- Twitter Engineering
- Netflix Engineering
- Uber Engineering
- Doordash Engineering
- Segment Engineering
- Dropbox Engineering
- Spotify Engineering
- Medium Engineering
- Facebook Engineering
- Obvious Engineering Handbook
- Gitlab Handbook
- The trap of amateur senior
- A team everyone is a leader
- Brag document
- How Apple is organized for innovation
- Nuts bolts of building a platform team
- Scaling with common sense
- AWS well-architected
- Architecture behind one man startup
- Building a platform team
- What is data engineering
- Build frameworks not pipelines
- Engineers shouldn’t write ETLs
- Data pipelines design principles
- Explaning event driven
- Data monolith to data mesh
- Distributed Data Mesh
- Data Mesh Architecture
- Data Mesh Principles
- How Doordash is scaling its data platform
- Emerging Architectures for modern data infrastructure
- How to build data platform like a product
- Modern data platform
- Vision of Netflix data platform
- Modern unified data architecture
- The future of data engineering
- Datum 360 platform
- Google cloud data governance
- In-house data discovery platforms
- Data discovery platforms
- Fundamentals of data catalog
- Auto-tagging in the data catalog
- Goods: Organising Google’s datasets
- Satori Data access management
- Google data catalog and on-prem RDMBs
- Essential features of data catalog
- Cloud data access control
- Ground: Data context service
- Datahub architecture
- Kafka data masking
- What is data observability
- Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression
- DoorDash’s ML Platform - The Beginning
- GCP MLOps
- Data Lifecycle GCP
- CI-CD for machine learning
- ML Feature serving infra at Lfyt
- ML model training at Lfyt
- 7 traits of modern metric stack
- Airbnb: Democratizing Metric Definition and Discovery
- How Airbnb achieved metric consistency at scale
- Uber: uMetric
- Features of a CDP
- CDP Segmentation
- Adobe experience platform
- Exponea CDP
- Customer data platform guide
- HBR: The surprising power of online experiments
- HBR: A refresher on randomized controlled experiments
- HBR: A Refresher on A/B Testing
- HBR: A refresher on statistical significance
- HBR: A/B testing and benefits of an exp culture
- HBR: Avoid the pitfalls of A/B testing
- HBR: Beware spurious correlations
- Twitter: What and why of XP
- Twitter: Experimentation technical overview
- Twitter: Detecting and avoiding bucket imbalance
- Netflix: Experimentation platform
- Netflix: Experimentation Analysis
- Netflix: Mathematical engineering
- Netflix: Engineering for a science-centric XP platform
- Netflix: XP paper
- Netflix: Interleaving in online experiments
- Grab: XP
- Grab: large scale data capture
- Grab: scaling experimentation
- Grab: feature toggles
- Pinterest: Experimentation platform
- Pinterest: Experimentation Analysis
- Uber: Intelligent Experimentation
- Uber: XP - Under the hood
- Uber: BYOM
- Doordash: Switchback experimentation intro
- Doordash: Switchback experimentation
- Doordash: Experiment analysis
- Spotify: EXP Part 1
- Spotify: EXP Part 2
- Spotify: Using click house for experimentation
- Linkedin: AB Testing
- Linkedin: Making XP engine faster
- Paypal: Democratizing experimentation
- Stitchfix: Significant samples
- Stitchfix: Exp with resource constraints
- Stitchfix: Large scale experimentation
- Stitchfix: Building exp platform
- Optimizely: New stats engine
- Why AX
- Facebook's A B Platform Interactive Analysis in Realtime
- Facebook: Planout
- LucidChart: The fatal flaw of AB testing peeking
- Don’t use Bandit Algorithms