Skill UI

A comprehensive guide for building applications that interact with Azure Blob Storage using the Azure SDK for Java. Covers essential patterns including client initialization (via connection string or credentials), uploading/downloading data from files or streams, listing blobs, and managing containers. Ideal for developers building scalable cloud data storage solutions.

This SDK provides a comprehensive Python interface for interacting with Azure Blob Storage, Microsoft's scalable object storage service. It enables users to perform core object storage operations, including uploading, downloading, listing, deleting blobs, managing containers, and setting custom metadata. The library supports both synchronous and asynchronous workflows, making it ideal for building robust data pipelines, data lakes, or cloud-native applications that require reliable, scalable file storage.

This is a comprehensive client library for interacting with Azure Blob Storage, Microsoft's scalable object storage solution for the cloud. It allows developers to perform core operations in Rust, such as uploading, downloading, listing, creating, and managing blobs and containers. Ideal for building cloud-native applications that require persistent, scalable data storage.

This SDK provides comprehensive Python support for Azure Data Lake Storage Gen2. It enables developers to interact with hierarchical file systems, performing essential big data operations such as creating, listing, uploading, downloading, and managing metadata for files and directories. Ideal for building data analytics pipelines and cloud-based data processing workflows.

A comprehensive Python SDK for interacting with Azure Queue Storage. This tool enables reliable, asynchronous communication patterns by managing message queuing, task distribution, and data processing workflows. It supports core operations including sending, receiving, peeking, updating, and deleting messages, making it ideal for building resilient, decoupled backend systems on Azure.

Automates Box cloud storage operations through Rube MCP, covering uploads, downloads, content search, folder lifecycle, sharing, collaborations, metadata queries, and sign requests; always start with RUBE_SEARCH_TOOLS to sync the current schema.

Automates configuring and aggregating infrastructure metrics across compute, storage, network, containers, load balancers, and databases with Prometheus, Datadog, or CloudWatch for dashboards and capacity tracking.

Guidelines for building Convex backends with TypeScript, detailing syntax, validators, schema design, queries, mutations, actions, scheduling, and file storage so you can implement Convex database operations, real-time updates, and cron jobs consistently.

This comprehensive guide details essential security best practices for integrating with Customer.io. It covers secure credential storage using secrets managers, sanitizing Personally Identifiable Information (PII) via hashing, verifying webhook signatures (HMAC-SHA256) to ensure data integrity, and establishing procedures for API key rotation to maintain compliance with GDPR/CCPA regulations.

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

An expert architect specializing in designing, optimizing, and scaling entire data layers from inception (greenfield) to re-architecture. Proficient in selecting the optimal database technology (SQL, NoSQL, Graph, Time-series), designing robust schemas, implementing sophisticated indexing strategies, and planning complex migration paths. Suitable for defining data storage solutions that meet high performance, scalability, and data integrity requirements.

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.