sqersters

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

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Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

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Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Generate production-ready Python code using Dataverse SDK with error handling, optimization, and best practices

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Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

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Guidelines for developing with Kysely, a type-safe TypeScript SQL query builder with autocompletion support

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Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

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Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

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Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.

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