diff --git a/README.md b/README.md
index 5151b0e..38c6208 100644
--- a/README.md
+++ b/README.md
@@ -248,21 +248,20 @@ An updated and curated list of readings to illustrate best practices and pattern
 		* [Pub-Sub in Chatting Architecture at LINE](https://engineering.linecorp.com/en/blog/detail/85)
 	* [Kafka the Message Broker](https://martin.kleppmann.com/papers/kafka-debull15.pdf)
 		* [When to use RabbitMQ or Kafka](https://content.pivotal.io/blog/understanding-when-to-use-rabbitmq-or-apache-kafka)		
-		* [Kafka at Scale at LinkedIn](https://engineering.linkedin.com/kafka/running-kafka-scale)
-		* [Real-time Data Pipeline with Kafka at Yelp](https://engineeringblog.yelp.com/2016/07/billions-of-messages-a-day-yelps-real-time-data-pipeline.html)
-		* [Building Reliable Reprocessing and Dead Letter Queues with Kafka at Uber](https://eng.uber.com/reliable-reprocessing/)
-		* [Audit Kafka End-to-End at Uber](https://eng.uber.com/chaperone/)
-		* [Kafka for PaaS at Rakuten](https://techblog.rakuten.co.jp/2016/01/28/rakuten-paas-kafka/)
-		* [Publishing with Kafka at The New York Times](https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077)
-		* [Kafka Streams on Heroku](https://blog.heroku.com/kafka-streams-on-heroku)
-		* [Kafka in Platform Events Architecture at Salesforce](https://engineering.salesforce.com/how-apache-kafka-inspired-our-platform-events-architecture-2f351fe4cf63)	
-		* [Kafka in Socket Architecture (with a Comprehensive Comparison Table) at Trello](https://tech.trello.com/why-we-chose-kafka/)	
-		* [Analytics Pipeline (Kafka, Dataflow, BigQuery) at Teads.tv](http://highscalability.com/blog/2018/4/9/give-meaning-to-100-billion-events-a-day-the-analytics-pipel.html)
+		* [Kafka at LinkedIn](https://engineering.linkedin.com/kafka/running-kafka-scale)
+		* [Kafka at Pinterest](https://medium.com/pinterest-engineering/how-pinterest-runs-kafka-at-scale-ff9c6f735be)
+		* [Kafka at Yelp](https://engineeringblog.yelp.com/2016/07/billions-of-messages-a-day-yelps-real-time-data-pipeline.html)
+		* [Kafka at Trello](https://tech.trello.com/why-we-chose-kafka/)	
+		* [Kafka at Salesforce](https://engineering.salesforce.com/how-apache-kafka-inspired-our-platform-events-architecture-2f351fe4cf63)
+		* [Kafka at Rakuten](https://techblog.rakuten.co.jp/2016/01/28/rakuten-paas-kafka/)
+		* [Kafka at The New York Times](https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077)
+		* [Reprocessing and Dead Letter Queues with Kafka at Uber](https://eng.uber.com/reliable-reprocessing/)
+		* [Chaperone: Audit Kafka End-to-End at Uber](https://eng.uber.com/chaperone/)
 	* [Stream Data Deduplication](https://en.wikipedia.org/wiki/Data_deduplication)
-		* [Exactly-once Semantics are Possible: How Kafka Does it](https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/)
-		* [Real-time Deduping at Scale with Kafka-based Pipleline at Tapjoy](http://eng.tapjoy.com/blog-list/real-time-deduping-at-scale)
-		* [Delivering Billions of Messages Exactly Once: Deduping at Segment](https://segment.com/blog/exactly-once-delivery/)
-		* [Deduplication For Efficient Storage (From 50 PB To 32 PB) At Mail.Ru](https://medium.com/@andrewsumin/efficient-storage-how-we-went-down-from-50-pb-to-32-pb-99f9c61bf6b4)				
+		* [Exactly-once Semantics with Kafka](https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/)
+		* [Real-time Deduping at Tapjoy](http://eng.tapjoy.com/blog-list/real-time-deduping-at-scale)
+		* [Deduplication at Segment](https://segment.com/blog/exactly-once-delivery/)
+		* [Deduplication at Mail.Ru](https://medium.com/@andrewsumin/efficient-storage-how-we-went-down-from-50-pb-to-32-pb-99f9c61bf6b4)				
 * [Distributed Searching](http://nwds.cs.washington.edu/files/nwds/pdf/Distributed-WR.pdf)
 	* [Search Architecture of Instagram](https://engineering.instagram.com/search-architecture-eeb34a936d3a)
 	* [Search Architecture of eBay](http://www.cs.otago.ac.nz/homepages/andrew/papers/2017-8.pdf)
@@ -580,6 +579,7 @@ An updated and curated list of readings to illustrate best practices and pattern
 	* [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)
 	* [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)