Your Analytics Are Slow Because You're Using a Swiss Army Knife for a Scalpel's Job.
In the world of data analytics, speed is not just a feature; it is the feature. When your product managers, analysts, and customers have to wait minutes for a dashboard to load, they don't just get frustrated—they stop asking questions. Your data, your most valuable asset, becomes inert. ClickHouse was built to solve this problem. It is an open-source, columnar database designed from the ground up for one thing: blazingly fast Online Analytical Processing (OLAP) queries on massive datasets.
But this incredible speed is the result of a highly specialized architecture. An engineer who approaches ClickHouse with the mindset of a traditional row-based, transactional database (like PostgreSQL or MySQL) will fail, spectacularly. They will design schemas that are impossible to query efficiently, write queries that don't leverage ClickHouse's parallel processing capabilities, and build a system that is both slow and expensive—negating the very reason for choosing it.
An engineer who can write a `SELECT` statement is not a ClickHouse expert. An expert understands the MergeTree family of table engines. They know how to choose a primary key and an order key to optimize data skipping. They can design and manage materialized views for real-time aggregation. They treat schema design as the primary lever for performance. This playbook explains how Axiom Cortex finds the rare engineers who possess this deep, specialized expertise.
Traditional Vetting and Vendor Limitations
A nearshore vendor sees "SQL" and "Databases" on a résumé and assumes proficiency in ClickHouse is a simple extension. The interview process rarely, if ever, tests for the specific, non-obvious skills required to operate a high-performance analytical database.
The predictable and painful results of this flawed vetting are common:
- The "Full Scan" Catastrophe: A query that should take milliseconds takes minutes because the developer failed to design the table's `ORDER BY` key correctly, forcing ClickHouse to perform a full scan over billions of rows instead of using its sparse primary index to skip massive blocks of data.
- Join Performance Hell: A developer, used to the flexibility of joins in a transactional database, writes a query that joins two massive distributed tables, triggering a huge amount of data shuffling across the network and bringing the cluster to its knees. They don't understand that in ClickHouse, you design your schema to avoid joins whenever possible.
- Ignoring Materialized Views: The team builds a complex, slow, and expensive ETL job to pre-aggregate data for a dashboard, completely unaware that ClickHouse can do this automatically and in real-time using materialized views.
- Data Type Inefficiency: The developer uses generic types like `String` for data that has low cardinality, missing the opportunity to use the `LowCardinality` data type to dramatically reduce memory usage and improve query speed.
How Axiom Cortex Evaluates ClickHouse Developers
Axiom Cortex is designed to find engineers who think in columns, partitions, and aggregations. We test for the practical skills and architectural mindset essential for building world-class analytical systems with ClickHouse. We evaluate candidates across four critical dimensions.
Dimension 1: Columnar Data Modeling and Schema Design
This is the single most important skill for a ClickHouse developer. Performance is designed at the schema level. This dimension tests a candidate's ability to model data for analytical workloads.
We provide a business problem (e.g., "design a system to analyze web analytics data") and evaluate their ability to:
- Choose the Right MergeTree Engine: Can they explain the difference between a simple `MergeTree`, a `ReplacingMergeTree`, and a `SummingMergeTree`?
- Design the Primary and Order Key: This is critical. Can they choose a primary key and an `ORDER BY` key that aligns with the most common query patterns to maximize data skipping?
- Denormalize for Performance: A high-scoring candidate will immediately talk about denormalizing the data to create a single, wide fact table to avoid expensive joins.
- Use Specialized Data Types: Do they know when to use data types like `LowCardinality`, `Enum`, or `AggregateFunction` to optimize performance and storage?
Dimension 2: Query Performance and Optimization
This dimension tests a candidate's ability to write queries that harness ClickHouse's power and to debug the ones that don't.
We present a slow query and evaluate if they can:
- Analyze an `EXPLAIN` Plan: Can they read the query plan to understand how ClickHouse is reading data and identify which stages are the most expensive?
- Use Aggregate Functions and Combinators: Are they proficient in using ClickHouse's rich library of aggregate functions and combinators (like `...If` and `...Array`) to perform complex analysis efficiently?
- Understand Distributed Queries: Can they explain how a query is executed on a distributed table across a cluster?
Dimension 3: Data Ingestion and Lifecycle Management
An analytical database is only as good as the data in it. This dimension tests a candidate's knowledge of how to get data into ClickHouse and manage it effectively.
We evaluate their knowledge of:
- Data Ingestion Patterns: Are they familiar with different methods for ingesting data, such as inserting in batches or streaming data from Kafka?
- Materialized Views and Aggregates: Can they design a materialized view to power a real-time dashboard, providing instant aggregations over a massive raw dataset?
- Data Retention (TTL): Do they know how to use ClickHouse's `TTL` (Time to Live) feature to automatically manage data retention and control storage costs?
From a Slow Data Warehouse to a Real-Time Analytics Engine
When you staff your data platform team with engineers who have passed the ClickHouse Axiom Cortex assessment, you are investing in a team that can build truly interactive analytics products.
A SaaS observability company was struggling to provide a fast log search feature for their customers. Their existing Elasticsearch-based system was expensive and slow for analytical queries. Using the Nearshore IT Co-Pilot, we assembled a pod of two elite nearshore ClickHouse developers.
In their first quarter, this team:
- Built a New Analytics Backend on ClickHouse: They designed a new schema optimized for log data and built a pipeline to stream data into a ClickHouse cluster.
- Achieved Sub-Second Query Latency: By correctly designing the table structure and using materialized views, they were able to provide p99 query latencies of under 500 milliseconds on a multi-terabyte dataset.
The result was a transformative new feature for the company. Their customers could now perform complex analytical queries on their log data in real-time, giving them a significant competitive advantage.
What This Changes for CTOs and CIOs
Using Axiom Cortex to hire for ClickHouse competency is not about finding a generic SQL developer. It is about insourcing the specialized discipline of high-performance analytical database engineering. It is a strategic move to build a data platform that can provide insights at the speed of thought, not the speed of a batch job.