Skill UI

This comprehensive guide provides structured steps and code examples for upgrading Databricks Runtime versions and performing critical data migrations. It covers best practices for version compatibility checking, transitioning from Hive Metastore to Unity Catalog using methods like SYNC and CTAS, and updating deprecated API endpoints, ensuring a smooth platform evolution for data pipelines.

This comprehensive geospatial science skill covers remote sensing, GIS, spatial analysis, and machine learning for Earth observation. It supports satellite imagery processing, vector and raster data operations, spatial statistics, and point cloud processing. Users can utilize cloud-native workflows and 8 programming languages with 500+ code examples. Ideal for terrain analysis, hydrological modeling, and marine spatial analysis tasks.

A comprehensive guide and toolkit for optimizing Hex platform costs. This resource details strategies for reducing compute expenditure by auditing seat licenses, consolidating data connections, and optimizing notebook run schedules. It includes code examples for implementing result caching and usage monitoring to prevent unexpected billing spikes.

This skill provides comprehensive methods and code examples for optimizing spending on Bright Data services. It covers product selection (Proxy vs. API), implementing caching strategies, bulk usage monitoring, and setting budget alerts, ensuring cost efficiency in large-scale web scraping projects.

This comprehensive guide demonstrates how to scrape live webpages using Bright Data's Web Unlocker proxy. It details how to bypass advanced anti-scraping mechanisms, such as CAPTCHAs and fingerprinting, automatically. Users are provided with full code examples in both Node.js and Python, showing methods for scraping via the proxy endpoint and through the dedicated REST API. Ideal for starting new scraping projects or integrating Bright Data into existing workflows.

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.

A comprehensive guide to product analytics, covering event tracking, funnels, cohort retention, north star metrics, OKRs, and dashboards. Examples include PostHog implementation and Python code for retention analysis. Ideal for product managers and data analysts seeking data-driven decisions.