knowledge with Metadata at Uber](https://eng.uber.com/databook/)
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* [Amundsen: Data Discovery & Metadata Engine at Lyft](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9)
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* [Maze: Funnel Visualization Platform at Uber](https://eng.uber.com/maze/)
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* [Metacat: Making Big Data Discoverable and Meaningful at Netflix](https://medium.com/netflix-techblog/metacat-making-big-data-discoverable-and-meaningful-at-netflix-56fb36a53520)
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* [SpinalTap: Change Data Capture System at Airbnb](https://medium.com/airbnb-engineering/capturing-data-evolution-in-a-service-oriented-architecture-72f7c643ee6f)
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* [Accelerator: Fast Data Processing Framework at eBay](https://www.ebayinc.com/stories/blogs/tech/announcing-the-accelerator-processing-1-000-000-000-lines-per-second-on-a-single-computer/)
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* [Omid: Transaction Processing Platform at Yahoo](https://yahooeng.tumblr.com/post/180867271141/a-new-chapter-for-omid)
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* [TensorFlowOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/157196488076/open-sourcing-tensorflowonspark-distributed-deep)
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* [CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/139916828451/caffeonspark-open-sourced-for-distributed-deep)
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* [Spark on Scala: Analytics Reference Architecture at Adobe](https://medium.com/adobetech/spark-on-scala-adobe-analytics-reference-architecture-7457f5614b4c)
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* [Experimentation Platform (2 parts) at Spotify](https://engineering.atspotify.com/2020/11/02/spotifys-new-experimentation-platform-part-2/)
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* [Experimentation Platform at Airbnb](https://medium.com/airbnb-engineering/https-medium-com-jonathan-parks-scaling-erf-23fd17c91166)
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* [Smart Product Platform at Zalando](https://jobs.zalando.com/tech/blog/zalando-smart-product-platform/?gh_src=4n3gxh1)
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* [Log Analysis Platform at LINE](https://www.slideshare.net/wyukawa/strata2017-sg)
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* [Data Visualisation Platform at Myntra](https://medium.com/myntra-engineering/universal-dashboarding-platform-udp-data-visualisation-platform-at-myntra-5f2522fcf72d)
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* [Building and Scaling Data Lineage at Netflix](https://medium.com/netflix-techblog/building-and-scaling-data-lineage-at-netflix-to-improve-data-infrastructure-reliability-and-1a52526a7977)
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* [Building a scalable data management system for computer vision tasks at Pinterest](https://medium.com/@Pinterest_Engineering/building-a-scalable-data-management-system-for-computer-vision-tasks-a6dee8f1c580)
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* [Structured Data at Etsy](https://codeascraft.com/2019/07/31/an-introduction-to-structured-data-at-etsy/)
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* [Scaling a Mature Data Pipeline - Managing Overhead at Airbnb](https://medium.com/airbnb-engineering/scaling-a-mature-data-pipeline-managing-overhead-f34835cbc866)
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* [Spark Partitioning Strategies at Airbnb](https://medium.com/airbnb-engineering/on-spark-hive-and-small-files-an-in-depth-look-at-spark-partitioning-strategies-a9a364f908)
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* [Scaling the Hadoop Distributed File System at LinkedIn](https://engineering.linkedin.com/blog/2021/the-exabyte-club--linkedin-s-journey-of-scaling-the-hadoop-distr)
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* [Scaling Hadoop YARN cluster beyond 10,000 nodes at LinkedIn](https://engineering.linkedin.com/blog/2021/scaling-linkedin-s-hadoop-yarn-cluster-beyond-10-000-nodes)
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* [Data Platform at Uber](https://eng.uber.com/uber-big-data-platform/)
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* [Data Platform at BMW](https://www.unibw.de/code/events-u/jt-2018-workshops/ws3_bigdata_vortrag_widmann.pdf)
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* [Data Platform at Netflix](https://www.youtube.com/watch?v=CSDIThSwA7s)
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* [Data Platform at Flipkart](https://blog.flipkart.tech/overview-of-flipkart-data-platform-20c6d3e9a196)
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* [Data Platform at Coupang](https://medium.com/coupang-tech/evolving-the-coupang-data-platform-308e305a9c45)
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* [Data Platform at DoorDash](https://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/)
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* [Data Platform at Khan Academy](http://engineering.khanacademy.org/posts/khanalytics.htm)
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* [Data Infrastructure at Airbnb](https://medium.com/airbnb-engineering/data-infrastructure-at-airbnb-8adfb34f169c)
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* [Data Infrastructure at LinkedIn](https://www.infoq.com/presentations/big-data-infrastructure-linkedin)
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* [Data Infrastructure at GO-JEK](https://blog.gojekengineering.com/data-infrastructure-at-go-jek-cd4dc8cbd929)
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* [Data Ingestion Infrastructure at Pinterest](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754)
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* [Data Analytics Architecture at Pinterest](https://medium.com/@Pinterest_Engineering/behind-the-pins-building-analytics-f7b508cdacab)
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* [Data Orchestration Service at Spotify](https://engineering.atspotify.com/2022/03/why-we-switched-our-data-orchestration-service/)
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* [Big Data Processing (2 parts) at Spotify](https://labs.spotify.com/2017/10/23/big-data-processing-at-spotify-the-road-to-scio-part-2/)
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* [Big Data Processing at Uber](https://cdn.oreillystatic.com/en/assets/1/event/160/Big%20data%20processing%20with%20Hadoop%20and%20Spark%2C%20the%20Uber%20way%20Presentation.pdf)
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* [Analytics Pipeline at Lyft](https://cdn.oreillystatic.com/en/assets/1/event/269/Lyft_s%20analytics%20pipeline_%20From%20Redshift%20to%20Apache%20Hive%20and%20Presto%20Presentation.pdf)
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* [Analytics Pipeline at Grammarly](https://tech.grammarly.com/blog/building-a-versatile-analytics-pipeline-on-top-of-apache-spark)
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* [Analytics Pipeline at Teads](https://medium.com/teads-engineering/give-meaning-to-100-billion-analytics-events-a-day-d6ba09aa8f44)
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* [ML Data Pipelines for Real-Time Fraud Prevention at PayPal](https://www.infoq.com/presentations/paypal-ml-fraud-prevention-2018)
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* [Big Data Analytics and ML Techniques at LinkedIn](https://cdn.oreillystatic.com/en/assets/1/event/269/Big%20data%20analytics%20and%20machine%20learning%20techniques%20to%20drive%20and%20grow%20business%20Presentation%201.pdf)
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* [Self-Serve Reporting Platform on Hadoop at LinkedIn](https://cdn.oreillystatic.com/en/assets/1/event/137/Building%20a%20self-serve%20real-time%20reporting%20platform%20at%20LinkedIn%20Presentation%201.pdf)
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* [Privacy-Preserving Analytics and Reporting at LinkedIn](https://engineering.linkedin.com/blog/2019/04/privacy-preserving-analytics-and-reporting-at-linkedin)
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* [Analytics Platform for Tracking Item Availability at Walmart](https://medium.com/walmartlabs/how-we-build-a-robust-analytics-platform-using-spark-kafka-and-cassandra-lambda-architecture-70c2d1bc8981)
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* [HALO: Hardware Analytics and Lifecycle Optimization at Facebook](https://code.fb.com/data-center-engineering/hardware-analytics-and-lifecycle-optimization-halo-at-facebook/)
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* [RBEA: Real-time Analytics Platform at King](https://techblog.king.com/rbea-scalable-real-time-analytics-king/)
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* [AresDB: GPU-Powered Real-time Analytics Engine at Uber](https://eng.uber.com/aresdb/)
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* [AthenaX: Streaming Analytics Platform at Uber](https://eng.uber.com/athenax/)
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* [Delta: Data Synchronization and Enrichment Platform at Netflix](https://medium.com/netflix-techblog/delta-a-data-synchronization-and-enrichment-platform-e82c36a79aee)
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* [Keystone: Real-time Stream Processing Platform at Netflix](https://medium.com/netflix-techblog/keystone-real-time-stream-processing-platform-a3ee651812a)
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* [Databook: Turning Big Data into Knowledge with Metadata at Uber](https://eng.uber.com/databook/)
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* [Amundsen: Data Discovery & Metadata Engine at Lyft](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9)
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* [Maze: Funnel Visualization Platform at Uber](https://eng.uber.com/maze/)
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* [Metacat: Making Big Data Discoverable and Meaningful at Netflix](https://medium.com/netflix-techblog/metacat-making-big-data-discoverable-and-meaningful-at-netflix-56fb36a53520)
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* [SpinalTap: Change Data Capture System at Airbnb](https://medium.com/airbnb-engineering/capturing-data-evolution-in-a-service-oriented-architecture-72f7c643ee6f)
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* [Accelerator: Fast Data Processing Framework at eBay](https://www.ebayinc.com/stories/blogs/tech/announcing-the-accelerator-processing-1-000-000-000-lines-per-second-on-a-single-computer/)
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* [Omid: Transaction Processing Platform at Yahoo](https://yahooeng.tumblr.com/post/180867271141/a-new-chapter-for-omid)
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* [TensorFlowOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/157196488076/open-sourcing-tensorflowonspark-distributed-deep)
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* [CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/139916828451/caffeonspark-open-sourced-for-distributed-deep)
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* [Spark on Scala: Analytics Reference Architecture at Adobe](https://medium.com/adobetech/spark-on-scala-adobe-analytics-reference-architecture-7457f5614b4c)
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* [Experimentation Platform (2 parts) at Spotify](https://engineering.atspotify.com/2020/11/02/spotifys-new-experimentation-platform-part-2/)
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* [Experimentation Platform at Airbnb](https://medium.com/airbnb-engineering/https-medium-com-jonathan-parks-scaling-erf-23fd17c91166)
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* [Smart Product Platform at Zalando](https://jobs.zalando.com/tech/blog/zalando-smart-product-platform/?gh_src=4n3gxh1)
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* [Log Analysis Platform at LINE](https://www.slideshare.net/wyukawa/strata2017-sg)
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* [Data Visualisation Platform at Myntra](https://medium.com/myntra-engineering/universal-dashboarding-platform-udp-data-visualisation-platform-at-myntra-5f2522fcf72d)
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* [Building and Scaling Data Lineage at Netflix](https://medium.com/netflix-techblog/building-and-scaling-data-lineage-at-netflix-to-improve-data-infrastructure-reliability-and-1a52526a7977)
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* [Building a scalable data management system for computer vision tasks at Pinterest](https://medium.com/@Pinterest_Engineering/building-a-scalable-data-management-system-for-computer-vision-tasks-a6dee8f1c580)
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* [Structured Data at Etsy](https://codeascraft.com/2019/07/31/an-introduction-to-structured-data-at-etsy/)
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* [Scaling a Mature Data Pipeline - Managing Overhead at Airbnb](https://medium.com/airbnb-engineering/scaling-a-mature-data-pipeline-managing-overhead-f34835cbc866)
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* [Spark Partitioning Strategies at Airbnb](https://medium.com/airbnb-engineering/on-spark-hive-and-small-files-an-in-depth-look-at-spark-partitioning-strategies-a9a364f908)
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* [Scaling the Hadoop Distributed File System at LinkedIn](https://engineering.linkedin.com/blog/2021/the-exabyte-club--linkedin-s-journey-of-scaling-the-hadoop-distr)
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* [Scaling Hadoop YARN cluster beyond 10,000 nodes at LinkedIn](https://engineering.linkedin.com/blog/2021/scaling-linkedin-s-hadoop-yarn-cluster-beyond-10-000-nodes)
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* [Data platform at Hotstar](https://blog.hotstar.com/ingesting-data-at-bharat-scale-36615cbb4294)
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* [Distributed Machine Learning](https://www.csie.ntu.edu.tw/~cjlin/talks/bigdata-bilbao.pdf)
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* [Machine Learning Platform at Uber](https://eng.uber.com/michelangelo/)
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* [Machine Learning Platform at Yelp](https://engineeringblog.yelp.com/2020/07/ML-platform-overview.html)
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