This week, while the coffee is slow and the world is loud, I am quietly enjoying a ClickHouse charming Dictionary structure, watching a billion rows find their meaning in a fraction of a second.

In analytical databases, JOINs are often the silent killer of query performance. Today I was diving deep into the different Clickhouse JOIN algorithms and want to share with you my summary.

These days, I am having a serious columnar affair with my new friend, ClickHouse. Since I have some experience with quite a few other data stores, I notice new features that I once wished were present. Today, I will share with you my reflection on some interesting Clickhouse data types that have grabbed my attention.

This blog post will solve a practical “headache” of cross-cloud MySQL configuration audit.

We use both ClickHouse and Databricks for different data analytics use cases. A recurring question is often asked: when to use each platform. I’ve created the decision tree below to help developers choose, and would be happy to hear your thoughts.

When all the images disappeared from my blog, a small thought crept into my mind that moving away from stable blogging SaaS platforms might not have been the smartest idea.

The moment you decide your relational database can’t keep up with your ambitions: wait no more and look into the in-memory data stores world. This one has creatures of all shapes and behaviours.

Yesterday, I discovered a delightful trick for conjuring log-based metrics into a GCP Dashboard. And naturally, I’d love to share this strange little magic with you.

We recently had a late-night memory troubleshooting session on MySQL in the GCP cloud.I am sharing with you my MySQL learning outcomes and review of GCP default Cloud MySQL configuration related to performance and memory management.I will…