Mini Projects to Learn ClickHouse: 5 Remote-Friendly Exercises to Build Your Portfolio

Hook: Make ClickHouse work for your remote portfolio — fast

Remote technical roles demand evidence: compact, meaningful projects that show you can design OLAP schemas, squeeze performance from distributed systems, and explain trade-offs to non-specialist hiring managers. If you’re a developer or SRE looking to prove analytical thinking and performance tuning skills, a set of focused ClickHouse mini projects is one of the fastest ways to show impact on your resume and GitHub.

Below are five remote-friendly ClickHouse exercises you can complete in days or weeks, with practical deliverables you can link in applications, interviews, and portfolios.

Why ClickHouse projects matter in 2026

ClickHouse has moved from niche analytics engine to mainstream OLAP contender. In January 2026 the company announced a major funding round that accelerated product and cloud investments.

“ClickHouse raised $400M led by Dragoneer at a $15B valuation,” — Dina Bass, Bloomberg (Jan 2026).

That growth means employers are increasingly using ClickHouse for high-throughput analytical workloads — real-time dashboards, cybersecurity analytics, feature analytics for ML, and event-driven observability. Practically, this creates demand for engineers who can:

• Design efficient OLAP schemas and partitioning strategies
• Optimize queries and materialized views for low-latency reads
• Benchmark and tune distributed storage (MergeTree family)
• Integrate ClickHouse into modern data stacks and CI/CD

How to use these mini projects

Each prompt below includes goals, recommended datasets, schema and query starter points, performance tuning tasks, deliverables for your portfolio, and optional stretch goals. You can run ClickHouse locally (Docker), on ClickHouse Cloud free tiers, or with a small instance in any cloud provider.

Commit everything: schema DDL, sample data ingestion scripts, benchmark results, and a clear README with a 2–3 minute demo video. Those deliverables make your projects interview-ready.

Project 1 — Real-Time Event Analytics (Streaming to OLAP)

Goal

Show you can ingest a high-volume event stream (simulated), store it efficiently in ClickHouse, and support sub-second dashboard queries with aggregations by minute/hour, user cohort, and geo.

Why this matters

Real-time event analytics is a common ClickHouse use case. Employers want engineers who understand trade-offs between ingestion latency, storage patterns, and read performance.

Datasets & tools

• Synthetic event generator (Python/Node) or public datasets: OpenWebLogs, GitHub Archive, or Kaggle event sets
• Kafka or simple file-based batch ingestion for a remote-friendly setup
• ClickHouse (Docker image) or ClickHouse Cloud trial

Starter schema

CREATE TABLE events
(
  event_time DateTime,
  user_id UInt64,
  event_type String,
  properties String,
  country_code LowCardinality(String)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (country_code, event_time, user_id);

Performance tasks

1. Measure ingestion throughput (events/sec) with clickhouse-client and your generator.
2. Experiment with ORDER BY and PRIMARY KEY choices — track query latency for top-N event-type counts.
3. Add TTL to drop old partitions (simulate retention) and measure storage savings.
4. Introduce a materialized view for hourly aggregates and compare query times with/without the view.

Deliverables

• README with architecture diagram and how to run the generator + ClickHouse
• DDL, ingestion scripts, and sample queries
• Benchmark table (events/sec, query latency percentiles)
• Short demo video or GIF showing the dashboard query executing in sub-second time

Stretch

Integrate with Grafana using the ClickHouse data source and create an alert based on query latency. Note the async remote onboarding/setup steps in your README.

Project 2 — Multi-dimensional Aggregates & Rollups

Goal

Demonstrate data modeling for OLAP: design schema for fast ad-hoc analytics on multiple dimensions (time, product, region), and implement rollups or projections for query acceleration.

Why this matters

Companies need analysts to run fast multi-dimensional queries. Your ability to model data and implement pre-aggregations shows domain-driven design and performance foresight.

Datasets & tools

• Public e-commerce datasets (e.g., Instacart, retail transactions) or synthetic sales data
• ClickHouse projections or materialized views

Starter schema

CREATE TABLE sales
(
  order_id UInt64,
  order_ts DateTime,
  product_id UInt32,
  category_id UInt16,
  price Float32,
  quantity UInt16,
  country LowCardinality(String)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(order_ts)
ORDER BY (category_id, product_id, order_ts);

Performance tasks

1. Run representative OLAP queries (top products by revenue, monthly retention by country) and capture latencies.
2. Create projections or materialized views for monthly/category rollups; compare query costs.
3. Test low-cardinality optimizations (LowCardinality type) and compression codecs (ZSTD levels).

Deliverables

• Query samples with explain/trace output and latency comparisons
• Before/after storage and CPU usage charts
• Short write-up of data-model decisions and trade-offs (e.g., projection vs. MV)

Stretch

Implement a dynamic rollup pipeline with Kafka + ClickHouse materialized views that builds aggregates as data arrives.

Project 3 — Join Optimization & Sharding Patterns

Goal

Show you can optimize complex joins and design sharding strategies for distributed ClickHouse clusters to minimize network and CPU cost.

Why this matters

Distributed joins and sharding are common hurdles in production OLAP. Recruiters look for engineers who can balance data locality and query performance.

Datasets & tools

• Two related datasets: users and events (or products and orders)
• Local multi-node ClickHouse cluster (Docker Compose) or ClickHouse Cloud with cluster mode

Starter schema & sample join

CREATE TABLE users (user_id UInt64, signup_ts DateTime, region String)
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/users', '{replica}')
PARTITION BY toYYYYMM(signup_ts)
ORDER BY user_id;

-- join example
SELECT u.region, countDistinct(e.user_id) AS active_users
FROM events e
JOIN users u USING (user_id)
WHERE e.event_time >= now() - INTERVAL 7 DAY
GROUP BY u.region;

Performance tasks

1. Compare join strategies: local joins using distributed tables vs. pre-joined denormalized tables.
2. Experiment with sharding keys to colocate related data (e.g., shard by user_id) and measure cross-shard traffic.
3. Use profile events (system.query_log, system.metrics) to capture CPU, network I/O, and memory usage.

Deliverables

• Cluster layout diagram and reasoning for shard keys
• Benchmark results showing CPU/network trade-offs for each join approach
• An action plan with recommended sharding pattern for a hypothetical product team

Stretch

Build a simple CI job (GitHub Actions) that deploys the cluster, runs joins, and posts benchmark results to a PR — demonstrates remote CI skills.

Project 4 — Time-Series & Compression Tuning

Goal

Prove you can optimize ClickHouse for dense time-series data: choose the right MergeTree engine, compression settings, and data granularities for cost-effective storage and fast reads.

Why this matters

Telemetry, monitoring, and observability systems often use ClickHouse for time-series due to its fast aggregation. Employers want engineers who can minimize storage cost without sacrificing query speed.

Datasets & tools

• Prometheus scraped metrics exported to ClickHouse, or synthetic sensor telemetry
• MergeTree variants: ReplacingMergeTree, VersionedCollapsingMergeTree if appropriate

Starter schema

CREATE TABLE metrics
(
  ts DateTime,
  metric_name LowCardinality(String),
  host_id UInt32,
  value Float64
)
ENGINE = ReplacingMergeTree()
PARTITION BY toYYYYMM(ts)
ORDER BY (metric_name, host_id, ts);

Performance tasks

1. Test different compression codecs (e.g., ZSTD:1 vs ZSTD:9) and measure storage and query latency.
2. Implement downsampling pipelines using materialized views with different aggregation windows and compare recall/latency trade-offs.
3. Benchmark point queries and range scans for different ORDER BY keys.

Deliverables

• Compression experiment matrix with storage/latency outcomes
• Downsampling strategy and code to rebuild a metric at three retention levels
• Short explanation of ReplacingMergeTree and when to use it

Stretch

Integrate ClickHouse with a lightweight anomaly detection model (e.g., rolling z-score) and produce alerts. Include an explanation of cost vs. accuracy.

Project 5 — Analytical ML Features Store with ClickHouse

Goal

Build a small features store in ClickHouse for fast analytical feature joins used in model training and validation. Showcase how ClickHouse can export features into training pipelines.

Why this matters

In 2026, data and ML teams increasingly use OLAP systems to compute features at scale. Demonstrating you can manage feature engineering workflows in ClickHouse is high-value.

Datasets & tools

• Events and labels dataset (e.g., churn labels or conversion events)
• Python for feature computations and export (pandas / polars + clickhouse-connect)

Starter schema & workflow

1. Create a feature table keyed by entity_id and snapshot_ts.
2. Compute windowed aggregations (7/30/90 day) and materialize them.
3. Export feature batches to CSV/Parquet for training or use clickhouse-driver directly.

CREATE TABLE features
(
  entity_id UInt64,
  snapshot_ts DateTime,
  feature_last_7d Float32,
  feature_last_30d Float32
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(snapshot_ts)
ORDER BY (entity_id, snapshot_ts);

Performance tasks

1. Show how incremental updates (upserts via CollapsingMergeTree or deduplication logic) keep features fresh.
2. Benchmark export times for training set creation and optimize using parallel exports or native Parquet writing.
3. Measure end-to-end latency from data arrival to feature availability.

Deliverables

• Feature table DDL and example SQL feature definitions
• Export pipeline scripts and timings
• Notes on feature drift and recomputation strategies

Stretch

Add a small batch of model training code that consumes features directly from ClickHouse and reports train/validation metrics.

How to present these projects in a remote-ready portfolio

Hiring managers need quick signals. For each project, include these items in your repo and README to maximize impact:

• One-line summary: Problem, approach, and outcome (e.g., “Reduced ad-hoc query latency from 2s to 150ms with projections and materialized views”).
• Architecture diagram: Data flow and components (ingestion, ClickHouse, dashboards, CI).
• Repro steps: Docker compose or ClickHouse Cloud setup script and a short runbook for remote reviewers.
• Benchmarks: Tables/plots of throughput, latency percentiles, CPU, and storage before/after tuning.
• Trade-offs: Short section explaining design choices and known limitations.
• Demo: 2-minute video or GIF for a fast review during async hiring.

Interview talking points & metrics recruiters care about

When you discuss these projects in interviews, highlight specific, measurable outcomes:

• Throughput: events/sec ingested
• Query latency: median, p95, p99 for representative queries
• Storage: GB reduced after compression/TLL/downsampling
• Cost estimate: node-hours or cloud cost comparison — use edge-oriented cost analysis where appropriate
• Operational incidents: how you monitored, alerted, and fixed regressions

2026 trends & what to expect next

As of 2026, ClickHouse adoption in production is accelerating. Expect hiring teams to value:

• Hybrid cloud and ClickHouse Cloud: Familiarity with managed services and hybrid networking.
• Embedding & analytics convergence: Growing patterns of combining embeddings into OLAP systems for similarity joins and feature stores.
• Cost-aware architectures: Engineers who can reduce TCO through compression, TTLs, and rollups.
• Automation & observability: CI for schema migrations, reproducible benchmarking, and automated alerting for query regressions.

Practical checklist: Getting started this week (remote-friendly)

1. Pick one project and fork the starter repo template (create a template with Docker + sample data).
2. Spend one afternoon getting ClickHouse running (Docker or free ClickHouse Cloud).
3. Build the schema and import a small sample (10k–100k rows) to validate queries.
4. Automate a simple benchmark and record results in a CSV for reproducibility.
5. Create a README.md that describes the problem, approach, and results. Add a short demo recording.

Real-world example

At a fintech startup (anonymized), a single engineer used a ClickHouse projection to pre-aggregate transaction-level data, cutting dashboard query time from 4s to 0.2s and reducing daily CPU consumption by 38%. That change was the core talking point in her next job interview and directly led to a senior role offer. Concrete outcomes like that are what hiring teams want to see in remote hires.

Final tips for standing out

• Be explicit about remote constraints: show scripts that reproduce results on a laptop or small cloud instance.
• Use CI to run benchmarks on PRs — shows discipline and async collaboration skills.
• Document trade-offs and cost estimates — hiring managers appreciate pragmatic thinking.
• Link a 2-minute demo GIF at the top of the README so recruiters can see results quickly.

Call to action

Ready to build one of these projects and get feedback? Start with the Real-Time Event Analytics prompt this week. Clone the companion template (Docker + sample generator), run a baseline benchmark, and open a public GitHub repo with your README and demo. When you’re ready, post the repo link on your LinkedIn and tag it as a portfolio project — I’ll review one submission per week and share actionable tuning advice.

Take the first step: choose a project, set up ClickHouse in Docker, and push your first commit today — small, measurable wins on your portfolio pay off rapidly in remote hiring.

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