Snowflake Reliability Patterns
Implement Snowflake reliability patterns: replication, failover, Time
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Implement Snowflake reliability patterns: replication, failover, Time
Optimize ClickHouse query performance with indexing, projections, settings tuning, and query analysis using system tables. Use when queries are slow, investigating performance bottlenecks, or tuning ClickHouse server settings. Trigger: "clickhouse performance", "optimize clickhouse query", "click...
Configure ClickHouse across dev, staging, and production with environment-specific settings, secrets management, and infrastructure-as-code patterns. Use when setting up per-environment ClickHouse instances, managing connection configs, or deploying to multiple environments. Trigger: "clickhouse ...
Core big data processing toolkit for data teams. Includes Polars, Dask, Vaex for large-scale data processing, ETL pipelines, and distributed computing. Use when working with datasets larger than memory, building data pipelines, or optimizing data processing performance.
Validate findings, design shuffled nulls, check label leakage, review causal features. TRIGGERS - shuffled null, label leakage
Generates specific sections of an onboarding playbook from the perspective of a data analytics manager, adhering to specific team objectives and paragraph formatting.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Dask parallel computing library. Use for scaling pandas.
dbt data transformation with SQL. Use for data pipelines.
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