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Analytics Pipeline at Teads

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@ -580,7 +580,7 @@ An organized reading list for illustrating the patterns behind scalable, reliabl
* [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.tv](http://highscalability.com/blog/2018/4/9/give-meaning-to-100-billion-events-a-day-the-analytics-pipel.html)
* [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)
@ -605,7 +605,6 @@ An organized reading list for illustrating the patterns behind scalable, reliabl
* [Log Analysis Platform at LINE](https://www.slideshare.net/wyukawa/strata2017-sg)
* [Data Visualisation Platform at Myntra](https://medium.com/myntra-engineering/universal-dashboarding-platform-udp-data-visualisation-platform-at-myntra-5f2522fcf72d)
* [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)
* [Give meaning to 100 billion analytics events a day](https://medium.com/teads-engineering/give-meaning-to-100-billion-analytics-events-a-day-d6ba09aa8f44)
* [Distributed Machine Learning](https://www.csie.ntu.edu.tw/~cjlin/talks/bigdata-bilbao.pdf)
* [Michelangelo: Machine Learning Platform at Uber](https://eng.uber.com/michelangelo/)
* [Scaling Michelangelo](https://eng.uber.com/scaling-michelangelo/)