How to: Intelligence
* [Big Data](https://insights.sei.cmu.edu/sei_blog/2017/05/reference-architectures-for-big-data-systems.html)	
	* [Data Platform at Uber](https://eng.uber.com/uber-big-data-platform/)
	* [Data Platform at BMW](https://www.unibw.de/code/events-u/jt-2018-workshops/ws3_bigdata_vortrag_widmann.pdf)
	* [Data Platform at Netflix](https://www.youtube.com/watch?v=CSDIThSwA7s)
	* [Data Platform at Flipkart](https://blog.flipkart.tech/overview-of-flipkart-data-platform-20c6d3e9a196)
	* [Data Platform at Coupang](https://medium.com/coupang-tech/evolving-the-coupang-data-platform-308e305a9c45)
	* [Data Platform at DoorDash](https://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/)
	* [Data Platform at Khan Academy](http://engineering.khanacademy.org/posts/khanalytics.htm)
	* [Data Infrastructure at Airbnb](https://medium.com/airbnb-engineering/data-infrastructure-at-airbnb-8adfb34f169c)
	* [Data Infrastructure at LinkedIn](https://www.infoq.com/presentations/big-data-infrastructure-linkedin)
	* [Data Infrastructure at GO-JEK](https://blog.gojekengineering.com/data-infrastructure-at-go-jek-cd4dc8cbd929)
	* [Data Ingestion Infrastructure at Pinterest](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754)
	* [Data Analytics Architecture at Pinterest](https://medium.com/@Pinterest_Engineering/behind-the-pins-building-analytics-f7b508cdacab)
	* [Data Orchestration Service at Spotify](https://engineering.atspotify.com/2022/03/why-we-switched-our-data-orchestration-service/)
	* [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/)
	* [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)
	* [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)
	* [Analytics Pipeline at Grammarly](https://tech.grammarly.com/blog/building-a-versatile-analytics-pipeline-on-top-of-apache-spark)
	* [Analytics Pipeline at Teads](https://medium.com/teads-engineering/give-meaning-to-100-billion-analytics-events-a-day-d6ba09aa8f44)
	* [ML Data Pipelines for Real-Time Fraud Prevention at PayPal](https://www.infoq.com/presentations/paypal-ml-fraud-prevention-2018)
	* [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)
	* [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)
	* [Privacy-Preserving Analytics and Reporting at LinkedIn](https://engineering.linkedin.com/blog/2019/04/privacy-preserving-analytics-and-reporting-at-linkedin)
	* [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)
	* [Real-Time Analytics for Mobile App Crashes using Apache Pinot at Uber](https://www.uber.com/en-SG/blog/real-time-analytics-for-mobile-app-crashes/)
	* [HALO: Hardware Analytics and Lifecycle Optimization at Facebook](https://code.fb.com/data-center-engineering/hardware-analytics-and-lifecycle-optimization-halo-at-facebook/)
	* [RBEA: Real-time Analytics Platform at King](https://techblog.king.com/rbea-scalable-real-time-analytics-king/)
	* [AresDB: GPU-Powered Real-time Analytics Engine at Uber](https://eng.uber.com/aresdb/)
	* [AthenaX: Streaming Analytics Platform at Uber](https://eng.uber.com/athenax/)
	* [Jupiter: Config Driven Adtech Batch Ingestion Platform at Uber](https://www.uber.com/en-SG/blog/jupiter-batch-ingestion-platform/)
	* [Delta: Data Synchronization and Enrichment Platform at Netflix](https://medium.com/netflix-techblog/delta-a-data-synchronization-and-enrichment-platform-e82c36a79aee)
	* [Keystone: Real-time Stream Processing Platform at Netflix](https://medium.com/netflix-techblog/keystone-real-time-stream-processing-platform-a3ee651812a)
	* [Databook: Turning Big Data into Knowledge with Metadata at Uber](https://eng.uber.com/databook/)
	* [Amundsen: Data Discovery & Metadata Engine at Lyft](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9)
	* [Maze: Funnel Visualization Platform at Uber](https://eng.uber.com/maze/)
	* [Metacat: Making Big Data Discoverable and Meaningful at Netflix](https://medium.com/netflix-techblog/metacat-making-big-data-discoverable-and-meaningful-at-netflix-56fb36a53520)
	* [SpinalTap: Change Data Capture System at Airbnb](https://medium.com/airbnb-engineering/capturing-data-evolution-in-a-service-oriented-architect