Master ClickHouse for ultra-fast OLAP, real-time analytics, and distributed data warehousing: the columnar engine behind sub-second analytics at massive scale, from fundamentals to cluster administration.
The program starts with what makes ClickHouse special: its columnar, OLAP-oriented architecture, table engines and the MergeTree family, data types and schema design, and how data is inserted and queried. Understanding why ClickHouse is fast, columnar storage, vectorized execution, and clever indexing, is the key to using it well.
This architectural grounding is essential because ClickHouse rewards designing with its strengths in mind. Once you understand how MergeTree stores and merges data, decisions about schema, sorting, and partitioning become clear, which is what lets you achieve the sub-second query performance ClickHouse is chosen for.
Designing with MergeTree in mind is what lets you achieve the sub-second performance ClickHouse is chosen for.
Query optimization
The program goes deep on performance: primary keys and sorting, indexes and skip indexes, materialized views, query optimization, and aggregations at scale. ClickHouse's speed comes from designing schemas and queries that exploit its architecture, and materialized views in particular are a powerful tool for pre-computing results.
Optimization is the heart of ClickHouse work, because the platform is chosen precisely when speed at scale matters. Learning to design primary keys, use materialized views, and structure queries for the engine is exactly what turns ClickHouse's raw capability into the sub-second analytics that real-time systems depend on.
Materialized views and careful schema design are what turn ClickHouse's raw capability into real-time analytics.
Distributed ClickHouse and real-time
The program covers running ClickHouse at scale: sharding and distribution, replication with ReplicatedMergeTree, cluster topology, managing a cluster, and scaling and rebalancing. It then covers real-time ingestion, Kafka integration, integrating with the broader data stack, and operations.
This distributed and real-time depth is what makes ClickHouse a platform rather than just a fast database, and it connects directly to the streaming work in the Kafka program. The capstone builds a sharded, replicated real-time analytics platform ingesting from Kafka, which is exactly how ClickHouse is deployed for high-throughput analytics.
The distributed and real-time depth is what makes ClickHouse a platform rather than just a fast database, and it connects to the streaming stage.
A worked example
See the method, not just the topic
A representative worked example from the program, so you can see the level of concreteness the curriculum works at.
A worked example: a MergeTree table and a materialized view that pre-aggregates on insert.
-- A MergeTree table sorted for fast range scans and filters.
CREATE TABLE events (
event_time DateTime,
region LowCardinality(String),
amount Decimal(18, 2)
) ENGINE = MergeTree
ORDER BY (region, event_time); -- primary key = sort key
-- A materialized view maintains a rollup automatically on insert,
-- so dashboards read tiny pre-aggregated data, not raw events.
CREATE MATERIALIZED VIEW daily_revenue_mv
ENGINE = SummingMergeTree
ORDER BY (region, day) AS
SELECT
region,
toDate(event_time) AS day,
sum(amount) AS revenue
FROM events
GROUP BY region, day;
-- Queries against daily_revenue_mv return in milliseconds because
-- the work was done at insert time. Designing the sort key and the
-- materialized view is how you get ClickHouse's famous speed.
Curriculum · 340 chapters across four tracks
The full ClickHouse Mastery syllabus
The complete program spans a Foundations base, then Developer, DBA, and Data Engineer tracks, and a set of hands-on labs and systems projects. Every module is listed below; expand any module to see its chapters.
Foundations
2 modules · 40 chapters
20 chaptersModule 1 - ClickHouse Fundamentals
001Introduction to ClickHouseOverview of ClickHouse, its purpose as a **high-performance OLAP database**, key use cases such as real-time analytics, log analytics, observability, and data warehousing.
002OLTP vs OLAP SystemsUnderstand the differences between **transactional databases (OLTP)** and **analytical systems (OLAP)**, including query patterns, storage models, and workload characteristics.
003Columnar Databases ExplainedLearn how **column-oriented storage** works, why it enables faster analytical queries, and how column compression improves performance compared to row-based databases.
004ClickHouse Architecture OverviewExplore the high-level architecture of ClickHouse including **server components, storage engines, query execution, and distributed clusters**.
005Installing ClickHouseStep-by-step installation of ClickHouse on **Linux, macOS, and Docker environments**, including verifying the installation and running the server.
006ClickHouse Server and Client ToolsIntroduction to **clickhouse-server**, **clickhouse-client**, and web interfaces used to interact with the database and run queries.
007ClickHouse Data TypesUnderstand supported data types including **numeric, string, date, array, map, and nested types**, and when to use each in analytics workloads.
008Creating Databases and TablesLearn how to create databases and tables, define schemas, and configure storage engines for efficient analytics workloads.
009Column Storage FundamentalsExplore how ClickHouse stores data in **column files**, how column encoding works, and how this design enables high-speed scanning of large datasets.
010Basic SQL QueriesRun your first ClickHouse queries including **SELECT statements, filtering, projections, and simple aggregations**.
011Filtering and Sorting DataLearn how to filter data using **WHERE conditions**, sort results with **ORDER BY**, and limit query outputs for faster analysis.
012Aggregation BasicsUnderstand aggregation functions such as **COUNT, SUM, AVG, MIN, and MAX**, and how they enable analytical insights from large datasets.
013Query Execution BasicsLearn how ClickHouse executes queries internally including **query parsing, execution stages, and result generation**.
014Compression FundamentalsExplore how ClickHouse compresses column data using algorithms like **LZ4 and ZSTD**, improving both storage efficiency and query speed.
015Partitioning ConceptsUnderstand how partitioning organizes large datasets into logical segments to improve query performance and data management.
016Query Performance BasicsIntroduction to techniques that improve query performance including **partition pruning, indexing, and efficient schema design**.
017ClickHouse System TablesExplore system tables that provide insights into **server status, query logs, storage usage, and cluster health**.
018Understanding Data PartsLearn how ClickHouse stores data in **immutable parts**, how parts are merged in the background, and how this affects query performance.
019First Analytics WorkloadBuild and query a small analytical dataset to simulate a real **business intelligence or event analytics workload**.
020Fundamentals ReviewSummary of key concepts from the module including **architecture, storage design, SQL basics, and performance fundamentals**.
20 chaptersModule 2 - ClickHouse Architecture
021Storage ArchitectureUnderstand the overall **ClickHouse storage architecture**, including how data is organized in tables, partitions, and column files to support high-speed analytical queries.
022MergeTree Engine OverviewIntroduction to the **MergeTree storage engine**, the core engine powering most ClickHouse workloads, including its structure, capabilities, and advantages for analytics.
023Data Parts and MergesLearn how ClickHouse stores data in **immutable parts** and how background merge processes combine parts to optimize storage and query performance.
024Primary Key vs Sorting KeyExplore the difference between **primary keys and sorting keys**, how they affect query filtering, and how to design keys for efficient data retrieval.
025Partition Key DesignUnderstand how partition keys organize data into logical segments and how proper partitioning improves **query performance and data management**.
026Background Merge ProcessesExamine how ClickHouse performs **automatic background merges**, optimizing storage layout and maintaining efficient query performance over time.
027Compression AlgorithmsLearn about compression techniques used in ClickHouse, including **LZ4, ZSTD, and other codecs**, and how compression improves storage efficiency and query speed.
028Data Skipping IndexesDiscover how **data skipping indexes** help ClickHouse avoid scanning irrelevant data blocks, significantly improving query performance.
029Query Execution PipelineUnderstand the stages of the **query execution pipeline**, from parsing and planning to execution and result generation.
030Parallel Query ProcessingLearn how ClickHouse uses **parallel execution across CPU cores** to process large datasets efficiently.
031Query Memory ManagementExplore how ClickHouse manages memory during query execution and how configuration settings prevent excessive memory usage.
032Disk I/O OptimizationUnderstand how ClickHouse optimizes **disk access patterns**, minimizing I/O bottlenecks and improving query throughput.
033Storage InternalsA deeper look at internal storage components including **column files, marks, and index files** used to accelerate analytical queries.
034Replication Architecture OverviewLearn how ClickHouse replicates data across nodes using **ReplicatedMergeTree tables and ClickHouse Keeper** for fault tolerance.
035Distributed Query ExecutionUnderstand how queries are executed across **multiple nodes in a distributed cluster**, including data routing and result aggregation.
036Cluster Topology BasicsExplore common cluster architectures including **single-node setups, replicated clusters, and sharded clusters**.
037Network ConsiderationsLearn how network performance affects distributed queries and how to configure clusters for **efficient data transfer and low latency**.
038Query Planner BasicsIntroduction to how the ClickHouse query planner determines the most efficient execution strategy for analytical queries.
039Architecture OptimizationLearn techniques for optimizing ClickHouse architecture including **hardware selection, storage configuration, and cluster design**.
040Architecture Best PracticesSummary of recommended best practices for designing **scalable, high-performance ClickHouse systems**.
Developer Track
3 modules · 90 chapters
40 chaptersModule 3 - Advanced SQL Queries
041SQL Syntax Deep DiveComprehensive overview of ClickHouse SQL syntax including SELECT structure, clauses, expressions, and query formatting best practices.
042Filtering and ProjectionsLearn how to filter datasets using **WHERE conditions** and control query outputs using **projection columns** to optimize analytical queries.
043Aggregate FunctionsExplore core aggregation functions such as **COUNT, SUM, AVG, MIN, MAX**, and understand how aggregations power analytical insights.
044Window FunctionsLearn how window functions enable advanced analytical calculations like **running totals, ranking, and moving averages** across result sets.
045SubqueriesUnderstand how subqueries work in ClickHouse and how they help break complex analytical queries into manageable components.
046Nested QueriesLearn to structure queries with nested layers for performing **multi-stage data transformations and aggregations**.
047Array FunctionsExplore array data types and functions that enable storing and querying **multi-value attributes within a single column**.
048Map Data TypesUnderstand the Map data structure and how it allows storing **key-value pairs for flexible analytical modeling**.
049JSON Data ProcessingLearn how to query and extract information from **JSON fields**, enabling analytics on semi-structured data.
050Date and Time FunctionsExplore functions for working with **timestamps, time intervals, and calendar calculations** in time-based analytics.
051Conditional ExpressionsLearn how conditional expressions like **CASE and IF statements** allow dynamic calculations within analytical queries.
052JOIN TypesUnderstand different JOIN operations including **INNER JOIN, LEFT JOIN, RIGHT JOIN, and CROSS JOIN** and their analytical applications.
053JOIN OptimizationLearn strategies to optimize JOIN performance when working with **large datasets across multiple tables**.
054Distributed JoinsExplore how joins operate across **distributed ClickHouse clusters** and techniques to maintain query efficiency.
055Aggregation OptimizationUnderstand how ClickHouse optimizes aggregation queries using **pre-aggregation, indexing, and query planning techniques**.
056Approximate FunctionsLearn about approximate algorithms such as **approximate distinct counts and quantiles** used for high-speed analytics.
057Statistical FunctionsExplore built-in functions for statistical analysis including **variance, standard deviation, and distribution metrics**.
058Geo FunctionsLearn how ClickHouse handles **geospatial analytics**, including distance calculations and geographic data queries.
059Query ProfilingUnderstand how to analyze query performance using profiling tools that track **execution time, CPU usage, and resource consumption**.
060Execution Plan AnalysisLearn how execution plans reveal how ClickHouse processes queries and how to identify performance bottlenecks.
061Query Optimization StrategiesExplore best practices for writing efficient queries including **filter pushdown, aggregation design, and index usage**.
062Handling Large DatasetsLearn techniques for querying **billions of rows efficiently** using optimized schema design and query patterns.
063Analytical Query PatternsUnderstand common analytical query patterns used in **business intelligence and data analysis workflows**.
064Time-Series QueriesLearn how ClickHouse handles time-series analytics including **time-based aggregations and rolling calculations**.
065Event Analytics QueriesExplore queries designed for analyzing **event streams such as user activity, logs, and transaction data**.
066Query CachingUnderstand how query caching reduces repeated computation and improves performance in analytics dashboards.
067ProjectionsLearn how projections pre-compute query structures to accelerate repeated analytical workloads.
068Query DebuggingExplore debugging techniques to identify errors, performance issues, and unexpected query behavior.
069Query ConcurrencyUnderstand how ClickHouse handles multiple simultaneous queries and how to manage concurrency effectively.
070Resource ManagementLearn how queries consume system resources including **CPU, memory, and disk**, and how to optimize usage.
071Advanced AggregationsExplore advanced aggregation patterns such as **multi-stage aggregation and hierarchical aggregation queries**.
072Complex Analytics QueriesLearn how to design multi-step analytical queries for complex datasets and real-world business scenarios.
073Query Performance TuningUnderstand how to tune queries for optimal performance through indexing, query restructuring, and resource configuration.
074Billion-Row Query OptimizationLearn techniques specifically designed for **analyzing extremely large datasets with minimal query latency**.
075Query Anti-PatternsIdentify common mistakes in SQL query design that lead to poor performance or inefficient resource usage.
076Analytical WorkloadsUnderstand how analytical workloads differ from transactional workloads and how queries should be designed accordingly.
077Dashboard Query DesignLearn how to design queries optimized for **BI dashboards and interactive analytics tools**.
078Real-Time Query PipelinesExplore query pipelines used in **real-time analytics environments** processing streaming data.
079SQL Performance Case StudiesReview real-world case studies showing how query optimization improves performance in production systems.
080SQL Mastery ReviewComprehensive review of advanced SQL concepts and best practices learned throughout the module.
081Table Engine OverviewIntroduction to ClickHouse table engines and how they control **data storage, indexing, and query behavior**.
082MergeTree EngineLearn how the **MergeTree engine** works, including partitions, primary keys, and data parts that power most ClickHouse analytical workloads.
083ReplacingMergeTreeUnderstand how ReplacingMergeTree supports **deduplication and record updates** using version columns.
084SummingMergeTreeExplore how SummingMergeTree automatically **aggregates numeric columns during merges**, improving performance for aggregated analytics.
085AggregatingMergeTreeLearn how AggregatingMergeTree stores **pre-aggregated states**, enabling efficient analytical queries on large datasets.
086CollapsingMergeTreeUnderstand how CollapsingMergeTree supports **row collapsing operations** to represent insert/delete changes in event-based datasets.
087VersionedCollapsingMergeTreeExplore advanced collapsing strategies using version numbers to **handle complex data updates and history tracking**.
088Log EnginesIntroduction to lightweight log engines such as **TinyLog, Log, and StripeLog** used for simple storage scenarios.
089Memory EngineLearn how the Memory engine stores data entirely in RAM for **ultra-fast temporary analytical operations**.
090Distributed EngineUnderstand how the Distributed engine enables **query execution across multiple nodes in a ClickHouse cluster**.
091Engine ComparisonCompare different ClickHouse table engines and understand their strengths for **various analytical workloads**.
092Choosing the Right EngineLearn how to select the most appropriate engine based on **data update patterns, query workloads, and storage requirements**.
093Partition Design StrategiesUnderstand how partitioning organizes large datasets and how well-designed partitions improve **query efficiency and data management**.
094Sorting Key OptimizationLearn how sorting keys influence **data ordering, indexing efficiency, and query filtering performance**.
095High-Cardinality HandlingExplore techniques for managing columns with **high-cardinality values** while maintaining query efficiency.
096Deduplication StrategiesUnderstand methods for handling duplicate data using **ReplacingMergeTree, unique keys, and data ingestion logic**.
097Data Retention PoliciesLearn how to manage historical data using **retention strategies and automated cleanup policies**.
098TTL ConfigurationExplore Time-To-Live rules that automatically **delete or move data after a defined time period**.
099Storage OptimizationLearn strategies to optimize storage layout, including **partitioning, compression, and schema design**.
100Compression TuningUnderstand how to configure compression codecs and balance **storage efficiency with query performance**.
101Schema EvolutionLearn how to modify schemas safely over time, including **adding columns and adapting to changing data structures**.
102Analytical Schema DesignExplore best practices for designing schemas optimized for **analytical workloads and high-speed querying**.
103Wide Tables vs Star SchemaCompare wide table models with star schema designs and understand their trade-offs for analytical performance.
104Fact TablesLearn how fact tables store **quantitative metrics and event data** for analytical reporting.
105Dimension TablesUnderstand how dimension tables store **descriptive attributes used for filtering and grouping analytical data**.
106Incremental Aggregation TablesExplore techniques for storing **pre-aggregated data** to accelerate analytical queries.
107Streaming Event TablesLearn how to design schemas optimized for **high-volume event streams such as logs or clickstream data**.
108Time-Series SchemasUnderstand schema design patterns for **time-series datasets including sensor data and financial transactions**.
109Schema Anti-PatternsIdentify common schema design mistakes that negatively impact **query performance and scalability**.
110Storage Design ReviewComprehensive review of table engines, schema design strategies, and storage optimization techniques.
111Materialized View FundamentalsIntroduction to materialized views and how they store **precomputed query results** to accelerate analytical workloads.
112Creating Materialized ViewsLearn how to create materialized views in ClickHouse and connect them to source tables for automatic data processing.
113Incremental Aggregation ViewsUnderstand how incremental aggregation allows ClickHouse to **update aggregated metrics continuously as new data arrives**.
114Pre-Computation StrategiesExplore strategies for precomputing heavy calculations to reduce query latency in large analytical datasets.
115Materialized View PipelinesLearn how materialized views can be used to build **data processing pipelines** that transform incoming data in real time.
116Query Acceleration with ViewsUnderstand how materialized views reduce query complexity by **serving pre-aggregated or pre-filtered datasets**.
117Materialized View MaintenanceLearn how to manage and maintain materialized views, including monitoring their performance and ensuring data accuracy.
118Refresh StrategiesExplore methods for keeping materialized views updated using **automatic updates, refresh schedules, or rebuild strategies**.
119Projections vs ViewsCompare ClickHouse projections and materialized views, and understand when each approach is best suited for query optimization.
120Aggregation CachingLearn how aggregation caching improves query performance by storing frequently accessed aggregated results.
121Query Result CachingUnderstand how ClickHouse caches query results to reduce computation time for repeated queries.
122Real-Time Aggregation ModelsExplore real-time data aggregation models used in **event analytics and monitoring systems**.
123Streaming AggregationsLearn how streaming aggregations process incoming data continuously to produce real-time metrics.
124Analytics Pipelines with ViewsUnderstand how materialized views form the backbone of **automated analytics pipelines**.
125Handling Late-Arriving DataLearn strategies to manage late-arriving events and maintain accurate aggregations in real-time systems.
126Debugging Materialized ViewsExplore troubleshooting techniques for identifying errors or unexpected behavior in materialized view pipelines.
127View OptimizationLearn how to optimize materialized views to minimize storage usage and maximize query performance.
128Scaling View PipelinesUnderstand how to scale materialized view pipelines to handle **high-volume streaming datasets**.
129View Architecture PatternsExplore common architectural patterns for using materialized views in **large analytics platforms**.
130Materialized View ReviewComprehensive review of materialized views, caching strategies, and their role in high-performance analytics systems.
171Backup ArchitectureOverview of backup architecture in ClickHouse and how backups protect data against failures, corruption, or accidental deletion.
172Snapshot BackupsLearn how snapshot backups capture the state of a database at a specific moment, allowing fast recovery.
173Incremental BackupsUnderstand incremental backup strategies that store only changes since the previous backup to reduce storage and backup time.
174Backup AutomationExplore methods for automating backups using scripts and scheduling tools to ensure regular data protection.
175Backup Storage DesignLearn how to design storage strategies for backups including **local storage, object storage, and remote backup repositories**.
176Restore ProceduresUnderstand how to restore ClickHouse data from backups and ensure minimal downtime during recovery.
177Disaster Recovery PlanningExplore disaster recovery strategies to maintain business continuity after critical failures.
178Data Protection StrategiesLearn techniques for protecting sensitive data and maintaining integrity in analytics environments.
179Security ArchitectureOverview of security architecture in ClickHouse including authentication, authorization, and secure communications.
180Role-Based Access ControlUnderstand how role-based access control (RBAC) restricts database access based on user roles and permissions.
181User AuthenticationLearn authentication mechanisms used to verify user identity before granting access to the database.
182Encryption MethodsExplore encryption techniques used to secure data both **in transit and at rest**.
183Network SecurityUnderstand network security practices including firewall configuration and secure communication between cluster nodes.
184Secure Cluster SetupLearn how to configure clusters securely using authentication, encryption, and restricted network access.
185Query Audit LoggingExplore how audit logs track query activity to support security monitoring and compliance.
186Resource QuotasUnderstand how resource quotas limit resource consumption by users or queries to maintain system stability.
187Memory PoliciesLearn how memory policies control memory usage during query execution to prevent system overload.
188CPU Resource ManagementExplore techniques for controlling CPU usage across workloads in a shared analytics environment.
189Disk MonitoringUnderstand how monitoring disk usage prevents storage shortages and maintains cluster stability.
190Query TimeoutsLearn how query timeout settings prevent runaway queries from consuming excessive resources.
191Operational MonitoringExplore monitoring systems that track cluster health, query activity, and infrastructure performance.
192Metrics AnalysisUnderstand how operational metrics help administrators detect anomalies and optimize system performance.
193Alerting SystemsLearn how alerting systems notify administrators about system issues such as failures or performance degradation.
194Incident ResponseUnderstand how to respond effectively to system incidents and restore services quickly.
195Performance TroubleshootingLearn techniques for identifying and resolving performance bottlenecks in ClickHouse systems.
196Query DebuggingExplore debugging methods used to analyze slow or failing queries.
197Production Support WorkflowsUnderstand how production support teams manage operational issues and maintain system reliability.
198SLA MonitoringLearn how service level agreements are monitored to ensure system performance meets defined standards.
199Operational Best PracticesReview recommended operational practices for maintaining stable, secure, and scalable ClickHouse deployments.
200Operations ReviewComprehensive review of backup strategies, security practices, and operational management techniques.
Data Engineer Track
3 modules · 80 chapters
20 chaptersModule 8 - Kafka & S3 Integration
201Data Pipeline ArchitectureOverview of modern data pipeline architectures and how ClickHouse fits into **real-time and batch analytics systems**.
202Batch vs Streaming PipelinesUnderstand the differences between **batch processing and streaming pipelines**, including when each approach is appropriate.
203Kafka Integration OverviewIntroduction to Apache Kafka and how it integrates with ClickHouse for **high-throughput event ingestion**.
204Kafka Engine TablesLearn how ClickHouse Kafka engine tables consume messages from Kafka topics and transform them into structured data.
205Kafka Ingestion PipelinesUnderstand how Kafka ingestion pipelines capture streaming events and load them into ClickHouse for analytics.
206Streaming IngestionExplore techniques for processing real-time data streams continuously as events arrive.
207Kafka Connect IntegrationLearn how Kafka Connect integrates external systems with ClickHouse using connectors for automated data ingestion.
208Event-Driven ArchitecturesUnderstand event-driven system design where applications generate events that feed analytics pipelines.
209CDC Pipeline DesignExplore Change Data Capture (CDC) pipelines that capture database changes and stream them into ClickHouse.
210PostgreSQL Ingestion PipelinesLearn how to ingest data from PostgreSQL databases into ClickHouse for analytical processing.
211MySQL Ingestion PipelinesUnderstand methods for streaming MySQL data into ClickHouse using CDC and connector-based pipelines.
212S3 Data IngestionLearn how to ingest datasets stored in Amazon S3 into ClickHouse using file-based data ingestion techniques.
213Object Storage IntegrationExplore how ClickHouse integrates with object storage platforms such as S3 for scalable data storage and ingestion.
214Parquet IngestionUnderstand how columnar file formats like Parquet enable efficient data ingestion and storage in ClickHouse.
215Data Lake IntegrationLearn how ClickHouse integrates with modern data lake architectures to support large-scale analytics.
216Schema EvolutionExplore techniques for adapting schemas as data structures change over time without disrupting analytics pipelines.
217Data Validation PipelinesUnderstand how validation steps ensure data quality and integrity before data enters analytical systems.
218Ingestion Failures HandlingLearn strategies for detecting and recovering from failures in data ingestion pipelines.
219Retry MechanismsExplore retry strategies that ensure reliable data ingestion in the presence of transient system failures.
220Backpressure ManagementUnderstand how backpressure mechanisms prevent pipeline overload when ingestion rates exceed processing capacity.
20 chaptersModule 9 - ETL Pipelines using Airflow
221Python Ingestion PipelinesLearn how Python scripts can be used to build automated pipelines that ingest data into ClickHouse.
222Spark Ingestion PipelinesUnderstand how Apache Spark processes large datasets and loads transformed data into ClickHouse for analytics.
223Airflow Orchestration BasicsIntroduction to Apache Airflow and how it orchestrates complex data workflows across multiple systems.
224Airflow ArchitectureExplore the architecture of Airflow including the **scheduler, executor, metadata database, and worker nodes**.
225Building Airflow DAGsLearn how to design Directed Acyclic Graphs (DAGs) that define the sequence and dependencies of ETL tasks.
226Scheduling WorkflowsUnderstand how Airflow schedules workflows and manages recurring data processing jobs.
227Automated ETL PipelinesLearn how ETL pipelines automatically extract, transform, and load data into ClickHouse.
228Data Transformation PipelinesExplore transformation techniques used to clean, enrich, and prepare raw data for analytics.
229Incremental Ingestion PipelinesUnderstand how incremental pipelines process only new or changed data to improve efficiency.
230Data Quality ChecksLearn how to implement validation checks to ensure data accuracy and consistency.
231Streaming AggregationsExplore real-time aggregation techniques used to compute metrics from streaming datasets.
232Pipeline MonitoringUnderstand how monitoring tools track pipeline health, execution status, and performance.
233Pipeline OptimizationLearn how to improve pipeline performance by optimizing scheduling, resource usage, and task dependencies.
234Large-Scale Ingestion SystemsExplore architectures designed to ingest **massive volumes of data from multiple sources simultaneously**.
235Data Platform ArchitecturesUnderstand how modern data platforms combine ingestion pipelines, data lakes, and analytics engines.
236Streaming Analytics SystemsLearn how streaming analytics systems process real-time data to generate insights immediately.
237Real-Time Data EngineeringExplore engineering techniques used to build **low-latency data pipelines for real-time analytics**.
238Pipeline TroubleshootingLearn how to diagnose and fix issues that occur during pipeline execution.
239Data Engineering Best PracticesReview best practices for building scalable, reliable, and maintainable data pipelines.
240Pipeline Architecture ReviewComprehensive review of ETL pipeline architectures, orchestration techniques, and data engineering strategies.
40 chaptersModule 10 - ClickHouse Cloud & BI Integrations
241ClickHouse Cloud OverviewIntroduction to ClickHouse Cloud, its architecture, managed services, and benefits for scalable analytics deployments.
242Deploying Cloud ClustersLearn how to deploy ClickHouse clusters in cloud environments and configure them for analytical workloads.
243Cloud Scaling StrategiesUnderstand scaling strategies such as **horizontal scaling, auto-scaling, and workload balancing** in cloud environments.
244Cloud Storage IntegrationExplore how cloud storage services integrate with ClickHouse to support scalable and cost-efficient data storage.
245Managed Cluster ConfigurationLearn how to configure managed ClickHouse clusters including resource allocation, networking, and storage settings.
246Cloud MonitoringUnderstand monitoring tools and dashboards used to track performance and system health in cloud deployments.
247Cloud SecurityExplore security measures for protecting cloud-based ClickHouse deployments including authentication, encryption, and network controls.
248Multi-Cloud ArchitecturesLearn how analytics systems operate across multiple cloud providers to improve resilience and reduce vendor lock-in.
249ClickHouse with GrafanaUnderstand how Grafana integrates with ClickHouse to build real-time dashboards and monitoring visualizations.
250ClickHouse with Power BILearn how Power BI connects to ClickHouse to create interactive business intelligence reports.
251ClickHouse with TableauExplore how Tableau can be used to visualize large analytical datasets stored in ClickHouse.
252ClickHouse with SupersetLearn how Apache Superset integrates with ClickHouse to build open-source BI dashboards.
253Python Analytics IntegrationUnderstand how Python libraries interact with ClickHouse for data analysis and automated workflows.
254Pandas IntegrationLearn how the Pandas library can retrieve and process ClickHouse datasets for advanced data analysis.
255Jupyter NotebooksExplore how Jupyter notebooks support exploratory data analysis using ClickHouse queries and Python tools.
256Real-Time DashboardsLearn how to design dashboards that display **live analytics data from streaming pipelines**.
257Observability AnalyticsUnderstand how ClickHouse powers observability platforms that analyze logs, metrics, and traces.
258Log Analytics SystemsExplore architectures for analyzing large volumes of system logs using ClickHouse.
259IoT Analytics SystemsLearn how ClickHouse processes time-series data generated by IoT devices and sensors.
260Marketing Analytics PlatformsUnderstand how marketing platforms analyze campaign performance and customer engagement using ClickHouse.
261Customer Analytics PlatformsExplore how organizations analyze customer behavior and interactions to generate insights.
262Retail Analytics SystemsLearn how retail companies use ClickHouse to analyze sales data, inventory trends, and customer purchasing patterns.
263Fintech Analytics PipelinesUnderstand how financial analytics systems process transactions and trading data in real time.
264Data Warehouse AccelerationExplore how ClickHouse accelerates traditional data warehouse workloads with faster query execution.
265Analytics System DesignLearn architectural principles for designing scalable analytics platforms powered by ClickHouse.
266Visualization OptimizationUnderstand techniques for optimizing visualization queries to ensure responsive dashboards.
267BI Performance TuningLearn how to tune queries and data models for high-performance business intelligence applications.
268Dashboard EngineeringExplore best practices for building reliable and scalable analytics dashboards.
269Analytics Architecture PatternsUnderstand common architecture patterns used in modern analytics platforms.
270Cloud Analytics Best PracticesReview best practices for deploying and managing analytics systems in cloud environments.
271Cloud Cost OptimizationLearn how to manage cloud costs through efficient resource allocation and storage optimization.
272Multi-Region Analytics ClustersUnderstand how to deploy clusters across multiple regions for high availability and global analytics.
273Hybrid Cloud ArchitecturesExplore architectures that combine on-premise infrastructure with cloud-based analytics systems.
274Observability PlatformsLearn how ClickHouse powers modern observability platforms used for monitoring applications and infrastructure.
275Analytics Ecosystem TrendsExplore emerging trends in analytics infrastructure and data platforms.
276Real-Time BI ArchitecturesUnderstand architectures that support real-time business intelligence and streaming analytics.
277Large-Scale VisualizationLearn techniques for visualizing massive datasets while maintaining responsive dashboards.
278Cloud Performance TuningExplore methods for optimizing ClickHouse performance in cloud environments.
279Future of Analytics PlatformsDiscuss the future evolution of analytics technologies including real-time processing and AI-driven analytics.
280Ecosystem ReviewComprehensive review of the ClickHouse cloud ecosystem, integrations, and analytics platform architecture.
Hands-On Labs & Projects
4 modules · 60 chapters
10 chaptersPerformance Benchmark Lab
281Benchmark Dataset SetupPrepare a large analytical dataset and configure the environment required for benchmarking ClickHouse queries.
282Loading Billion-Row DatasetsLearn techniques for efficiently loading **very large datasets** into ClickHouse for performance testing.
283Query Profiling ExercisesAnalyze query execution metrics such as **CPU usage, execution time, and memory consumption**.
284Aggregation Performance TestsBenchmark common aggregation queries like **COUNT, SUM, and GROUP BY** on large datasets.
285Partition Tuning ExperimentsEvaluate how different partition strategies impact query performance and data scanning efficiency.
286Compression BenchmarkingCompare compression codecs and analyze how compression affects storage size and query speed.
287Query Optimization ChallengeApply optimization techniques such as **better filtering, indexing, and schema design** to improve query performance.
288Benchmark AnalysisInterpret benchmark results to identify performance bottlenecks and improvement opportunities.
289Performance Report CreationCreate a structured performance report summarizing benchmark results and optimization findings.
290Benchmark Lab ReviewReview lessons learned from benchmarking experiments and summarize best practices for ClickHouse performance tuning.
10 chaptersReal-Time Ingestion Project
291Kafka Environment SetupInstall and configure Apache Kafka, set up brokers, and prepare the environment for real-time event streaming.
292Streaming Event GenerationCreate simulated event streams such as user activity, system logs, or transaction events for testing ingestion pipelines.
293Kafka Topic ConfigurationLearn how to configure Kafka topics including **partitions, replication factors, and retention policies**.
294Kafka Table EnginesUnderstand how the ClickHouse Kafka engine consumes streaming data from Kafka topics and transforms messages into structured records.
295Streaming Ingestion PipelineBuild a real-time ingestion pipeline that continuously loads Kafka events into ClickHouse tables.
296Real-Time Aggregation QueriesDesign queries that compute metrics such as event counts, averages, and trends in real time.
297Dashboard CreationConnect Grafana to ClickHouse and build dashboards that visualize real-time data from the ingestion pipeline.
298Monitoring Ingestion PipelinesImplement monitoring techniques to track ingestion throughput, latency, and system health.
299Scaling Ingestion PipelinesLearn how to scale the pipeline using **partitioning, distributed ingestion, and cluster resources**.
300Project ReviewReview the architecture and lessons learned from building a real-time analytics pipeline.
10 chaptersDistributed Cluster Setup
301Multi-Node Cluster SetupDesign the cluster topology and prepare infrastructure for a multi-node ClickHouse deployment.
302Installing ClickHouse NodesInstall ClickHouse server software on multiple nodes and verify that each node is operational.
303Cluster Configuration FilesConfigure cluster settings using ClickHouse configuration files that define **shards, replicas, and node roles**.
304Creating Distributed TablesLearn how distributed tables route queries across multiple shards and combine results from cluster nodes.
305Replication SetupConfigure replicated tables to ensure **data redundancy and fault tolerance** across nodes.
306Load Balancing ConfigurationSet up load balancing mechanisms that distribute query workloads across cluster nodes.
307Failover TestingSimulate node failures and verify that the cluster automatically redirects queries to available replicas.
308Distributed Query TestingRun analytical queries across the cluster to evaluate distributed query execution and performance.
309Cluster MonitoringImplement monitoring tools and dashboards to track cluster health, performance metrics, and system activity.
310Cluster ScalingLearn how to scale the cluster by adding nodes and redistributing workloads efficiently.
30 chaptersAdvanced Systems Projects
311Log Analytics PlatformDesign and implement a platform that ingests and analyzes system logs for operational insights.
312Observability Analytics SystemBuild an observability system that analyzes metrics, logs, and events to monitor application performance.
313IoT Analytics PipelinesDevelop pipelines that process high-volume sensor data generated by IoT devices.
314Marketing Analytics PlatformCreate an analytics platform that evaluates marketing campaign performance and customer engagement.
315Ad-Tech Analytics PipelineBuild a pipeline that processes advertising events such as impressions and clicks for real-time analytics.
316Fraud Detection AnalyticsDesign analytics workflows that detect suspicious transactions and patterns in financial data.
317Financial Transaction AnalyticsAnalyze large volumes of financial transactions to generate insights and risk metrics.
318Security Event AnalyticsDevelop systems that analyze cybersecurity events and identify potential threats.
319Supply Chain AnalyticsBuild analytics solutions that monitor logistics operations, inventory levels, and shipment performance.
320Customer Behavior AnalyticsAnalyze customer activity data to identify behavior patterns and improve business decision-making.
321Trillion-Row Query OptimizationLearn strategies for optimizing queries against extremely large datasets with trillions of records.
322Extreme Ingestion TestingEvaluate ingestion pipelines under heavy workloads to ensure scalability and reliability.
323Cluster Stress TestingSimulate high-load conditions to test cluster stability and performance limits.
324High Concurrency Query TestingTest system behavior when multiple users run analytical queries simultaneously.
325Storage Optimization ExperimentsExperiment with storage strategies to improve data access efficiency and reduce resource usage.
326Query Planner TuningAnalyze and optimize query planning mechanisms to improve query execution performance.
327Resource OptimizationTune CPU, memory, and storage usage to maximize system efficiency.
328Disaster Recovery SimulationSimulate disaster scenarios and practice recovery procedures for analytics systems.
329Multi-Region Failover SimulationTest failover mechanisms in distributed clusters deployed across multiple geographic regions.
330Large Dataset AnalyticsAnalyze extremely large datasets to evaluate system performance and analytical capabilities.
331Real-Time ML Feature PipelinesBuild pipelines that generate real-time features used for machine learning models.
332AI Analytics DatasetsPrepare large-scale datasets used for training and evaluating AI models.
333Vector Search IntegrationExplore integration with vector search systems for AI-driven analytics and similarity queries.
334ClickHouse Lakehouse IntegrationIntegrate ClickHouse with modern lakehouse architectures for hybrid analytics workloads.
335Observability Pipeline DesignDesign pipelines that process observability data such as logs, traces, and performance metrics.
336Event Analytics ArchitectureDevelop architectures that analyze large streams of event data in real time.
337Real-Time Data Platform ArchitectureDesign complete data platforms that combine ingestion pipelines, analytics engines, and visualization tools.
338Extreme Analytics System DesignArchitect analytics systems capable of handling **petabyte-scale data and ultra-high query volumes**.
339Production Architecture ReviewEvaluate production architectures and review best practices for large-scale analytics systems.
340Program CompletionFinal review of the program, summarizing key concepts across **data engineering, analytics architecture, and ClickHouse optimization**.
Ways to learn
Available in every format
The ClickHouse Mastery Program is available self-paced online, as a mentor-led cohort, and as a private corporate cohort. Choose the format that fits how you learn.
Self-paced
The complete program is available on demand as an online, self-paced course you can start immediately and work through at your own pace, with lifetime access to lessons and labs.
Learn alongside a cohort with a mentor, scheduled sessions, and guided progress through the tracks and projects, for those who prefer structure and live support.
The program is hands-on and project-driven: you work with ClickHouse from its columnar architecture to distributed clusters through real labs rather than watching from a distance, and it builds toward a capstone you can keep and show. Every concept is applied, because platform skills are built by doing, not by reading about them.
It is structured so a motivated learner can start where they are and build steadily, with worked examples and code throughout. The through-line is always real, production-shaped work, so at every stage you are learning the platform the way practitioners actually use it.
Prerequisites and pace
SQL is the main prerequisite, and the Kafka program complements the real-time ingestion material. The program builds ClickHouse from fundamentals. The pace builds from foundations to a capstone, and the most effective approach is to complete each lab rather than skim it, since the labs accumulate into the project.
For someone working toward the senior end of this track, consistency matters more than speed: steady progress through the material, and through the capstone, is what builds durable capability and a portfolio that demonstrates it.
What makes this program different
It teaches you to design for sub-second OLAP with MergeTree, materialized views, and sharding, the techniques behind real-time analytics at scale. That focus is what turns knowledge of a platform into the ability to build and operate real systems on it.
The other distinction is the orientation toward the whole journey. Every program in this track is designed to fit with the others and to build toward the senior technical-leadership destination, so this one is taught as a step on that path rather than an isolated course.
Design schemas and optimize for sub-second queries
Run distributed, sharded, replicated ClickHouse
Ingest real-time data from Kafka
Operate ClickHouse analytics in production
Who it is for
Who should take it
Data engineers building real-time analytics
Engineers running high-throughput OLAP
Platform engineers operating ClickHouse clusters
Teams needing sub-second analytics at scale
Common questions
When would I choose ClickHouse? When you need sub-second analytics over huge volumes. It is purpose-built for OLAP, and the program teaches you to design schemas that exploit that speed.
Does it work with the streaming stage? Yes. ClickHouse ingests real-time data from Kafka, so it pairs naturally with the streaming program to build live analytics platforms.
Where it leads, toward Principal
In the near term, this program opens Real-time Analytics and OLAP Engineer roles, and toward senior data-platform roles where speed at scale matters. The capstone is concrete evidence of that capability, which matters more than any list of topics studied.
In the longer term, it is one rung on the ladder toward Principal Engineer and Director of Cloud and Data Platforms. That destination is reached through breadth across the whole stack plus the architecture, operations, and leadership judgment the senior programs emphasize, and this program contributes a genuine, in-demand piece of that breadth.
How it fits the journey
This program is the real-time analytics database that closes the databases stage. It rests on the foundations before it and connects to the programs around it, so taking it in sequence builds cumulative command rather than isolated knowledge.
Because every program is also complete in itself, you can enter here if this is exactly the platform your goals require, and still get a whole, finished program. The sequence is a guide, not a gate, all the way up to the Principal and Director destination.
The project
What you build and keep
Build a real-time analytics platform on ClickHouse: design MergeTree tables and materialized views for sub-second queries, ingest a high-throughput stream from Kafka, and stand up a sharded, replicated cluster with monitoring, benchmarking the query performance you achieve.
Format: Self-paced with hands-on labs from fundamentals to a real-time analytics capstone.
Corporate training
Run this program for your team
Every program can be delivered as a private, tailored cohort for your organization, aligned to your systems, policies, and career frameworks.
What is the ClickHouse - Real-Time Analytics Mastery program?
Master ClickHouse for ultra-fast OLAP, real-time analytics, and distributed data warehousing: the columnar engine behind sub-second analytics at massive scale, from fundamentals to cluster administration.
Who is this program for?
It suits data engineers building real-time analytics, along with others described on this page.
How is it delivered?
Self-paced with hands-on labs from fundamentals to a real-time analytics capstone.
Is there a project or capstone?
Build a real-time analytics platform on ClickHouse: design MergeTree tables and materialized views for sub-second queries, ingest a high-throughput stream from Kafka, and stand up a sharded, replicated cluster with monitoring, benchmarking the query performance you achieve.
How does this fit the wider journey?
ClickHouse completes the core databases stage with real-time OLAP, and it integrates with Kafka from the streaming stage. Sub-second analytics at scale is an increasingly sought capability, rounding out the breadth that senior and principal platform engineers bring.
Can my organization run this as a private cohort?
Yes. Every program can be delivered as a tailored corporate cohort. Contact us to scope it.