data warehouse

Warehouse vs Lake vs Lakehouse

Ask two engineers to compare warehouse vs lake vs lakehouse and then grab some popcorn because you’ve just bought yourself ringside seats to a prize fight. I’ve listened to engineers argue about the meanings of these terms until my eyes glaze over, and if I hear the ol’ “is a lake just a filesystem or a conceptual repository?” argument one more time I might. just… snap. But why so much confusion? It’s not the applications, it’s the labels. The idea are familiar enough to most people who work in data, it’s just that the terminology isn’t interpreted consistently enough or even agreed upon across the industry. The confusion isn’t about what each does, it’s often about what exactly the terms … Read more

Databricks vs Snowflake: Lakehouse vs Warehouse

Databricks vs Snowflake. For over a decade, since the cloud brought bricks and flakes, data stacks have been reorganized around two different centers of mass. Snowflake and Databricks both promise that your data can be centralized, governed, and made useful to many teams at once—but they grew up solving different problems, and that difference still shows up in what they are, how they behave, and what tradeoffs they impose. Snowflake is, in the plainest terms, a cloud data warehouse: a managed system built to store data and run SQL queries over it with high concurrency. Snowflake describes its compute as virtual warehouses, clusters of compute resources used to execute queries and other operations, and it emphasizes the separability of those … Read more

Snowflake: A Cloud Warehouse Blows Smoke

Snowflake may be a name most engineers rattle off when asked to name a data warehouse, but considering how often I encounter confusion about what Snowflake is, I think a word about what Snowflake isn’t is appropriate here. And because so much of Snowflake’s marketing reads like it was written by someone who’s spent so long in the cloud that everything comes out as fog, in order to get to the truth of Snowflake you’ve got to parse the jargon like Scrapy parses data. To take one tiny example, when Snowflake says it “brings together data storage, processing, and analytic solutions,” they appear to assign to themselves the agency of “brings” in a way that they don’t technically deserve, as … Read more

Data Analytics: An Overview of the Architecture

Ask ten developers what data analytics actually is, and you’ll get ten slightly different answers — each involving some combination of dashboards, SQL queries, and a vague promise of “insights.” What Is Data Analytics, Really? At its core, data analytics is the process of collecting, transforming, and interpreting data to support decision-making. That might sound abstract, but think of it as a pipeline with three distinct engineering challenges: A good analytics system automates all three. It bridges the gap between data in the wild (raw, messy, inconsistent) and data in context (structured, queryable, meaningful). Let’s go deeper… What Data Analytics Means To You Data analytics isn’t just for analysts anymore. Engineers now sit at the center of how data flows … Read more