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Querying billions of weather records and getting results in under 200 milliseconds isn’t theory; it’s what real-time analytics solutions provide. Processing streaming IoT data from thousands of sensors while delivering real-time dashboards with no lag is what certain business domains need. That’s what you’ll learn at the end of this guide through building a ClickHouse-modeled analytics use case.
You’ll learn how to land data in ClickHouse that is optimized for real-time data applications, going from basic ingestion to advanced techniques like statistical sampling, pre-aggregation strategies, and multi-level optimization. I’ve included battle-tested practices from Rill’s years of implementing real-time analytics for customers processing everything from financial transactions and programmatic advertising to IoT telemetry.
This article is for data engineers and practitioners who want to build analytics that deliver sub-second query responses, and who want to unlock ClickHouse’s full potential for real-time analytics demands. By the end, you’ll have a playbook for ClickHouse data modeling plus a working example that ingests NOAA weather data from S3 and visualizes it with a single configuration file.
Why ClickHouse If you haven’t heard of ClickHouse or are wondering why it’s becoming the go-to choice for real-time analytics, here’s what sets it apart from traditional data warehouses. ClickHouse achieves blazingly fast analytical performance through column-oriented storage that reads only relevant data, advanced compression (LZ4/ZSTD), and vectorized query execution that maximizes CPU capabilities. Its sparse primary indexing with data skipping eliminates irrelevant data blocks, while the C++ implementation avoids JVM overhead for bare-metal performance. These innovations enable sub-second query responses on billions of rows, performance that would take minutes or hours in traditional data warehouses. Storage efficiency has a direct impact on both cost and speed at scale, making ClickHouse the ideal foundation for the real-time analytics modeling strategies covered in this article.
Data Flow for Real-time Analytics
Before we see a concrete example of modeling data with ClickHouse, specifically for real-time and online analytical processing (OLAP) cubes, it’s important to understand the flow of data, its trade-offs, and payoffs. Where Does Data Come From, and Where Does It Go?
Data Flow is Knowing the Requirements
Data flows from sources to analytics. In the simplest terms, we have sources of data, a transformation with aggregations, and the visualization. Most often, the data should travel from source to visualization as quickly as possible and respond fast to queries.
Most data flow modeling is handled in the transformation phase. Connecting to a source, whether it is an S3 or R2 bucket, a relational database like Postgres or others, or visualization on an analytical tool. We need to aggregate and combine the data to extract business insights out of masses of information to answer the questions our business needs to answer.
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