Results for “data loading”

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Import SaaS data into DuckDB using dlt, a data loading tool, then generate a Wren project with table definitions and relationships. Verify the setup with real SQL queries, or start from an existing DuckDB file.

• Importing HubSpot or Stripe data for SQL analysis
• Creating a Wren project from an existing dlt DuckDB file
• Generating table definitions and parent-child relationships

Query public single-cell and spatial gene expression data by tissue, disease, or cell type. Retrieve selected expression values, cell metadata, and summary counts for analysis and comparisons across datasets without first downloading entire collections.

• Explore cell types within a tissue
• Retrieve expression values for selected marker genes
• Compare cells across tissues and datasets

Find missing data in connected sources, inspect table structure, and suggest loading options for analysis. It also helps prepare a connection form for a new source, leaving connection and loading choices to the user.

• Find external tables needed for an analysis
• Browse available connected data sources
• Check table columns before choosing data

Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.

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Build and review Nuxt 4 applications with consistent server and browser rendering. Guide data fetching, page rendering and caching rules, and deferred loading to address rendering mismatches and improve performance.

• Debugging differences between server and browser rendering
• Reviewing page data fetching with useFetch and useAsyncData
• Choosing rendering and caching rules for each route group

Provision a PostgreSQL database with Neon Instagres and save connection settings for application use and migrations. Includes setup examples for Drizzle and Prisma, initial data loading, and claiming a temporary database to keep using it.

• Provision a PostgreSQL database for development
• Configure database access with Drizzle or Prisma
• Load a SQL file with initial data

Plans and scripts data migrations between SQL databases: schema mapping, type conversion, batched and resumable loading, validation and cutover. Use when the user mentions "data migration," "database migration," "migrate from MySQL to PostgreSQL," "schema migration," "ETL pipeline," "data transfer," "database consolidation," "legacy migration," or "move data between databases." Covers schema analysis, mapping, transformation, batch processing, validation, and cutover planning. For query optimization during migration, see sql-optimizer.

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Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.

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Helps developers send conversion and event data to Google Ads, Google Analytics, and Floodlight through the Data Manager API. It covers destination setup, data formatting, validation, migration from other APIs, and checking processing results.

• Implement offline conversion uploads to Google Ads
• Send recommended or custom GA4 events
• Migrate event ingestion from another Google API

Build Next.js 14+ applications with App Router, server-rendered components, and form actions. Produces routes, page layouts, data fetching, loading and error states, and SEO metadata, while supporting performance optimization and deployment preparation.

• Building pages and layouts with App Router
• Implementing form submissions with Server Actions
• Adding loading states, error handling, and SEO metadata

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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Check whether images visible when a page first opens are unnecessarily lazy loaded, meaning their loading is deferred. Verify rendering delays with performance measurements, then guide changes to loading attributes and request priority.

• Review loading settings for hero images
• Investigate image loading delays affecting LCP
• Adjust loading attributes for images in the initial viewport

Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.

• Read and write h5ad or Zarr datasets
• Combine experimental batches with source labels
• Select cells or genes using metadata

Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.

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Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.

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Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Guides implementation of audience uploads, individual removals, and full replacements through the Data Manager API. It covers formatting and hashing identifiers, validating requests, and checking processing results for Customer Match and supported device ID audiences.

• Upload Customer Match audience members
• Remove selected users from an audience
• Clear or replace an entire audience

Audit website images for search visibility and loading performance, then prioritize fixes. Check descriptive text, file sizes, and loading settings, convert files to WebP or AVIF, and add creator and copyright metadata.

• Audit product and article images for SEO issues
• Convert large images to WebP or AVIF
• Fix image loading settings and layout shifts

Populate Convex database tables with sample records or bulk imports. The skill guides data preparation against the schema, repeatable loading without duplicate records, and row count checks after the import.

• Load sample data for development
• Import bulk data into Convex
• Make data loading safe to repeat

Trigger on-chain data collection (a "Commit") in Tres Finance for wallets that have already been onboarded. Use this skill whenever the user wants to collect data, pull balances, sync wallets, refresh on-chain data, run a commit, trigger a commit, or "collect" anything in Tres. This is the second step of the Tres onboarding flow — it sits between wallet upload (tres-wallets-upload) and balance validation (tres-asset-balance-validation). Always trigger this skill for any request that mentions "commit", "collect", "data collection", "pull data", "fetch on-chain data", "sync wallets", "refresh balances", or anytime the user just finished uploading wallets and is ready to bring in their on-chain data.

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Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.

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Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django/Hibernate/Rails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.

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