Nov 17, 2025 · 10 min read · In the world of data management, you'll often hear the terms "data lake" and "data warehouse" used. While they both store data, they serve very different purposes and are built on different principles. Understanding the distinction is crucial for mak...
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Jul 29, 2025 · 2 min read · 🚨 The Challenge:We had to replace millions of rows daily, covering data from the past 60 days, and run aggregations on top. Initially, we explored: ✅ AggregatingMergeTree: Worked well only with incremental data, but not suited for full data replace...
Join discussionJul 28, 2025 · 5 min read · In today's digital-first economy, data is more than a resource—it's the engine behind every strategic decision, operational improvement, and AI-driven product. But the sheer volume, velocity, and variety of data produced by modern enterprises can ove...
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Jun 29, 2025 · 3 min read · Choosing a cloud data warehouse in 2025 isn't just about picking between Redshift, BigQuery, or Snowflake anymore. It’s a high-stakes decision with a new lineup of challengers shaking up the modern data stack. This guide helps you cut through the noi...
Join discussionJun 23, 2025 · 7 min read · When you create a data warehouse is not enough attention is paid to cleaning the incoming information into it. Apparently, it is believed that the larger the storage, the better. This is a surefire way to turn your data warehouse into a garbage dump....
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May 28, 2025 · 3 min read · At my day job, I’ve been working with Apache Iceberg over the last 9 months or so, and upon hearing of yet another open table format - Ducklake - I was interested to dig in. Here is my reaction. DuckDB - First of all, I’m a fan of duckdb, it’s really...
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May 9, 2025 · 5 min read · I know what some of you are probably thinking: "Oh great, another article about DuckDB." And honestly, I get it. A few months ago, I was in the same boat. It felt like DuckDB was following me around—blog posts, tweets, random mentions. It was everywh...
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Apr 17, 2025 · 6 min read · One can easily assume that data lakes are the natural evolution of the old, clunky data warehouses. Another can claim that the difference is all about your workload; that is, if it is more about machine learning, then you would be better with a data ...
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