Results for “data”

Provides expert guidance on modern SQL, cloud-native databases, query optimization, performance tuning, and data modeling for OLTP/OLAP workloads.

Use OpenMetadata UI for data discovery, governance, and collaboration. Use when searching for data assets, managing descriptions and ownership, creating glossary terms, setting up data contracts, or tracking data insights and KPIs.

Audits codebases for GDPR and CCPA/CPRA compliance by inventorying PII fields, mapping data collection points, reviewing consent mechanisms, verifying data subject rights, tracing third-party data sharing, and checking retention policies.

Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.

Automates college football data operations through Composio's College Football Data toolkit via Rube MCP, with tool discovery and connection management.

Synchronizes Datadog monitor definitions between environments using the Datadog API v2 monitors endpoint, with diff-based updates, tag filtering, and Terraform state reconciliation.

Build interactive, accessible charts, graphs, and data dashboards using Recharts, D3, or Victory. Use when the user says "chart", "graph", "data visualization", "analytics dashboard", "metrics display", "D3", "Recharts", "time-series", or "data display".

Defines structural schemas, data ontologies, and interaction boundaries for autonomous entities and synthetic data architectures.

Database management, forms, reports, and data operations with LibreOffice Base.

Database management, forms, reports, and data operations with LibreOffice Base.

Database management, forms, reports, and data operations with LibreOffice Base.

Database management, forms, reports, and data operations with LibreOffice Base.

Demystifying CLIP Data

Database management, forms, reports, and data operations with LibreOffice Base.

Training Data Lifecycle Management (v5.4.2)

Executes GDPR Article 20 data portability requests, covering machine-readable format requirements (JSON, CSV, XML), direct controller-to-controller transfer mechanisms, and scope limitations to data provided by the subject on consent or contract basis. Activate for portability, data export, Art. 20, data transfer queries.

Execute KQL queries and manage Azure Data Explorer resources for fast, scalable big data analytics on log, telemetry, and time series data.

Send custom logs to Azure Monitor via Data Collection Rules and Data Collection Endpoints using the Java SDK.

Generate XML configuration files and execute SSMA Console commands for Oracle to SQL Server database migration, including schema conversion and data migration.

Validate financial data quality in market analysis documents before publication, checking price scales, instrument notation, date accuracy, allocation totals, and unit usage.

Manages biological datasets and models with LaminDB, covering setup, artifact registration, querying, lineage tracking, validation, ontology annotation, collections, branches, storage, and workflow integrations.

Manage, annotate, and trace biological data with LaminDB, an open-source FAIR data framework that makes datasets queryable, versioned, and reproducible. Use when registering or querying biological datasets (scRNA-seq, spatial, flow cytometry), validating and curating data against ontologies (genes, cell types, diseases, tissues), tracking data lineage and computational workflows, building data lakehouses, or wiring integrations with Nextflow, Snakemake, W&B, or MLflow. Part of the AlterLab Academic Skills suite.

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Automate web crawling and data extraction with Firecrawl: scrape pages, crawl sites, extract structured data, batch scrape URLs, and map website structures.

Use when the team needs to understand how data moves through frontend, backend, database, jobs, auth, and external integrations. Trigger on requests to map data flow, event flow, or state transitions across the system.

SBU Captions Dataset

Document datasets, variables, sources, and merge keys for replication

Automates GDPR Data Subject Access Request (DSAR) workflows including identity verification, PII discovery across databases and files using regex and NER, data mapping, response templating per Article 15 requirements, deadline tracking, and audit logging.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.