ClickHouse Education Program Overview

ClickHouse Training at ChistaDATA University is engineered as a competency ladder rather than a collection of unrelated courses. Each learner enters at a level determined by a free technical assessment, then progresses through a defined sequence of modules, labs and graded assessments. The curriculum is versioned against ClickHouse releases, so every ClickHouse Education cohort works with current syntax, current table engines and current cluster topologies rather than deprecated patterns.

The programme is organised into three core skill levels — Beginner, Intermediate and Advanced — plus two specialist programs that sit on top of the ladder: ClickHouse Troubleshooting and Advanced Analytics in ClickHouse. Executive briefings for CTOs and CXOs run in parallel so that technical adoption and business sponsorship advance together.

Table of Contents

• ClickHouse Education Program Overview
• Curriculum Framework and Learning Paths
• ClickHouse Architecture Taught Across the Curriculum
• Program 1 — Beginner ClickHouse Training (CHT-100)
• Program 2 — Intermediate ClickHouse Training (CHT-200)
• Program 3 — Advanced ClickHouse Training (CHT-300)
• Program 4 — ClickHouse Troubleshooting (CHT-400)
• Program 5 — Advanced Analytics in ClickHouse (CHT-500)
• Executive Track for CTOs and CXOs (CHT-900)
• Competency Matrix and Certification Ladder
• Delivery Models, Lab Environment and Corporate Training
• University and Academic Partnership Programs
• Engagement Process and Contracting
• ClickHouse Training FAQ

Curriculum Framework and Learning Paths

Figure 1 shows how the five ClickHouse Training programs relate to each other. Beginner, Intermediate and Advanced form the vertical spine of the ClickHouse Education curriculum. The Troubleshooting and Advanced Analytics programs branch from the Advanced tier and can also be delivered standalone to teams that already run ClickHouse in production.

Every ClickHouse Training program listed above is available as instructor-led virtual delivery, on-site workshop, or self-paced ClickHouse Education with graded labs.

ClickHouse Architecture Taught Across the Curriculum

Effective ClickHouse Training has to be grounded in the engine’s actual execution model. Learners are introduced to the architecture in the Beginner program at a conceptual level, revisit it in the Intermediate program from a schema-design perspective, and dissect it at source-code level in the Advanced program. Figure 2 is the reference model used throughout the ClickHouse Education curriculum.

MergeTree Write Path and Part Lifecycle

The single most common cause of production incidents in ClickHouse is a misunderstanding of how inserts become parts and how background merges consume them. This lifecycle is taught in the Intermediate program and forms the diagnostic backbone of the ClickHouse Troubleshooting program.

Program 1 — Beginner ClickHouse Training (CHT-100)

The Beginner tier of ClickHouse Training answers a single question rigorously: why is ClickHouse fast, and how do I use that speed correctly? Rather than starting with syntax, the program starts with the physics of columnar storage — compression ratios, sequential I/O, granule scanning and vectorised aggregation — then builds the SQL surface on top of that mental model. By the end of this ClickHouse Education track, a learner can stand up a single-node instance, model a first fact table, load hundreds of millions of rows and explain the query plan.

CHT-100 Module Breakdown

Representative Lab: Primary Key Selection and Granule Skipping

Beginner ClickHouse Training makes the cost of a bad sorting key measurable rather than theoretical. Learners create two tables over identical data and compare the number of granules read.

-- Variant A: sorting key does not match the access pattern
CREATE TABLE lab.events_a
(
    event_time  DateTime,
    tenant_id   UInt32,
    event_type  LowCardinality(String),
    user_id     UInt64,
    revenue     Decimal(18, 4)
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_time);

-- Variant B: sorting key leads with the filtered, low-cardinality column
CREATE TABLE lab.events_b
(
    event_time  DateTime,
    tenant_id   UInt32,
    event_type  LowCardinality(String),
    user_id     UInt64,
    revenue     Decimal(18, 4)
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(event_time)
ORDER BY (tenant_id, event_type, event_time);

-- The learner runs the same predicate against both and compares plans
EXPLAIN indexes = 1
SELECT event_type, sum(revenue)
FROM lab.events_b
WHERE tenant_id = 4711
  AND event_time >= now() - INTERVAL 7 DAY
GROUP BY event_type;

The measured outcome — typically one to two orders of magnitude fewer granules read in Variant B — anchors every later discussion of schema design in the Intermediate and Advanced ClickHouse Training programs.

CHT-100 Deliverables

• A documented single-node ClickHouse deployment with tuned users, profiles and quotas.
• A normalised-to-denormalised modelling exercise with justification for the chosen ORDER BY and PARTITION BY.
• Ten completed labs with graded query-performance evidence taken from system.query_log.
• A capstone dashboard answering ten business questions over a 100+ million row dataset.
• ChistaDATA Certified ClickHouse Professional (Associate) examination eligibility.

Program 2 — Intermediate ClickHouse Training (CHT-200)

Intermediate ClickHouse Training moves from single-table querying to designing and operating a real ingestion platform. The dominant theme is write-path engineering: how data arrives, how it is deduplicated, how aggregates are maintained incrementally, and how schema decisions made in week one determine query latency two years later. This is the tier most corporate ClickHouse Education engagements begin with, because it maps directly to the work a delivery team is already attempting.

CHT-200 Module Breakdown

Reference Architecture Taught in CHT-200

The Intermediate ClickHouse Training capstone requires learners to build and defend the reference pipeline in Figure 4 end to end, including failure injection at each stage.

Representative Lab: Incremental Rollups With AggregatingMergeTree

-- Target rollup table holding aggregate states, not final values
CREATE TABLE analytics.hourly_tenant_stats
(
    hour          DateTime,
    tenant_id     UInt32,
    events        AggregateFunction(sum, UInt64),
    unique_users  AggregateFunction(uniqCombined64, UInt64),
    p95_latency   AggregateFunction(quantilesTDigest(0.95), Float32)
)
ENGINE = AggregatingMergeTree
PARTITION BY toYYYYMM(hour)
ORDER BY (tenant_id, hour);

-- Materialized view maintains the rollup on every insert into the raw table
CREATE MATERIALIZED VIEW analytics.hourly_tenant_stats_mv
TO analytics.hourly_tenant_stats AS
SELECT
    toStartOfHour(event_time)            AS hour,
    tenant_id,
    sumState(toUInt64(1))                AS events,
    uniqCombined64State(user_id)         AS unique_users,
    quantilesTDigestState(0.95)(latency) AS p95_latency
FROM analytics.raw_events
GROUP BY hour, tenant_id;

-- Read path merges the states at query time
SELECT
    tenant_id,
    sumMerge(events)                     AS events,
    uniqCombined64Merge(unique_users)     AS users,
    quantilesTDigestMerge(0.95)(p95_latency)[1] AS p95_ms
FROM analytics.hourly_tenant_stats
WHERE hour >= today() - 30
GROUP BY tenant_id
ORDER BY events DESC
LIMIT 20;

CHT-200 Deliverables

• A production-shaped streaming pipeline from Kafka to a replicated fact table with idempotent replay.
• A schema design document reviewed by a ChistaDATA principal engineer, including rejected alternatives.
• A rollup layer that reduces dashboard query cost by a measured, documented factor.
• A Grafana observability pack with alerting thresholds for part counts, merge backlog and insert latency.

Program 3 — Advanced ClickHouse Training (CHT-300)

Advanced ClickHouse Training is an internals and distributed-systems program. Learners work on a multi-shard, multi-replica cluster with ClickHouse Keeper, drive it into degraded states deliberately, and recover it. The syllabus reaches into the source tree: LSM-style part management, vectorised operator pipelines, allocator behaviour, and the replication protocol. Teams that complete this ClickHouse Education tier are equipped to own capacity planning, upgrade strategy and incident command for a ClickHouse fleet.

CHT-300 Module Breakdown

Cluster Topology Built During Advanced ClickHouse Training

Failure Drills in CHT-300

• Keeper quorum loss: stop two of three Keeper nodes, observe read-only degradation, restore quorum, verify replication queue drain.
• Replica divergence: corrupt a part on one replica, detect via CHECK TABLE and system.parts checksums, restore with SYSTEM RESTORE REPLICA.
• Merge starvation: saturate the background pool, watch part counts climb, and apply the correct remediation order.
• Rolling upgrade: upgrade across a minor version boundary with mixed-version replicas and validate wire compatibility.
• Region failover: promote a cross-region replica set and measure achieved RPO against the design target.

Program 4 — ClickHouse Troubleshooting (CHT-400)

ClickHouse Troubleshooting is the program built directly from ChistaDATA’s break-fix engineering practice. Every scenario in the syllabus is derived from a real production incident: the cluster is pre-broken, the learner receives only the symptoms a monitoring alert would surface, and they must reach root cause using system tables, logs and profiling — not guesswork. This ClickHouse Training program is deliberately methodology-first, because the failure modes change between releases while the diagnostic discipline does not.

Diagnostic Methodology

Incident Catalogue Covered in CHT-400

Representative Diagnostic Queries Taught in CHT-400

-- 1. Which tables are accumulating active parts fastest?
SELECT database, table, partition,
       count() AS active_parts,
       formatReadableSize(sum(bytes_on_disk)) AS size
FROM system.parts
WHERE active
GROUP BY database, table, partition
ORDER BY active_parts DESC
LIMIT 20;

-- 2. Top offenders by scan volume over the last 24 hours
SELECT normalized_query_hash,
       count()                                  AS runs,
       formatReadableSize(sum(read_bytes))      AS total_read,
       round(avg(query_duration_ms))            AS avg_ms,
       formatReadableSize(max(memory_usage))    AS peak_mem,
       any(substring(query, 1, 120))            AS sample
FROM system.query_log
WHERE type = 'QueryFinish'
  AND event_time > now() - INTERVAL 1 DAY
GROUP BY normalized_query_hash
ORDER BY sum(read_bytes) DESC
LIMIT 15;

-- 3. Replication health at a glance
SELECT database, table, is_readonly, absolute_delay,
       queue_size, inserts_in_queue, merges_in_queue, last_queue_update
FROM system.replicas
WHERE absolute_delay > 30 OR is_readonly
ORDER BY absolute_delay DESC;

-- 4. Merge pressure and long-running merges
SELECT database, table, elapsed, progress, num_parts,
       formatReadableSize(total_size_bytes_compressed) AS merging_size
FROM system.merges
ORDER BY elapsed DESC;

CHT-400 Deliverables

• A tiered incident runbook covering severity classification, first-response commands and escalation paths.
• A reusable diagnostic query library packaged as saved views for the whole team.
• Root-cause analysis write-ups for six simulated production incidents.
• A preventive alerting specification with thresholds justified from measured baselines.

Teams often pair this ClickHouse Training program with ChistaDATA break-fix engineering support so that the same engineers who taught the drills are on call during the first production quarter.

Program 5 — Advanced Analytics in ClickHouse (CHT-500)

The Advanced Analytics program treats ClickHouse as an analytical compute engine rather than a storage layer. It teaches the function families that let teams replace multi-stage Spark or Python pipelines with a single declarative query: aggregate-function combinators, array and higher-order functions, window functions, sequence and funnel analysis, time-series interpolation, geospatial primitives and approximate algorithms. Learners leave able to express funnel, retention, attribution, cohort and anomaly workloads directly in ClickHouse SQL and to reason about their cost.

CHT-500 Module Breakdown

Representative Lab: Funnel, Retention and Session Analytics

-- Conversion funnel across four steps within a 30-minute window
SELECT
    level,
    count() AS users
FROM
(
    SELECT
        user_id,
        windowFunnel(1800)(
            event_time,
            event_type = 'view_product',
            event_type = 'add_to_cart',
            event_type = 'begin_checkout',
            event_type = 'purchase'
        ) AS level
    FROM analytics.raw_events
    WHERE event_date >= today() - 30
    GROUP BY user_id
)
GROUP BY level
ORDER BY level DESC;

-- Weekly retention cohort matrix
SELECT
    cohort_week,
    retentionState[1] AS week_0,
    retentionState[2] AS week_1,
    retentionState[3] AS week_2,
    retentionState[4] AS week_3
FROM
(
    SELECT
        toMonday(min(event_date)) AS cohort_week,
        retention(
            event_date >= cohort_start,
            event_date >= cohort_start + 7,
            event_date >= cohort_start + 14,
            event_date >= cohort_start + 21
        ) AS retentionState
    FROM analytics.user_activity
    GROUP BY user_id
)
GROUP BY cohort_week
ORDER BY cohort_week;

-- Gap-free time series for a dashboard, with interpolation
SELECT
    toStartOfFiveMinutes(event_time) AS bucket,
    avg(latency_ms)                  AS avg_latency
FROM analytics.raw_events
WHERE event_time BETWEEN now() - INTERVAL 6 HOUR AND now()
GROUP BY bucket
ORDER BY bucket WITH FILL STEP INTERVAL 5 MINUTE
INTERPOLATE (avg_latency AS avg_latency);

CHT-500 Deliverables

• A production analytical model supporting funnel, cohort and retention questions with documented p95 latency.
• A rewrite portfolio converting at least five external-pipeline computations into single ClickHouse queries.
• An A/B testing harness with statistically defensible significance calculations executed in SQL.
• A multi-tenant customer-facing analytics API design with isolation, quotas and caching strategy.

Executive Track for CTOs and CXOs (CHT-900)

Technical ClickHouse Training succeeds faster when the sponsoring executive understands the same architecture at decision level. CHT-900 is a condensed 10-hour briefing series that runs alongside the engineering programs.

Related reading: how ChistaDATA partners with CTOs to build next-generation data infrastructure.

Competency Matrix and Certification Ladder

Each ClickHouse Training program maps to explicit, testable competencies. The matrix below is what hiring managers and delivery leads use to decide which program a given engineer needs.

Delivery Models, Lab Environment and Corporate Training

Every ClickHouse Training program is delivered in one of four formats. The syllabus and assessment standard are identical across formats; only pacing and interaction change.

Lab Environment Specification

Hands-on time is the point of the program, so the lab platform is specified as tightly as the syllabus.

• Dedicated cluster per learner group: three shards, two replicas each, plus a three-node ClickHouse Keeper ensemble, provisioned on demand.
• Realistic data volumes: multi-billion row public datasets so that bad schema decisions actually hurt and good ones are measurably better.
• Full companion stack: Kafka or Redpanda, Grafana, Prometheus, MinIO for object storage tiers, and a Kubernetes cluster with the ClickHouse operator.
• Break-fix sandboxes: pre-broken clusters used exclusively by the ClickHouse Troubleshooting program, reset between attempts.
• Version matrix: labs available on current stable and one previous LTS line, so learners rehearse upgrades rather than only reading about them.
• Retained access: lab access continues for 30 days after the final session for revision and certification preparation.

Corporate ClickHouse Training

Corporate engagements are the most common way organisations consume ClickHouse Education from ChistaDATA. A corporate program is assembled from the five core programs and then customised: your schemas replace the sample schemas, your incident history becomes the troubleshooting case set, and your latency targets become the pass criteria for the labs.

University and Academic Partnership Programs

ChistaDATA University also licenses its ClickHouse Education material to higher-education institutions running database systems, data engineering and analytics courses. The academic package is designed to slot into an existing semester structure rather than replace it.

Departments and bootcamps can request a syllabus mapping document that aligns each ClickHouse Training module to their existing learning outcomes and accreditation requirements.

Engagement Process and Contracting

Most ClickHouse Training contracts follow the six-stage process below. Stages one and two are free of charge and produce a written scope before any commitment.

Why Engineering Teams Choose ChistaDATA for ClickHouse Training

For deeper technical background, the official ClickHouse documentation and the ClickHouse source repository are both used as primary references throughout the curriculum.

ClickHouse Training FAQ

Further Reading

• ChistaDATA Break Fix Engineering Services and Support for ClickHouse
• ChistaDATA ClickHouse Consulting and Support Services
• How ChistaDATA Partners with CTOs to Build Next-Generation Data Infrastructure
• Data Strategy — How to Build One
• ChistaDATA Data Warehousing Support and Managed Services