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Zero-to-Interview: Build a ClickHouse Analytics Project for Your Data Science Portfolio

UUnknown

2026-02-16

10 min read

Build a production-style ClickHouse OLAP pipeline and turn it into an interview-ready case study with code, dashboards, and talking points.

Hook: Turn confusion into an interview-ready analytics case study

Feeling lost choosing a portfolio project that hiring managers will respect? You don’t need a giant ML model or months of data munging—what employers want in 2026 is clear: real-world, scalable analytics pipelines that show you can design systems, move data reliably, and extract business insights fast. This guide walks you step-by-step from zero to interview-ready by building a production-style OLAP analytics pipeline on ClickHouse, plus the GitHub structure and talking points that win interviews.

Why ClickHouse for a data science portfolio in 2026?

ClickHouse has become a dominant OLAP engine for high-throughput analytics. In late 2025 it raised a major funding round (Dragoneer-led $400M at a reported $15B valuation), reflecting rapid enterprise adoption and a growing cloud ecosystem. For portfolio projects, ClickHouse offers three decisive advantages:

Performance: sub-second aggregations on billions of rows—great to demonstrate engineering chops.
Real-world tooling: connectors (Kafka, Airbyte), SQL extensions, materialized views, and cloud/self-hosted options mirror production stacks.
Scalability story: you can show data modeling, partitioning, and cost/perf trade-offs—exactly the conversation interviewers want.

Project overview — What you’ll deliver

By the end you will have:

A GitHub repo with code, ETL scripts, and README that frames the project as a case study.
A ClickHouse schema and ingestion pipeline (streaming or batch).
Materialized views and pre-aggregations for fast OLAP queries.
A dashboard (Metabase / Apache Superset / Grafana) showing core KPIs.
An interview-ready narrative, resume bullets, and talking points including metrics showing performance and cost trade-offs.

High-level architecture (choose one to implement)

Pick either Streaming (real-time) or Batch for your portfolio; both are interview-worthy. Here are two minimal architectures:

Streaming (recommended to show real-time skills)

Source: simulated events (Python script) or public event stream
Transport: Kafka (or ClickHouse native Kafka engine)
Ingest: ClickHouse Kafka engine + materialized views to MergeTree
Serve: ClickHouse SQL queries powering dashboards

Batch (simpler, faster to build)

Source: CSV/Parquet files (public dataset)
Transport: Airbyte / simple Python loader
Ingest: clickhouse-client import or HTTP insert
Serve: scheduled materialized views / pre-aggregations

Step 1 — Choose a dataset and business question

Pick a dataset that supports clear KPIs and segmentation. Examples:

E-commerce events (page views, add-to-cart, purchases)
Public transit GPS + ridership (time-series, geospatial hooks)
Ad impressions & clicks (high-cardinality user IDs)

Define 3–5 core questions. Example for e-commerce:

Daily revenue, conversion rate by traffic source
Top products by lifetime value and velocity
Real-time anomaly detection for traffic spikes

Step 2 — Repo layout & deliverables (GitHub-ready)

Structure your repo so reviewers can reproduce everything in 30–60 minutes:

clickhouse-analytics-project/
├─ README.md
├─ data/                # sample CSV / scripts to generate data
├─ infra/               # docker-compose or ClickHouse Docker setup
├─ ingestion/           # Kafka producer, Airbyte config, Python loaders
├─ schema/              # DDL: tables, materialized views
├─ dashboards/          # Metabase JSON export or Superset objects
├─ notebooks/           # analysis notebooks (Jupyter / nteract)
└─ docs/                # Case study + interview talking points

Publish with a clear README that contains a one-paragraph summary, setup steps, and a short “Key results” section with metrics.

Step 3 — Setup ClickHouse (quick local or cloud)

Options in 2026:

Local: Docker with official ClickHouse image (fastest for demos)
Managed: ClickHouse Cloud or other managed providers (realistic production story)
Cluster: single-node is fine for portfolio; note scalability discussion in your case study

Example Docker Compose snippet (put in infra/docker-compose.yml):

version: '3.7'
services:
  clickhouse:
    image: clickhouse/clickhouse-server:latest
    ports:
      - "8123:8123"
      - "9000:9000"
    volumes:
      - ./data/clickhouse:/var/lib/clickhouse

Step 4 — Schema design for OLAP (fewer surprises in interviews)

Designing schema in ClickHouse is about read performance and compression. Key decisions to document:

Engine: Use MergeTree variants for large tables (ReplacingMergeTree / SummingMergeTree when appropriate)
Primary key / ORDER BY: Determines data locality. For event tables choose (event_date, user_id) or (event_date, event_type) depending on query patterns.
Partitioning: Partition by month/day when data grows fast to enable efficient TTL and DROP PARTITION.
Compression & types: Prefer low-cardinality enums, Nullable only if needed, Date/DateTime64 for timestamps.

Sample DDL (schema/create_tables.sql):

CREATE TABLE events_raw (
  event_date Date,
  event_time DateTime64(3),
  user_id String,
  session_id String,
  event_type String,
  properties String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_date, user_id);

Step 5 — Ingestion: streaming vs batch patterns

Streaming with Kafka

Create a Kafka topic for events.
Use ClickHouse's Kafka table engine to read messages and a materialized view to write to MergeTree for durability.
Benefits: shows real-time pipeline knowledge and monitoring (consumer lag, offsets).

Materialized view example:

CREATE TABLE events_buffer (
  -- same columns as raw
) ENGINE = Kafka SETTINGS kafka_broker_list = 'kafka:9092', kafka_topic_list='events', kafka_group_name='ch_group', kafka_format='JSONEachRow';

CREATE MATERIALIZED VIEW events_mv TO events_raw AS
SELECT
  toDate(event_time) AS event_date,
  event_time,
  user_id,
  session_id,
  event_type,
  properties
FROM events_buffer;

Batch loading

Use clickhouse-client or HTTP insert for CSV / Parquet files.
Airbyte can be added to the stack to simulate real ETL connectors.

Step 6 — Pre-aggregation & materialized views

Pre-aggregations are essential in OLAP. Create materialized views that maintain daily aggregates (e.g., revenue_by_day, events_by_type) to keep dashboards snappy.

CREATE MATERIALIZED VIEW daily_metrics
ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(day)
ORDER BY day
AS
SELECT
  toDate(event_time) AS day,
  event_type,
  count() AS cnt,
  sum(if(event_type='purchase', toFloat64(JSONExtractFloat(properties,'price')), 0)) AS revenue
FROM events_raw
GROUP BY day, event_type;

In your case study, report the latency improvement (e.g., ad-hoc query 20s → 150ms after pre-agg).

Step 7 — Query patterns and performance tips

Use ORDER BY to match common GROUP BYs. ClickHouse reads ranges efficiently when ORDER BY is aligned with query filters.
Leverage materialized views for heavy aggregation patterns.
Profile queries with system.query_log and system.metrics to show concrete numbers.
Consider sampling for exploratory analysis on very large tables.

Step 8 — Visualization & dashboarding

Choose a dashboard (Metabase / Apache Superset / Grafana) tool that supports ClickHouse (Metabase is easiest to ship and screenshot).

Create 4–6 dashboard cards: Overview KPIs, Funnel, Top products, Time series, Anomaly detection.
Export dashboard JSON to the repo and include screenshots in the README.

Step 9 — Tests, monitoring, and cost notes

To make the project feel production-ready, add:

Unit tests for ingestion transformations (pytest + test data)
Smoke tests for queries using sample datasets
Basic monitoring notes: monitor disk usage, merges, replica lag (if clustered), and Kafka lag
Cost estimate: run a short section in README comparing local vs ClickHouse Cloud for a monthly budget estimate

Step 10 — Document a clear case study narrative

Structure your project’s case study in the repo’s docs/ folder and README with this template:

Summary (2–3 lines): Business problem andTL;DR outcome (e.g., “Built a ClickHouse OLAP pipeline for e-commerce events; reduced ad-hoc query time from 20s to 150ms; cost approximation $X/month.”)
Problem: Data scale, KPIs, stakeholders.
Approach: Architecture choices, ingestion strategy, schema decisions.
Implementation: Key code links, DDL, materialized views, dashboard screenshots.
Results: Query latency, dataset size, ingest throughput, sample SQL run times.
Trade-offs & next steps: What you would change with more time or budget.

Interview talking points — what to say (and how to show impact)

Use STAR-based talking points. Keep each verbal bullet 15–30 seconds and one slide/screenshot per point:

Situation: “We had 100M events/month and dashboards taking 20s to respond.”
Task: “Deliver sub-second queries for product analytics while supporting near-real-time ingestion.”
Action: “Designed an event schema with MergeTree partitioning, implemented Kafka ingestion, and added materialized view pre-aggregations.”
Result: “Reduced median query latency from 20s to 150ms and sustained ingestion at 5k events/sec in the local test.”

Additional technical bullets to rehearse:

Why you chose ORDER BY and partition keys (explain query alignment).
How ClickHouse handles merges and why TTL/partitioning matters for storage hygiene.
How you validated correctness (unit tests and checksum comparisons between raw and aggregated tables).
Failure scenarios you considered (Kafka backpressure, disk full) and mitigation plans.

Resume & LinkedIn-ready bullets

Concise bullets that fit a resume:

Built a ClickHouse-based OLAP pipeline ingesting 100M+ events/month with Kafka and materialized views; reduced ad-hoc query latency from 20s to 150ms.
Designed MergeTree schema and partition strategy to optimize read-heavy analytics and lower storage costs via TTL.
Published GitHub repo (link) with reproducible Docker, ingestion scripts, notebooks, and dashboard exports.

Code & reproducibility tips

Make it easy for reviewers to run your project—document these steps:

git clone https://github.com/your-username/clickhouse-analytics-project
cd infra && docker-compose up -d
Run ingestion script: python ingestion/send_events.py --rate 100
Open dashboard: http://localhost:3000 (Metabase port)

Include sample data and a fast-mode flag so reviewers can see results without waiting for long ingests.

2026 trends to mention in interviews

We advise referencing recent ecosystem trends to show domain awareness:

ClickHouse’s enterprise momentum—large funding rounds and product expansion signal that OLAP engines are central to analytics in 2026.
Shift toward hybrid workloads: ClickHouse is increasingly used for both analytics and low-latency operational queries.
Real-time analytics expectations: teams want streaming-first pipelines integrated with observability and LLM-powered anomaly explainability.

Use these to frame why you chose ClickHouse and the real-world relevance of your design choices.

Sample metrics and how to measure them (include in your repo)

Ingest throughput: events/sec measured by your producer script.
Query latency: median and p95 using system.query_log.
Storage: data size per month; compression ratio.
Cost proxy: estimated vCPU / disk needed and approximate monthly cost for ClickHouse Cloud.

Common pitfalls and interview defenses

“I used a single node”—defend with: reproducibility, cost constraints, and how you’d scale (replication, sharding, distributed DDL).
“No authentication in the demo”—explain how to lock down ClickHouse (users, profiles, network) for production.
“I used simplified JSON properties”—explain trade-offs and propose normalized tables or materialized columns for production readiness.

Extras to polish your case study

Include a short video (2–4 minutes) demoing setup and dashboards.
Add a benchmark notebook showing before/after queries and resource usage.
Create a lightweight architecture diagram image and put it in docs/.

Tip: Interviewers often care more about the trade-offs you considered than the perfect architecture. Document the why as much as the how.

Putting it all together — a 2-hour sprint plan

If you want a minimal viable case study fast, follow this sprint:

Hour 0–0.5: Repo scaffold + Docker Compose for ClickHouse.
Hour 0.5–1: Load sample dataset (batch) and create MergeTree table.
Hour 1–1.5: Add a materialized view for 1–2 core KPIs and run queries.
Hour 1.5–2: Add a Metabase dashboard screenshot and write README summary + talking points.

Deliver that as a portfolio entry and iterate for depth later (streaming, tests, monitoring).

Final checklist before you publish

README with TL;DR results and setup steps
Screenshots and a short demo video
DDL files and ingestion scripts in top-level folders
Notebook with analysis and benchmark numbers
Docs/case-study narrative and interview talking points

Wrapping up — Make your work speak for you

In 2026, employers prefer evidence: reproducible projects that show system-level thinking and measurable impact. A ClickHouse OLAP project demonstrates data engineering, performance tuning, and product-minded analytics in one piece of work. Ship a clean GitHub repo, document the trade-offs, and practice the concise talking points above—then you’ll convert portfolio views into interview calls.

Call to action

Ready to build? Clone a starter repo template and follow the 2-hour sprint above. Publish your repo link in the notes below and I’ll review your README and talking points—leave your GitHub URL in the project comments to get feedback and a short checklist to tighten your interview narrative.

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