Skill UI

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

This skill provides comprehensive patterns for extracting and transforming Navan booking and transaction data. It addresses complex data pipeline challenges such as API pagination, date range chunking, and UUID-based deduplication. It supports both direct REST API implementation (via code examples) and configuration for managed connectors like Fivetran and Airbyte. Ideal for populating data warehouses, building robust analytics dashboards, or ensuring data quality from Navan sources.