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Securing Databricks Data and Access

databricks-security-basics

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to implementing best practices for security within Databricks. Learn how to manage secrets using scoped storage, enforce the principle of least privilege via Unity Catalog grants, audit API tokens, and apply advanced data protection techniques like column-level masking and row-level security for enhanced data governance and compliance.

Databricks Migration And Version Upgrade

databricks-upgrade-migration

jeremylongshore/claude-code-plugins-plus-skills

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.

Dbt Model Generator

dbt-model-generator

jeremylongshore/claude-code-plugins-plus-skills

Guides you through generating dbt models inside data pipelines, covering setup, code generation, configuration, and validation while following best practices for ETL, orchestration, and streaming workloads.

Dbt Test Creator

dbt-test-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates dbt test creation within data pipelines, offering guided best practices, code generation, and validation to keep ETL, transformation, and streaming workflows reliable.

Early Stopping Callback

early-stopping-callback

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Feature Importance Analyzer

feature-importance-analyzer

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

Feature Store Connector

feature-store-connector

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for setting up and validating feature store connectors within ML deployment pipelines, covering best practices, code generation, and production monitoring for inference.

Forecast Generator Assistant

forecast-generator

jeremylongshore/claude-code-plugins-plus-skills

Automated assistant for forecast generator tasks in data analytics, guiding through SQL, visualization, statistics, and BI workflows with best practices, ready-to-run code, and validation.

Time Series Forecaster

forecasting-time-series-data

jeremylongshore/claude-code-plugins-plus-skills

Enables Claude to analyze historical time series patterns, select the best forecasting model, and predict future values with confidence intervals for sales, traffic, or other time-dependent metrics.

Funnel Analytics Guide

funnel-analysis-builder

jeremylongshore/claude-code-plugins-plus-skills

Automates funnel analysis builder workflows by offering guidance on SQL, visualization, statistics, and BI best practices for data analytics teams needing production-ready insights.

Incremental Load Setup

incremental-load-setup

jeremylongshore/claude-code-plugins-plus-skills

Guides data-engineering teams through configuring incremental loads in pipelines, covering best practices, orchestration tips, example code, and output validation for streaming or batch workflows.

Optimizing Juicebox API Performance

juicebox-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

This guide provides advanced strategies for optimizing interactions with the Juicebox AI API. It covers crucial best practices such as implementing caching mechanisms (with defined TTLs), executing batch operations for efficiency, managing rate limits, and optimizing data chunking for large datasets. Following these steps can significantly reduce end-to-end analysis time and ensure responsive, scalable data workflows.

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