DATA ENGINEERING & ARCHITECTURE Why applying relational database update patterns to cloud lakehouses causes write amplification, high compute costs, and degraded query performance. Apache Iceberg is getting a lot of attention—and for good reason. It brings capabilities such as ACID transactions, snapshot isolation, schema evolution, time travel, and support
Columnar storage formats like Parquet and ORC, and columnar execution engines such as BigQuery, are widely used in analytics systems because they store data column by column on disk instead of row by row. When a query reads only the columns it needs, only those columns are read from disk,
Conversations about data formats often focus on Parquet versus Avro, or columnar versus row-oriented storage. What is discussed less often is the block-based structure that actually enables files to be split and processed in parallel across distributed systems. This article explains that underlying mechanism. A block (e.g.
Own the fire. * When things break, step forward. * Ownership under pressure is what separates engineers from passengers. * Roll up the sleeves * A fix without understanding is just moving the problem somewhere else. Don’t act blindly on someone else’s suggestion. * If it fails, you own the failure. * If it
Bitcoin is down around 50% from its peak, but the more interesting story is not the price. It is the supply. More than 95% of all Bitcoin has already been mined. But here is the strange part: Mining the remaining 5% will take more than 100 years. That sounds wrong
Every device connected to the internet needs an address. That address is called an IP address. The older internet runs primarily on IPv4, a 32-bit addressing system, which provides roughly 4.3 billion possible addresses. When the internet was young, that number sounded massive. Today? It’s nowhere near
About this series This is a five-episode series breaking down the Google Analytics (GA4) ecosystem by service, not by features. Think of it as a short series, not a binge. Each article focuses on one layer of the GA4 pipeline and explains: * What that layer actually does * What decisions
Previous Episode 0: The GA4 Pipeline in One View We’ll start with the first layer (Define) of the pipeline. This is where Google Tag Manager comes into the picture. Imagine you’re a marketer or analyst. Your site already uses GA4, and GTM has been set up once. Now
Previous Episode 1: Define | Google Tag Manager In episode 1, we looked at where events are defined. Now we move to the next step in the pipeline: Send This is the moment data actually leaves your website or app. What “send” means in GA4 Once an event is defined, it
Previous Episode 2: Send | gtag.js / Mobile SDKs In the previous episode, we looked at how events are sent. Now we move to the next step in the pipeline: Collect This is where GA4 receives events and processes them before you ever see them. What “collect” means in GA4 When
Previous Episode 3: Collect | GA4 Collection & Processing By the time data reaches this stage, it has already been: Defined, Sent, Collected & Processed. What remains is interpretation. This is where most users interact with GA4 and where its design tradeoffs become visible. GA4 interpretation is aggregate-first GA4 is
Imagine going to a movie theater. And instead of a film, you see: Total scenes: 127 You’d walk out. But that’s exactly how most companies watch their data. Every customer’s path tells a story A user visits your site. Reads reviews. Adds to cart. Hesitates. Leaves. Returns
Imagine you want to understand how coupons impact upselling. Are they driving additional purchases? What actions do customers take before contributing to upselling? Looking at individual data points won’t reveal the full picture. But when you examine the sequence of actions leading to a completed purchase, the story becomes
It sounds simple. A person visits a website, clicks around, maybe signs up, maybe leaves, maybe comes back later. You’d think we could see that whole story clearly. But in real life, tracking that path is painful. It doesn’t matter if a company has its own engineers or
How behavior sequences reveal hidden conversion intent Most analytics tools answer one question really well: “Who already converted?” We wanted to answer a harder and more valuable question: “Who is most likely to convert next, based purely on behavior?” So we built an AI algorithm that compares how converted users
Drawing from recent industry reports and benchmarks, here's the stark reality: * Up to 25% of marketing budgets are wasted due to unreliable signals, disconnected tools, and "phantom" metrics that make weak programs appear strong, forcing teams to over-rely on guesswork. Source * Without proper attribution models,