An updated and organized reading list for illustrating the patterns of scalable, reliable, and performant large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to billions of users.
> Understand your problems: scalability problem (fast for a single user but slow under heavy load) or performance problem (slow for a single user) by reviewing some [design principles](#principle) and checking how [scalability](#scalability) and [performance](#performance) problems are solved at tech companies. The section of [intelligence](#intelligence) are created for those who work with data and machine learning at big (data) and deep (learning) scale.
> "Even if you lose all one day, you can build all over again if you retain your calm!" - Thuan Pham, former CTO of Uber. So, keep calm and mind the [availability](#availability) and [stability](#stability) matters!
> Look at some [interview notes](#interview) and [real-world architectures with completed diagrams](#architecture) to get a comprehensive view before designing your system on whiteboard. You can check some [talks](#talk) of engineers from tech giants to know how they build, scale, and optimize their systems. Good luck!
> The goal of scaling team is not growing team size but increasing team output and value. You can find out how tech companies reach that goal in various aspects: hiring, management, organization, culture, and communication in the [organization](#organization) section.
> Contributions are greatly welcome! You may want to take a look at the [contribution guidelines](CONTRIBUTING.md). If you see a link here that is no longer maintained or is not a good fit, please submit a pull request!
> Many long hours of hard work have gone into this project. If you find it helpful, please share on Facebook, [on Twitter](https://ctt.ec/V8B2p), [on Weibo](http://t.cn/RnjFLCB), or on your chat groups! Knowledge is power, knowledge shared is power multiplied. Thank you!
* [Designs, Lessons and Advice from Building Large Distributed Systems - Jeff Dean, Google](https://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf)
* [Database Isolation Levels and Effects on Performance and Scalability](http://highscalability.com/blog/2011/2/10/database-isolation-levels-and-their-effects-on-performance-a.html)
* [Data Access for Highly-Scalable Solutions: Using SQL, NoSQL, and Polyglot Persistence](https://docs.microsoft.com/en-us/previous-versions/msp-n-p/dn271399(v=pandp.10))
* [The Benefits of Server Side Rendering over Client Side Rendering](https://medium.com/walmartlabs/the-benefits-of-server-side-rendering-over-client-side-rendering-5d07ff2cefe8)
* [PowerfulSeal: Testing Tool for Kubernetes Clusters at Bloomberg](https://www.techatbloomberg.com/blog/powerfulseal-testing-tool-kubernetes-clusters/)
* [Docker Containers that Power Over 100.000 Online Shops at Shopify](https://shopifyengineering.myshopify.com/blogs/engineering/docker-at-shopify-how-we-built-containers-that-power-over-100-000-online-shops)
* [GRIT: Protocol for Distributed Transactions across Microservices at eBay](https://tech.ebayinc.com/engineering/grit-a-protocol-for-distributed-transactions-across-microservices/)
* [CRISP: Critical Path Analysis for Microservice Architectures at Uber](https://eng.uber.com/crisp-critical-path-analysis-for-microservice-architectures/)
* [Application Data Caching from RAM to SSD at NetFlix](https://medium.com/netflix-techblog/evolution-of-application-data-caching-from-ram-to-ssd-a33d6fa7a690)
* [Scaling Redis at Twitter](http://highscalability.com/blog/2014/9/8/how-twitter-uses-redis-to-scale-105tb-ram-39mm-qps-10000-ins.html)
* [Scaling Job Queue with Redis at Slack](https://slack.engineering/scaling-slacks-job-queue-687222e9d100)
* [Moving persistent data out of Redis at Github](https://githubengineering.com/moving-persistent-data-out-of-redis/)
* [Storing Hundreds of Millions of Simple Key-Value Pairs in Redis at Instagram](https://engineering.instagram.com/storing-hundreds-of-millions-of-simple-key-value-pairs-in-redis-1091ae80f74c)
* [Redis at Trivago](http://tech.trivago.com/2017/01/25/learn-redis-the-hard-way-in-production/)
* [Improving Distributed Caching Performance and Efficiency at Pinterest](https://medium.com/pinterest-engineering/improving-distributed-caching-performance-and-efficiency-at-pinterest-92484b5fe39b)
* [Standardize and Improve Microservices Caching at DoorDash](https://doordash.engineering/2023/10/19/how-doordash-standardized-and-improved-microservices-caching/)
* [HAProxy with Kubernetes for User-facing Traffic at SoundCloud](https://developers.soundcloud.com/blog/how-soundcloud-uses-haproxy-with-kubernetes-for-user-facing-traffic)
* [Bandaid: Service Proxy at Dropbox](https://blogs.dropbox.com/tech/2018/03/meet-bandaid-the-dropbox-service-proxy/)
* [Chubby: Lock Service for Loosely Coupled Distributed Systems at Google](https://blog.acolyer.org/2015/02/13/the-chubby-lock-service-for-loosely-coupled-distributed-systems/)
* [Distributed Locking at Uber](https://www.youtube.com/watch?v=MDuagr729aU)
* [Distributed Locks using Redis at GoSquared](https://engineering.gosquared.com/distributed-locks-using-redis)
* [ZooKeeper at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/zookeeper-at-twitter.html)
* [Eliminating Duplicate Queries using Distributed Locking at Chartio](https://blog.chartio.com/posts/eliminating-duplicate-queries-using-distributed-locking)
* [Improve Zipkin Traces using Kubernetes Pod Metadata at SoundCloud](https://developers.soundcloud.com/blog/using-kubernetes-pod-metadata-to-improve-zipkin-traces)
* [Building Cron at Google](https://landing.google.com/sre/sre-book/chapters/distributed-periodic-scheduling/)
* [Distributed Cron Architecture at Quora](https://engineering.quora.com/Quoras-Distributed-Cron-Architecture)
* [Chronos: A Replacement for Cron at Airbnb](https://medium.com/airbnb-engineering/chronos-a-replacement-for-cron-f05d7d986a9d)
* [Scheduler at Nextdoor](https://engblog.nextdoor.com/we-don-t-run-cron-jobs-at-nextdoor-6f7f9cc62040)
* [Peloton: Unified Resource Scheduler for Diverse Cluster Workloads at Uber](https://eng.uber.com/peloton/)
* [Fenzo: OSS Scheduler for Apache Mesos Frameworks at Netflix](https://medium.com/netflix-techblog/fenzo-oss-scheduler-for-apache-mesos-frameworks-5c340e77e543)
* [Unicorn: Remediation System at eBay](https://www.ebayinc.com/stories/blogs/tech/unicorn-rheos-remediation-center/)
* [M3: Metrics and Monitoring Platform at Uber](https://eng.uber.com/optimizing-m3/)
* [Athena: Automated Build Health Management System at Dropbox](https://blogs.dropbox.com/tech/2019/05/athena-our-automated-build-health-management-system/)
* [Vortex: Monitoring Server Applications at Dropbox](https://blogs.dropbox.com/tech/2019/11/monitoring-server-applications-with-vortex/)
* [Nuage: Cloud Management Service at LinkedIn](https://engineering.linkedin.com/blog/2019/solving-manageability-challenges-with-nuage)
* [Monitoring and Alert System using Graphite and Cabot at HackerEarth](http://engineering.hackerearth.com/2017/03/21/monitoring-and-alert-system-using-graphite-and-cabot/)
* [Data Pipeline Monitoring System at LinkedIn](https://engineering.linkedin.com/blog/2019/an-inside-look-at-linkedins-data-pipeline-monitoring-system-)
* [Aardvark and Repokid: AWS Least Privilege for Distributed, High-Velocity Development at Netflix](https://medium.com/netflix-techblog/introducing-aardvark-and-repokid-53b081bf3a7e)
* [LISA: Distributed Firewall at LinkedIn](https://www.slideshare.net/MikeSvoboda/2017-lisa-linkedins-distributed-firewall-dfw)
* [Secure Infrastructure To Store Bitcoin In The Cloud at Coinbase](https://engineering.coinbase.com/how-coinbase-builds-secure-infrastructure-to-store-bitcoin-in-the-cloud-30a6504e40ba)
* [Athenz: Fine-Grained, Role-Based Access Control at Yahoo](https://yahooeng.tumblr.com/post/160481899076/open-sourcing-athenz-fine-grained-role-based)
* [Diffy: Differencing Engine for Digital Forensics in the Cloud at Netflix](https://medium.com/netflix-techblog/netflix-sirt-releases-diffy-a-differencing-engine-for-digital-forensics-in-the-cloud-37b71abd2698)
* [Detecting Credential Compromise in AWS at Netflix](https://medium.com/netflix-techblog/netflix-cloud-security-detecting-credential-compromise-in-aws-9493d6fd373a)
* [Edge Authentication and Token-Agnostic Identity Propagation at Netflix](https://netflixtechblog.com/edge-authentication-and-token-agnostic-identity-propagation-514e47e0b602)
* [Hardening Kubernetes Infrastructure with Cilium at Palantir](https://blog.palantir.com/hardening-palantirs-kubernetes-infrastructure-with-cilium-1c40d4c7ef0)
* [Improving Web Vulnerability Management through Automation at Lyft](https://eng.lyft.com/improving-web-vulnerability-management-through-automation-2631570d8415)
* [Samza: Stream Processing System for Latency Insighs at LinkedIn](https://engineering.linkedin.com/blog/2018/04/samza-aeon--latency-insights-for-asynchronous-one-way-flows)
* [Bullet: Forward-Looking Query Engine for Streaming Data at Yahoo](https://yahooeng.tumblr.com/post/161855616651/open-sourcing-bullet-yahoos-forward-looking)
* [Dynein: Distributed Delayed Job Queueing System at Airbnb](https://medium.com/airbnb-engineering/dynein-building-a-distributed-delayed-job-queueing-system-93ab10f05f99)
* [Timestone: Queueing System for Non-Parallelizable Workloads at Netflix](https://netflixtechblog.com/timestone-netflixs-high-throughput-low-latency-priority-queueing-system-with-built-in-support-1abf249ba95f)
* [Cross-platform In-app Messaging Orchestration Service at Netflix](https://medium.com/netflix-techblog/building-a-cross-platform-in-app-messaging-orchestration-service-86ba614f92d8)
* [Improving Stream Data Quality With Protobuf Schema Validation at Deliveroo](https://deliveroo.engineering/2019/02/05/improving-stream-data-quality-with-protobuf-schema-validation.html)
* [Scalable content feed using Event Sourcing and CQRS patterns at Brainly](https://medium.com/engineering-brainly/scalable-content-feed-using-event-sourcing-and-cqrs-patterns-e09df98bf977)
* [MemQ: Cloud Native Pub-Sub System at Pinterest](https://medium.com/pinterest-engineering/memq-an-efficient-scalable-cloud-native-pubsub-system-4402695dd4e7)
* [Finding Kafka throughput limit in infrastructure at Dropbox](https://blogs.dropbox.com/tech/2019/01/finding-kafkas-throughput-limit-in-dropbox-infrastructure/)
* [InfluxDB and Kafka to Scale to Over 1 Million Metrics a Second at Hulu](https://medium.com/hulu-tech-blog/how-hulu-uses-influxdb-and-kafka-to-scale-to-over-1-million-metrics-a-second-1721476aaff5)
* [Logging at LinkedIn](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying)
* [Scalable and Reliable Log Ingestion at Pinterest](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754)
* [High-performance Replicated Log Service at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2015/building-distributedlog-twitter-s-high-performance-replicated-log-servic.html)
* [Logging Service with Spark at CERN Accelerator](https://databricks.com/blog/2017/12/14/the-architecture-of-the-next-cern-accelerator-logging-service.html)
* [Logging and Aggregation at Quora](https://engineering.quora.com/Logging-and-Aggregation-at-Quora)
* [Centralized Application Logging at eBay](https://tech.ebayinc.com/engineering/low-latency-and-high-throughput-cal-ingress/)
* [Enrich VPC Flow Logs at Hyper Scale to provide Network Insight at Netflix](https://netflixtechblog.com/hyper-scale-vpc-flow-logs-enrichment-to-provide-network-insight-e5f1db02910d)
* [BookKeeper: Distributed Log Storage at Yahoo](https://yahooeng.tumblr.com/post/109908973316/bookkeeper-yahoos-distributed-log-storage-is)
* [LogDevice: Distributed Data Store for Logs at Facebook](https://code.facebook.com/posts/357056558062811/logdevice-a-distributed-data-store-for-logs/)
* [LogFeeder: Log Collection System at Yelp](https://engineeringblog.yelp.com/2018/03/introducing-logfeeder.html)
* [DBLog: Generic Change-Data-Capture Framework at Netflix](https://medium.com/netflix-techblog/dblog-a-generic-change-data-capture-framework-69351fb9099b)
* [Indexing and Querying Telemetry Logs with Lucene at Palantir](https://medium.com/palantir/indexing-and-querying-telemetry-logs-with-lucene-234c5ce3e5f3)
* [Stability and Scalability for Search at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2022/stability-and-scalability-for-search)
* [Manas: High Performing Customized Search System at Pinterest](https://medium.com/@Pinterest_Engineering/manas-a-high-performing-customized-search-system-cf189f6ca40f)
* [Nebula: Storage Platform to Build Search Backends at Airbnb](https://medium.com/airbnb-engineering/nebula-as-a-storage-platform-to-build-airbnbs-search-backends-ecc577b05f06)
* [Improve Performance using Elasticsearch Plugins (2 parts) at Tinder](https://medium.com/tinder-engineering/how-we-improved-our-performance-using-elasticsearch-plugins-part-2-b051da2ee85b)
* [Moving Core Business Search to Elasticsearch at Yelp](https://engineeringblog.yelp.com/2017/06/moving-yelps-core-business-search-to-elasticsearch.html)
* [Self-Ranking Search with Elasticsearch at Wattpad](http://engineering.wattpad.com/post/146216619727/self-ranking-search-with-elasticsearch-at-wattpad)
* [MemSQL Architecture - The Fast (MVCC, InMem, LockFree, CodeGen) And Familiar (SQL)](http://highscalability.com/blog/2012/8/14/memsql-architecture-the-fast-mvcc-inmem-lockfree-codegen-and.html)
* [Image Recovery at Scale Using S3 Versioning at Trivago](https://tech.trivago.com/2018/09/03/efficient-image-recovery-at-scale-using-amazon-s3-versioning/)
* [Dynamometer: Scale Testing HDFS on Minimal Hardware with Maximum Fidelity at LinkedIn](https://engineering.linkedin.com/blog/2018/02/dynamometer--scale-testing-hdfs-on-minimal-hardware-with-maximum)
* [Hammerspace: Persistent, Concurrent, Off-heap Storage at Airbnb](https://medium.com/airbnb-engineering/hammerspace-persistent-concurrent-off-heap-storage-3db39bb04472)
* [MezzFS: Mounting Object Storage in Media Processing Platform at Netflix](https://medium.com/netflix-techblog/mezzfs-mounting-object-storage-in-netflixs-media-processing-platform-cda01c446ba)
* [PostgreSQL at Twitch](https://blog.twitch.tv/how-twitch-uses-postgresql-c34aa9e56f58)
* [Scaling MySQL-based Financial Reporting System at Airbnb](https://medium.com/airbnb-engineering/tracking-the-money-scaling-financial-reporting-at-airbnb-6d742b80f040)
* [Scaling Database Access for 100s of Billions of Queries per Day at PayPal](https://medium.com/paypal-engineering/scaling-database-access-for-100s-of-billions-of-queries-per-day-paypal-introducing-hera-e192adacda54)
* [MySQL Parallel Replication (4 parts) at Booking.com](https://medium.com/booking-com-infrastructure/evaluating-mysql-parallel-replication-part-4-annex-under-the-hood-eb456cf8b2fb)
* [Mitigating MySQL Replication Lag and Reducing Read Load at Github](https://githubengineering.com/mitigating-replication-lag-and-reducing-read-load-with-freno/)
* [Black-Box Auditing: Verifying End-to-End Replication Integrity between MySQL and Redshift at Yelp](https://engineeringblog.yelp.com/2018/04/black-box-auditing.html)
* [Partitioning Main MySQL Database at Airbnb](https://medium.com/airbnb-engineering/how-we-partitioned-airbnb-s-main-database-in-two-weeks-55f7e006ff21)
* [Cassandra at Instagram](https://www.slideshare.net/DataStax/cassandra-at-instagram-2016)
* [Storing Images in Cassandra at Walmart](https://medium.com/walmartlabs/building-object-store-storing-images-in-cassandra-walmart-scale-a6b9c02af593)
* [Storing Messages with Cassandra at Discord](https://blog.discordapp.com/how-discord-stores-billions-of-messages-7fa6ec7ee4c7)
* [Moving Food Feed from Redis to Cassandra at Zomato](https://www.zomato.com/blog/how-we-moved-our-food-feed-from-redis-to-cassandra)
* [Benchmarking Cassandra Scalability on AWS at Netflix](https://medium.com/netflix-techblog/benchmarking-cassandra-scalability-on-aws-over-a-million-writes-per-second-39f45f066c9e)
* [Service Decomposition at Scale with Cassandra at Intuit QuickBooks](https://quickbooks-engineering.intuit.com/service-decomposition-at-scale-70405ac2f637)
* [Cassandra for Keeping Counts In Sync at SoundCloud](https://developers.soundcloud.com/blog/keeping-counts-in-sync)
* [Cassandra Driver Configuration for Improved Performance and Load Balancing at Glassdoor](https://medium.com/glassdoor-engineering/cassandra-driver-configuration-for-improved-performance-and-load-balancing-1b0106ce12bb)
* [eBay: Building Mission-Critical Multi-Data Center Applications with MongoDB](https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb)
* [The AWS and MongoDB Infrastructure of Parse (acquired by Facebook)](https://medium.baqend.com/parse-is-gone-a-few-secrets-about-their-infrastructure-91b3ab2fcf71)
* [Espresso: Distributed Document Store at LinkedIn](https://engineering.linkedin.com/espresso/introducing-espresso-linkedins-hot-new-distributed-document-store)
* [Beringei: High-performance Time Series Storage Engine at Facebook](https://code.facebook.com/posts/952820474848503/beringei-a-high-performance-time-series-storage-engine/)
* [MetricsDB: TimeSeries Database for storing metrics at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/metricsdb.html)
* [Atlas: In-memory Dimensional Time Series Database at Netflix](https://medium.com/netflix-techblog/introducing-atlas-netflixs-primary-telemetry-platform-bd31f4d8ed9a)
* [Heroic: Time Series Database at Spotify](https://labs.spotify.com/2015/11/17/monitoring-at-spotify-introducing-heroic/)
* [Roshi: Distributed Storage System for Time-Series Event at SoundCloud](https://developers.soundcloud.com/blog/roshi-a-crdt-system-for-timestamped-events)
* [Goku: Time Series Database at Pinterest](https://medium.com/@Pinterest_Engineering/goku-building-a-scalable-and-high-performant-time-series-database-system-a8ff5758a181)
* [Stemma: Distributed Git Server at Palantir](https://medium.com/@palantir/stemma-distributed-git-server-70afbca0fc29)
* [Configuration Management for Distributed Systems at Flickr](https://code.flickr.net/2016/03/24/configuration-management-for-distributed-systems-using-github-and-cfg4j/)
* [Single Repository at Google](https://cacm.acm.org/magazines/2016/7/204032-why-google-stores-billions-of-lines-of-code-in-a-single-repository/fulltext)
* [Scaling Infrastructure and (Git) Workflow at Adyen](https://medium.com/adyen/from-0-100-billion-scaling-infrastructure-and-workflow-at-adyen-7b63b690dfb6)
* [Scaling Continuous Integration and Continuous Delivery](https://www.synopsys.com/blogs/software-security/agile-cicd-devops-glossary/)
* [Continuous Integration Stack at Facebook](https://code.fb.com/web/rapid-release-at-massive-scale/)
* [Continuous Integration with Distributed Repositories and Dependencies at Netflix](https://medium.com/netflix-techblog/towards-true-continuous-integration-distributed-repositories-and-dependencies-2a2e3108c051)
* [Continuous Integration and Deployment with Bazel at Dropbox](https://blogs.dropbox.com/tech/2019/12/continuous-integration-and-deployment-with-bazel/)
* [Screwdriver: Continuous Delivery Build System for Dynamic Infrastructure at Yahoo](https://yahooeng.tumblr.com/post/155765242061/open-sourcing-screwdriver-yahoos-continuous)
* [Resilience Engineering with Project Waterbear at LinkedIn](https://engineering.linkedin.com/blog/2017/11/resilience-engineering-at-linkedin-with-project-waterbear)
* [Resiliency against Traffic Oversaturation at iHeartRadio](https://tech.iheart.com/resiliency-against-traffic-oversaturation-77c5ed92a5fb)
* [Resiliency in Distributed Systems at GO-JEK](https://blog.gojekengineering.com/resiliency-in-distributed-systems-efd30f74baf4)
* [Practical NoSQL Resilience Design Pattern for the Enterprise at eBay](https://www.ebayinc.com/stories/blogs/tech/practical-nosql-resilience-design-pattern-for-the-enterprise/)
* [Ensuring Resilience to Disaster at Quora](https://engineering.quora.com/Ensuring-Quoras-Resilience-to-Disaster)
* [Site Resiliency at Expedia](https://www.infoq.com/presentations/expedia-website-resiliency?utm_source=presentations_about_Case_Study&utm_medium=link&utm_campaign=Case_Study)
* [Introduction to Modern Network Load Balancing and Proxying](https://blog.envoyproxy.io/introduction-to-modern-network-load-balancing-and-proxying-a57f6ff80236)
* [Load Balancing infrastructure to support more than 1.3 billion users at Facebook](https://www.usenix.org/conference/srecon15europe/program/presentation/shuff)
* [DHCPLB: DHCP Load Balancer at Facebook](https://code.facebook.com/posts/1734309626831603/dhcplb-an-open-source-load-balancer/)
* [Katran: Scalable Network Load Balancer at Facebook](https://code.facebook.com/posts/1906146702752923/open-sourcing-katran-a-scalable-network-load-balancer/)
* [Deterministic Aperture: A Distributed, Load Balancing Algorithm at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/daperture-load-balancer.html)
* [Load Balancing with Eureka at Netflix](https://medium.com/netflix-techblog/netflix-shares-cloud-load-balancing-and-failover-tool-eureka-c10647ef95e5)
* [Edge Load Balancing at Netflix](https://medium.com/netflix-techblog/netflix-edge-load-balancing-695308b5548c)
* [Zuul 2: Cloud Gateway at Netflix](https://medium.com/netflix-techblog/open-sourcing-zuul-2-82ea476cb2b3)
* [Load Balancing at Yelp](https://engineeringblog.yelp.com/2017/05/taking-zero-downtime-load-balancing-even-further.html)
* [Load Balancing at Github](https://githubengineering.com/introducing-glb/)
* [Consistent Hashing to Improve Load Balancing at Vimeo](https://medium.com/vimeo-engineering-blog/improving-load-balancing-with-a-new-consistent-hashing-algorithm-9f1bd75709ed)
* [UDP Load Balancing at 500 pixel](https://developers.500px.com/udp-load-balancing-with-keepalived-167382d7ad08)
* [QALM: QoS Load Management Framework at Uber](https://eng.uber.com/qalm/)
* [Traffic Steering using Rum DNS at LinkedIn](https://www.usenix.org/conference/srecon17europe/program/presentation/rastogi)
* [Traffic Infrastructure (Edge Network) at Dropbox](https://blogs.dropbox.com/tech/2018/10/dropbox-traffic-infrastructure-edge-network/)
* [Autoscaling Pub-Sub Consumers at Spotify](https://labs.spotify.com/2017/11/20/autoscaling-pub-sub-consumers/)
* [Autoscaling Bigtable Clusters based on CPU Load at Spotify](https://labs.spotify.com/2018/12/18/bigtable-autoscaler-saving-money-and-time-using-managed-storage/)
* [Availability in Globally Distributed Storage Systems at Google](http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36737.pdf)
* [Enforce Timeout: A Reliability Methodology at DoorDash](https://doordash.engineering/2018/12/21/enforce-timeout-a-doordash-reliability-methodology/)
* [Troubleshooting a Connection Timeout Issue with tcp_tw_recycle Enabled at eBay](https://www.ebayinc.com/stories/blogs/tech/a-vip-connection-timeout-issue-caused-by-snat-and-tcp-tw-recycle/)
* [Crash-safe Replication for MySQL at Booking.com](https://medium.com/booking-com-infrastructure/better-crash-safe-replication-for-mysql-a336a69b317f)
* [Bulkheads: Partition and Tolerate Failure in One Part](https://skife.org/architecture/fault-tolerance/2009/12/31/bulkheads.html)
* [Steady State: Always Put Logs on Separate Disk](https://docs.microsoft.com/en-us/sql/relational-databases/policy-based-management/place-data-and-log-files-on-separate-drives)
* [Throttling: Maintain a Steady Pace](http://www.sosp.org/2001/papers/welsh.pdf)
* [Multi-Clustering: Improving Resiliency and Stability of a Large-scale Monolithic API Service at LinkedIn](https://engineering.linkedin.com/blog/2017/11/improving-resiliency-and-stability-of-a-large-scale-api)
* [Improving Performance with Background Data Prefetching at Instagram](https://engineering.instagram.com/improving-performance-with-background-data-prefetching-b191acb39898)
* [Fixing Linux filesystem performance regressions at LinkedIn](https://engineering.linkedin.com/blog/2020/fixing-linux-filesystem-performance-regressions)
* [Compression Techniques to Solve Network I/O Bottlenecks at eBay](https://www.ebayinc.com/stories/blogs/tech/how-ebays-shopping-cart-used-compression-techniques-to-solve-network-io-bottlenecks/)
* [Optimizing Web Servers for High Throughput and Low Latency at Dropbox](https://blogs.dropbox.com/tech/2017/09/optimizing-web-servers-for-high-throughput-and-low-latency/)
* [Linux Performance Analysis in 60.000 Milliseconds at Netflix](https://medium.com/netflix-techblog/linux-performance-analysis-in-60-000-milliseconds-accc10403c55)
* [Live Downsizing Google Cloud Persistent Disks (PD-SSD) at Mixpanel](https://engineering.mixpanel.com/2018/07/31/live-downsizing-google-cloud-pds-for-fun-and-profit/)
* [Server Side Rendering at Wix](https://www.youtube.com/watch?v=f9xI2jR71Ms)
* [30x Performance Improvements on MySQLStreamer at Yelp](https://engineeringblog.yelp.com/2018/02/making-30x-performance-improvements-on-yelps-mysqlstreamer.html)
* [Performance Monitoring with Riemann and Clojure at Walmart](https://medium.com/walmartlabs/performance-monitoring-with-riemann-and-clojure-eafc07fcd375)
* [Profiling C++ (Part 1: Optimization, Part 2: Measurement and Analysis) at Riot Games](https://engineering.riotgames.com/news/profiling-optimisation)
* [Hardware-Assisted Video Transcoding at Dailymotion](https://medium.com/dailymotion-engineering/hardware-assisted-video-transcoding-at-dailymotion-66cd2db448ae)
* [Cross Shard Transactions at 10 Million RPS at Dropbox](https://blogs.dropbox.com/tech/2018/11/cross-shard-transactions-at-10-million-requests-per-second/)
* [Improving key expiration in Redis at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/improving-key-expiration-in-redis.html)
* [Predictive CPU isolation of containers at Netflix](https://medium.com/netflix-techblog/predictive-cpu-isolation-of-containers-at-netflix-91f014d856c7)
* [Unthrottled: Fixing CPU Limits in the Cloud (2 parts) at Indeed](https://engineering.indeedblog.com/blog/2019/12/unthrottled-fixing-cpu-limits-in-the-cloud/)
* [Garbage Collection in Java Applications at LinkedIn](https://engineering.linkedin.com/garbage-collection/garbage-collection-optimization-high-throughput-and-low-latency-java-applications)
* [Garbage Collection in Go Application at Twitch](https://blog.twitch.tv/go-memory-ballast-how-i-learnt-to-stop-worrying-and-love-the-heap-26c2462549a2)
* [Analyzing V8 Garbage Collection Logs at Alibaba](https://www.linux.com/blog/can-nodejs-scale-ask-team-alibaba)
* [Python Garbage Collection for Dropping 50% Memory Growth Per Request at Instagram](https://instagram-engineering.com/copy-on-write-friendly-python-garbage-collection-ad6ed5233ddf)
* [Optimizing 360 Photos at Scale at Facebook](https://code.facebook.com/posts/129055711052260/optimizing-360-photos-at-scale/)
* [Reducing Image File Size in the Photos Infrastructure at Etsy](https://codeascraft.com/2017/05/30/reducing-image-file-size-at-etsy/)
* [Improving GIF Performance at Pinterest](https://medium.com/@Pinterest_Engineering/improving-gif-performance-on-pinterest-8dad74bf92f1)
* [Optimizing Video Playback Performance at Pinterest](https://medium.com/@Pinterest_Engineering/optimizing-video-playback-performance-caf55ce310d1)
* [Optimizing Video Stream for Low Bandwidth with Dynamic Optimizer at Netflix](https://medium.com/netflix-techblog/optimized-shot-based-encodes-now-streaming-4b9464204830)
* [The Process of Optimizing for Client Performance at Expedia](https://medium.com/expedia-engineering/go-fast-or-go-home-the-process-of-optimizing-for-client-performance-57bb497402e)
* [Performance Optimization by Brotli Compression](https://blogs.akamai.com/2016/02/understanding-brotlis-potential.html)
* [Boosting Site Speed Using Brotli Compression at LinkedIn](https://engineering.linkedin.com/blog/2017/05/boosting-site-speed-using-brotli-compression)
* [Brotli at Booking.com](https://medium.com/booking-com-development/bookings-journey-with-brotli-978b249d34f3)
* [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)
* [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)
* [HALO: Hardware Analytics and Lifecycle Optimization at Facebook](https://code.fb.com/data-center-engineering/hardware-analytics-and-lifecycle-optimization-halo-at-facebook/)
* [Delta: Data Synchronization and Enrichment Platform at Netflix](https://medium.com/netflix-techblog/delta-a-data-synchronization-and-enrichment-platform-e82c36a79aee)
* [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-architecture-72f7c643ee6f)
* [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/)
* [TensorFlowOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/157196488076/open-sourcing-tensorflowonspark-distributed-deep)
* [CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/139916828451/caffeonspark-open-sourced-for-distributed-deep)
* [Spark on Scala: Analytics Reference Architecture at Adobe](https://medium.com/adobetech/spark-on-scala-adobe-analytics-reference-architecture-7457f5614b4c)
* [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)
* [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)
* [Scaling a Mature Data Pipeline - Managing Overhead at Airbnb](https://medium.com/airbnb-engineering/scaling-a-mature-data-pipeline-managing-overhead-f34835cbc866)
* [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)
* [Scaling Big Data Access Controls at Pinterest](https://medium.com/pinterest-engineering/securely-scaling-big-data-access-controls-at-pinterest-bbc3406a1695)
* [Infrastructure to Run User Forecasts at Spotify](https://engineering.atspotify.com/2022/06/how-we-built-infrastructure-to-run-user-forecasts-at-spotify/)
* [Flyte: Cloud Native Machine Learning and Data Processing Platform at Lyft](https://eng.lyft.com/introducing-flyte-cloud-native-machine-learning-and-data-processing-platform-fb2bb3046a59)
* [LyftLearn: ML Model Training Infrastructure built on Kubernetes at Lyft](https://eng.lyft.com/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb)
* [Concourse: Generating Personalized Content Notifications in Near-Real-Time at LinkedIn](https://engineering.linkedin.com/blog/2018/05/concourse--generating-personalized-content-notifications-in-near)
* [Altus Care: Applying a Chatbot to Platform Engineering at eBay](https://www.ebayinc.com/stories/blogs/tech/altus-care-apply-chatbot-to-ebay-platform-engineering/)
* [PyKrylov: Accelerating Machine Learning Research at eBay](https://tech.ebayinc.com/engineering/pykrylov-accelerating-machine-learning-research-at-ebay/)
* [Box Graph: Spontaneous Social Network at Box](https://blog.box.com/blog/box-graph-how-we-built-spontaneous-social-network/)
* [PricingNet: Pricing Modelling with Neural Networks at Skyscanner](https://hackernoon.com/pricingnet-modelling-the-global-airline-industry-with-neural-networks-833844d20ea6)
* [PinText: Multitask Text Embedding System at Pinterest](https://medium.com/pinterest-engineering/pintext-a-multitask-text-embedding-system-in-pinterest-b80ece364555)
* [Cannes: ML saves $1.7M a year on document previews at Dropbox](https://dropbox.tech/machine-learning/cannes--how-ml-saves-us--1-7m-a-year-on-document-previews)
* [Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at Yelp](https://engineeringblog.yelp.com/2018/01/building-a-distributed-ml-pipeline-part1.html)
* [Deep Learning for Image Classification Experiment at Mercari](https://medium.com/mercari-engineering/mercaris-image-classification-experiment-using-deep-learning-9b4e994a18ec)
* [Deep Learning for Frame Detection in Product Images at Allegro](https://allegro.tech/2016/12/deep-learning-for-frame-detection.html)
* [Improving Photo Selection With Deep Learning at TripAdvisor](http://engineering.tripadvisor.com/improving-tripadvisor-photo-selection-deep-learning/)
* [Personalized Recommendations for Experiences Using Deep Learning at TripAdvisor](https://www.tripadvisor.com/engineering/personalized-recommendations-for-experiences-using-deep-learning/)
* [Personalised Recommender Systems at BBC](https://medium.com/bbc-design-engineering/developing-personalised-recommender-systems-at-the-bbc-e26c5e0c4216)
* [Natural Language Processing and Content Analysis (2 parts) at Condé Nast](https://technology.condenast.com/story/natural-language-processing-and-content-analysis-at-conde-nast-part-2-system-architecture)
* [Mapping the World of Music Using Machine Learning (2 parts) at iHeartRadio](https://tech.iheart.com/mapping-the-world-of-music-using-machine-learning-part-2-aa50b6a0304c)
* [Machine Learning to Improve Streaming Quality at Netflix](https://medium.com/netflix-techblog/using-machine-learning-to-improve-streaming-quality-at-netflix-9651263ef09f)
* [Machine Learning to Match Drivers & Riders at GO-JEK](https://blog.gojekengineering.com/how-we-use-machine-learning-to-match-drivers-riders-b06d617b9e5)
* [Improving Video Thumbnails with Deep Neural Nets at YouTube](https://youtube-eng.googleblog.com/2015/10/improving-youtube-video-thumbnails-with_8.html)
* [Quantile Regression for Delivering On Time at Instacart](https://tech.instacart.com/how-instacart-delivers-on-time-using-quantile-regression-2383e2e03edb)
* [Cross-Lingual End-to-End Product Search with Deep Learning at Zalando](https://jobs.zalando.com/tech/blog/search-deep-neural-network/)
* [Machine Learning at Jane Street](https://blog.janestreet.com/real-world-machine-learning-part-1/)
* [Large-Scale Machine Learning Pipeline for Job Recommendations at Indeed](http://engineering.indeedblog.com/blog/2016/04/building-a-large-scale-machine-learning-pipeline-for-job-recommendations/)
* [Atom Smashing using Machine Learning at CERN](https://cdn.oreillystatic.com/en/assets/1/event/144/Atom%20smashing%20using%20machine%20learning%20at%20CERN%20Presentation.pdf)
* [Map Pins with DBSCAN & Random Forests at Foursquare](https://engineering.foursquare.com/you-are-probably-here-better-map-pins-with-dbscan-random-forests-9d51e8c1964d)
* [Scaling Machine Learning to Recommend Driving Routes at Pivotal](http://engineering.pivotal.io/post/scaling-machine-learning-to-recommend-driving-routes/)
* [Machine Learning for Indexing Text from Billions of Images at Dropbox](https://blogs.dropbox.com/tech/2018/10/using-machine-learning-to-index-text-from-billions-of-images/)
* [Computer Vision at Airbnb](https://medium.com/airbnb-engineering/amenity-detection-and-beyond-new-frontiers-of-computer-vision-at-airbnb-144a4441b72e)
* [SplitNet Architecture for Ad Candidate Ranking at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/splitnet-architecture-for-ad-candidate-ranking.html)
* [Moderating Promotional Spam and Inappropriate Content in Photos at Scale at Yelp](https://engineeringblog.yelp.com/2021/05/moderating-promotional-spam-and-inappropriate-content-in-photos-at-scale-at-yelp.html)
* [Architecture of Ad Platform at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2020/building-twitters-ad-platform-architecture-for-the-future.html)
* [Lightweight Distributed Architecture to Handle Thousands of Library Releases at eBay](https://tech.ebayinc.com/engineering/a-lightweight-distributed-architecture-to-handle-thousands-of-library-releases-at-ebay/)
* [Nearline System for Scale and Performance (2 parts) at Glassdoor](https://medium.com/glassdoor-engineering/building-a-nearline-system-for-scale-and-performance-part-ii-9e01bf51b23d)
* [Real-time User Action Counting System for Ads at Pinterest](https://medium.com/@Pinterest_Engineering/building-a-real-time-user-action-counting-system-for-ads-88a60d9c9a)
* [Simone: Distributed Simulation Service at Netflix](https://medium.com/netflix-techblog/https-medium-com-netflix-techblog-simone-a-distributed-simulation-service-b2c85131ca1b)
* [Seagull: Distributed System that Helps Running > 20 Million Tests Per Day at Yelp](https://engineeringblog.yelp.com/2017/04/how-yelp-runs-millions-of-tests-every-day.html)
* [PriceAggregator: Intelligent System for Hotel Price Fetching (3 parts) at Agoda](https://medium.com/agoda-engineering/priceaggregator-an-intelligent-system-for-hotel-price-fetching-part-3-52acfc705081)
* [Architecture of Following Feed, Interest Feed, and Picked For You at Pinterest](https://medium.com/@Pinterest_Engineering/building-a-dynamic-and-responsive-pinterest-7d410e99f0a9)
* [Ads Pacing Service at Twitter](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2021/how-we-built-twitter-s-highly-reliable-ads-pacing-service)
* [Architectures of Finance, Banking, and Payment Systems](https://www.redhat.com/architect/portfolio/detail/12-integrating-a-modern-payments-architecture)
* [Bank Backend at Monzo](https://monzo.com/blog/2016/09/19/building-a-modern-bank-backend/)
* [Trading Platform for Scale at Wealthsimple](https://medium.com/@Wealthsimple/engineering-at-wealthsimple-reinventing-our-trading-platform-for-scale-17e332241b6c)
* [Core Banking System at Margo Bank](https://medium.com/margobank/choosing-an-architecture-85750e1e5a03)
* [Avoiding Double Payments in a Distributed Payments System at Airbnb](https://medium.com/airbnb-engineering/avoiding-double-payments-in-a-distributed-payments-system-2981f6b070bb)
* [Handles Millions of Digital Transactions Safely Everyday at Paytm](https://paytm.com/blog/engineering/how-paytm-handles-millions-of-digital-transactions-safely-everyday/)
* [Software Engineering Advice from Building Large-Scale Distributed Systems - Jeff Dean](https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)
* [Anatomy of a System Design Interview](https://hackernoon.com/anatomy-of-a-system-design-interview-4cb57d75a53f)
* [8 Things You Need to Know Before a System Design Interview](http://blog.gainlo.co/index.php/2015/10/22/8-things-you-need-to-know-before-system-design-interviews/)
* [Top 10 System Design Interview Questions ](https://hackernoon.com/top-10-system-design-interview-questions-for-software-engineers-8561290f0444)
* [Top 10 Common Large-Scale Software Architectural Patterns in a Nutshell](https://towardsdatascience.com/10-common-software-architectural-patterns-in-a-nutshell-a0b47a1e9013)
* [How NOT to design Netflix in your 45-minute System Design Interview?](https://hackernoon.com/how-not-to-design-netflix-in-your-45-minute-system-design-interview-64953391a054)
* [Scaling Engineering Teams at Twitter](https://www.youtube.com/watch?v=-PXi_7Ld5kU)
* [Scaling Decision-Making Across Teams at LinkedIn](https://engineering.linkedin.com/blog/2018/03/scaling-decision-making-across-teams-within-linkedin-engineering)
* [Scaling Data Science Team at GOJEK](https://blog.gojekengineering.com/the-dynamics-of-scaling-an-organisation-cb96dbe8aecd)
* [Scaling Agile at Zalando](https://jobs.zalando.com/tech/blog/scaling-agile-zalando/?gh_src=4n3gxh1)
* [Lessons Learned from Scaling a Product Team at Intercom](https://blog.intercom.com/how-we-build-software/)
* [Hiring, Managing, and Scaling Engineering Teams at Typeform](https://medium.com/@eleonorazucconi/toby-oliver-cto-typeform-on-hiring-managing-and-scaling-engineering-teams-86bef9e5a708)
* [Team Model for Scaling a Design System at Salesforce](https://medium.com/salesforce-ux/the-salesforce-team-model-for-scaling-a-design-system-d89c2a2d404b)
* [From 2 Founders to 1000 Employees at Transferwise](https://medium.com/transferwise-ideas/from-2-founders-to-1000-employees-how-a-small-scale-startup-grew-into-a-global-community-9f26371a551b)
* [Lessons Learned Growing a UX Team from 10 to 170 at Adobe](https://medium.com/thinking-design/lessons-learned-growing-a-ux-team-from-10-to-170-f7b47be02262)
* [Using Metrics to Improve the Development Process (and Coach People) at Indeed](https://engineering.indeedblog.com/blog/2018/10/using-metrics-to-improve-the-development-process-and-coach-people/)
* [Mistakes to Avoid while Creating an Internal Product at Skyscanner](https://medium.com/@SkyscannerEng/9-mistakes-to-avoid-while-creating-an-internal-product-63d579b00b1a)
* [Experiment Idea Review at Pinterest](https://medium.com/pinterest-engineering/how-pinterest-supercharged-its-growth-team-with-experiment-idea-review-fd6571a02fb8)
* [Distributed Systems in One Lesson - Tim Berglund, Senior Director of Developer Experience at Confluent](https://www.youtube.com/watch?v=Y6Ev8GIlbxc)
* [Building Real Time Infrastructure at Facebook - Jeff Barber and Shie Erlich, Software Engineer at Facebook](https://www.usenix.org/conference/srecon17americas/program/presentation/erlich)
* [Building Reliable Social Infrastructure for Google - Marc Alvidrez, Senior Manager at Google](https://www.usenix.org/conference/srecon16/program/presentation/alvidrez)
* [Site Reliability Engineering at Dropbox - Tammy Butow, Site Reliability Engineering Manager at Dropbox](https://www.youtube.com/watch?v=ggizCjUCCqE)
* [How Google Does Planet-Scale for Planet-Scale Infra - Melissa Binde, SRE Director for Google Cloud Platform](https://www.youtube.com/watch?v=H4vMcD7zKM0)
* [Architecture to Handle 80K RPS Celebrity Sales at Shopify - Simon Eskildsen, Engineering Lead at Shopify](https://www.youtube.com/watch?v=N8NWDHgWA28)
* [Performance Optimization for the Greater China Region at Salesforce - Jeff Cheng, Enterprise Architect at Salesforce](https://www.salesforce.com/video/1757880/)
* [How GIPHY Delivers a GIF to 300 Millions Users - Alex Hoang and Nima Khoshini, Services Engineers at GIPHY](https://vimeo.com/252367076)
* [High Performance Packet Processing Platform at Alibaba - Haiyong Wang, Senior Director at Alibaba](https://www.youtube.com/watch?v=wzsxJqeVIhY&list=PLMu8-hpCxIVENuAue7bd0eCAglLGY_8AW&index=7)
* [Solving Large-scale Data Center and Cloud Interconnection Problems - Ihab Tarazi, CTO at Equinix](https://atscaleconference.com/videos/solving-large-scale-data-center-and-cloud-interconnection-problems/)
* [Scaling with Performance at Facebook - Bill Jia, VP of Infrastructure at Facebook](https://atscaleconference.com/videos/performance-scale-2018-opening-remarks/)
* [Scaling Live Videos to a Billion Users at Facebook - Sachin Kulkarni, Director of Engineering at Facebook](https://www.youtube.com/watch?v=IO4teCbHvZw)
* [Scaling Infrastructure at Instagram - Lisa Guo, Instagram Engineering](https://www.youtube.com/watch?v=hnpzNAPiC0E)
* [Scaling Infrastructure at Twitter - Yao Yue, Staff Software Engineer at Twitter](https://www.youtube.com/watch?v=6OvrFkLSoZ0)
* [Scaling Infrastructure at Etsy - Bethany Macri, Engineering Manager at Etsy](https://www.youtube.com/watch?v=LfqyhM1LeIU)
* [Scaling Real-time Infrastructure at Alibaba for Global Shopping Holiday - Xiaowei Jiang, Senior Director at Alibaba](https://atscaleconference.com/videos/scaling-alibabas-real-time-infrastructure-for-global-shopping-holiday/)
* [Scaling Load Balancing Infra to Support 1.3 Billion Users at Facebook - Patrick Shuff, Production Engineer at Facebook](https://www.youtube.com/watch?v=bxhYNfFeVF4)
* [Scaling (a NSFW site) to 200 Million Views A Day And Beyond - Eric Pickup, Lead Platform Developer at MindGeek](https://www.youtube.com/watch?v=RlkCdM_f3p4)
* [Scaling Multitenant Architecture Across Multiple Data Centres at Shopify - Weingarten, Engineering Lead at Shopify](https://www.youtube.com/watch?v=F-f0-k46WVk)
