Hellmar Becker’s Blog

Many database systems offer the ability to evaluate an arbitrary string as SQL and run the resulting query - EXECUTE IMMEDIATE in Oracle, EXECUTE in Postgres.

Taking the latest value of a timeseries is, from ClickHouse’s point of view, just a form of aggregation.

Refreshable Materialized Views (RMV) are a way to define scheduled data transformations in ClickHouse.

Did you know you can send files, like log files, directly to an endpoint in ClickHouse?

One of the most flexible ways to get data from Kafka into ClickHouse is the Kafka Table Engine.

Intro A while ago, I wrote this blog about quantiles in Druid.

Asynchronous inserts and buffer tables ClickHouse is known for being able to handle large amounts of data that are inserted in near real time.

Here’s another of my learnings from playing with ClickHouse.

The Problem In text search, you often want to be agnostic of diacritics - those appendages to Latin script letters, like umlaut markers, accents, cedilles, and other modifiers.

This is a question I encountered recently.

ClickHouse has a lot of built-in functionality that is just there for analysts and data engineers to discover.

Let’s pick up one of my earlier blogs.

In my previous post, I redefined the dot product of two vectors.

After a brief hiatus, the Data Cookbook Elf is back!

Up until Druid 30, data in a Druid segment was always sorted by time.

Imply’s Polaris service has a new feature: Lookups.

In the previous episode of the Druid Data Cookbook, I showed how to extract and process all elements out of a nested array of objects in Druid.

A common problem in data that we ingest into Druid is that we may encounter arrays of nested objects, and we want to reason about specific fields within those objects.

Let’s look at a neat new feature in Druid 30 that makes using the API more flexible.

In the latest versions of Druid, handling of logical expressions, and in particular of NULL (unknown) values, has been changed to match the SQL standard.