ClickHouse plugins

Manage and optimize ClickHouse databases across the full lifecycle — schema design, data ingestion, query optimization, production deployment, monitoring, security, and cost management. Includes error diagnostics, CI setup, RBAC, migrations, and streaming ingestion.

Apply 28 prioritized best practice rules for ClickHouse schema design, query optimization, and data ingestion with supporting skills for deploying to ClickHouse Cloud, setting up local development environments, integrating Node.js and Python clients, and troubleshooting performance issues.

Diagnose and fix problems with the ClickHouse Node.js database client, including socket errors, data type mismatches, and proxy/TLS setup.

Architect ClickHouse systems with workload-aware guidance for ingestion strategy, partitioning, enrichment, and late-arriving data, using provenance-labeled best practices

Replace pandas with a ClickHouse-backed DataFrame API to accelerate data filtering, grouping, and joining across files, databases, and cloud sources.

Run ClickHouse SQL directly in Python on local files (Parquet, CSV, JSON), cloud storage (S3), and remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without a server. Supports multi-step sessions, parametrized queries, UDFs, cross-source joins, and output to DataFrames.

Audit, design, and build analytics dashboards and reporting pipelines — inventory BI tools, define metrics specs, write SQL queries, and schedule Slack/email delivery of KPI reports

Define product metrics and KPIs with complete specifications including formulas, data sources, segmentation, SQL/event tracking specs, and success thresholds for any product area or feature

Run automated code quality checks across your stack: detect unused code, code clones, and schema contract violations; validate database migrations and integration boundaries; profile performance bottlenecks; and enforce quality gates before PRs and releases.

Evaluate quantitative trading strategies with SOTA metrics including non-IID Sharpe ratios, PSR/DSR/MinTRL tests, ML prediction quality (IC, autocorrelation), and Walk-Forward Optimization to prevent overfitting. Supports range bars, open deviation bars, and ZigZag swing patterns with exchange calendar-aware session detection and ClickHouse-backed SQL validation.

Manage Reverse ETL pipelines with drt: generate YAML sync configs, initialize projects, debug failures, and migrate from Census, Hightouch, or custom scripts.

Create and manage Reverse ETL pipelines using drt — generate sync configurations, initialize projects, debug failures, migrate from Census and Hightouch, and run diagnostic checks on data warehouse connections.

Manage Estuary Flow data pipelines: capture streaming data from databases (PostgreSQL, MySQL, MongoDB), transform with SQL/TypeScript/Python derivations, and materialize to warehouses (BigQuery, Snowflake, ClickHouse). Includes schema modifications (projections, redaction, field selection) and operational tools (monitoring, SSH tunnels, health checks).

Acts as a senior DBA across 230+ database systems, handling schema design, query optimization, replication, backup, migration, monitoring, security hardening, capacity planning, and database DevOps for relational, document, key-value, graph, time-series, vector, and streaming databases.

Provides expertise for building IoT systems: device lifecycle management, MQTT messaging, edge computing, digital twins, time-series databases, security for constrained devices, and cloud platform integration (AWS, Azure, GCP).

Design ClickHouse schemas with optimal ORDER BY, PARTITION BY, and engine selection; build real-time aggregation pipelines using Materialized Views with SummingMergeTree and AggregatingMergeTree; and debug slow queries with 20+ optimization rules for 10-100x faster execution and 90% less memory usage.

Manage SigNoz observability directly from Claude: create dashboards, alert rules, and ad-hoc queries; investigate alerts, ingestion costs, and cardinality; orchestrate full service setup with SLIs, SLOs, and tuning.

Reviews ClickHouse schemas, queries, and configurations with 31 prioritized rules for schema design, query optimization, data ingestion, and agent connectivity. Essential when working with ClickHouse databases.

Stream real-time blockchain data to your infrastructure by building, deploying, and debugging data pipelines (Turbo, Mirror, Compose, Subgraphs) that index onchain events from 20+ chains into PostgreSQL, ClickHouse, Kafka, and more.