Results for “crustdata”

Create and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores like S3, GCS, Azure, HTTP, or HuggingFace.

Perform CRUD operations, queries, bulk operations, and container management on Azure Cosmos DB NoSQL API using the @azure/cosmos TypeScript/JavaScript SDK.

Provides command-line access to search, scraping, and structured web data from over 40 APIs including Google, Amazon, Yelp, and Zillow.

Write and run analytical SQL queries against Redpanda SQL (Oxla), a distributed PostgreSQL-wire-compatible columnar database, including data loading, external sources, and type handling.

Manages annotated data matrices for single-cell genomics, covering creation, I/O, concatenation, and manipulation of AnnData objects in h5ad and zarr formats.

Extract public web data, search engine results, and structured data from platforms like Google, Amazon, and Zillow using HasData APIs.

Extract public web data via HasData APIs, including search engine results, structured data from ecommerce, travel, jobs, and local business platforms, with support for web scraping, pre-parsed APIs, and async jobs.

Query external data from Oxla — Kafka topics via catalogs, Apache Iceberg tables, and S3/GCS/Azure parquet/ORC files — alongside native Oxla tables. Use when querying Kafka topics with CREATE KAFKA CATALOG or CREATE REDPANDA CATALOG, reading Apache Iceberg tables with the catalog=>path.table syntax, loading or.

Provides CRUD operations for Azure Cosmos DB NoSQL API from Rust, including document management, container operations, and globally distributed data access.

Provides code samples and best practices for using the Azure Tables SDK for Python to perform NoSQL key-value storage, entity CRUD, batch operations, and queries against Azure Storage Tables or Cosmos DB Table API.

Generate Python SDK setup, CRUD, bulk, and paging snippets for Microsoft Dataverse using official patterns.

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

Provides a pre-built Salesforce Sales Cloud data model in Mermaid flowchart format with color coding, relationship indicators, and optional live org metadata enrichment.

Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Provides a pre-built Salesforce B2B Commerce data model as a Mermaid flowchart with color coding and relationship indicators, plus an optional script to enrich the diagram with live org metadata.

Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.