How Canva Collects 25 Billion Events Per Day
- Long Nguyen tl;dr: “These use cases are powered by a stream of analytics events at a rate of 25 billion events per day (800 billion events per month), with 99.999% uptime. Our Product Analytics Platform team manages this data pipeline. Their mission is to provide a reliable, ergonomic, and cost-effective way to collect user interaction events and distribute the data to a wide range of destinations for consumption.”featured in #531
Struggling with Snowflake Costs? Try our Cost Optimization Calculator
tl;dr: Snowflake costs skyrocket for SaaS providers because the need to deliver real-time, interactive analytics is always on. If your Snowflake bill is spiraling, try our cost optimization calculator to discover your potential savings when using a Snowflake warehouse for ad-hoc queries. (No form required)featured in #501
Top 5 Challenges of Designing Your Data Warehouse for Multi-Tenant Analytics
tl;dr: Data warehouses are built to store large volumes of data from numerous sources, not for SaaS platforms working with multi-tenant analytics where data security is vital. This guide helps you avoid the headaches that come with that architecture mismatch featuring solutions from our analytics experts.featured in #499
Custom Data Models: The Key to Unlocking Powerful Embedded Analytics
- Brian Dreyer tl;dr: Without custom data models, even the most advanced analytics fail to deliver value, leading to customer churn. If you’re a SaaS leader, learn why custom data models are imperative for multi-tenant software platforms and four features of conventional data warehousing that are limiting your growth.featured in #495
Multi-Tenant Analytics: Why It’s Hard To Build
- Brian Dreyer tl;dr: Multi-tenant analytics empowers SaaS companies to provide more value to their customers, while ensuring privacy and security of their data. It delivers cost efficiency, scalability, greater customization, and more. It’s a win-win situation for SaaS, but it’s hard to build. Learn why in this article.featured in #493
Real-Time Analytics For Mobile App Crashes using Apache Pinot
tl;dr: "At Uber, we have built a system called “Healthline” to help with our Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR) issues and to avoid potential outages and large-scale user impacts. Due to our ability to detect the issues in real time, this has become the go-to tool for release managers to observe the impact of canary release and decide whether to proceed further or to rollback. In this article we will be sharing details on how we are leveraging Apache Pinot™ to achieve this in real time at Uber scale."featured in #463
Unified Session For Analytical Events
tl;dr: Uber's post delves into the challenges and solutions surrounding the implementation of a "Unified Session" for analytical events. Historically, Uber utilized multiple session definitions, leading to fragmented data and limiting cross-domain analytics. The article introduces the "Unified Session" concept, which aims to bridge frontend and backend signals under a single session definition. Key strategies include: (1) Extensible session definition: adapting to various marketplace signals across different lines of business. (2) Scalable system design: handling Uber-scale traffic and efficiently propagating session IDs. (3) Mobile call flow: reducing backend calls by utilizing session cookies. (4) Migration of data lake: a two-phase approach ensuring seamless transition to the new session definition. (5) Resilient recovery: ensuring data consistency and system reliability.featured in #456
Goodbye, Google Analytics - Why and How You Should Leave The Platform
- Martin Heinz tl;dr: 2 main reasons why: (1) Court rulings in multiple EU countries stating that it's illegal to use GA, as the data of EU citizens is being transferred to US violates GDPR. (2) Google is deprecating Universal Analytics in 2023, resulting in the loss of access of data.featured in #312
Building Analytics (And An Analytics Team) At Mux
- James Isbell tl;dr: Mux is building more than just better video; we’re building the teams, systems, and culture to power online video for developers everywhere. We’re excited to share how we built out an Analytics function at Mux, and what we’ve learned along the way.featured in #299
Tracing At Slack: Thinking In Causal Graphs
- Suman Karumuri tl;dr: Suman describes the architecture of Slack's tracing system, which produces ~330M traces and ~8.5B spans per day.featured in #203