'data processing' 검색 결과

Reads a project or processing description and returns a DRAFT data protection impact assessment pack: processing description, necessity and proportionality questions, a risk table with blank rating cells, mitigations to consider and open questions. Never rates risk, determines the lawful basis or judges adequacy; the DPO assesses and signs. Works from descriptions, never personal data. Use when the user asks to "start a DPIA for the new monitoring tool", "draft a data protection impact assessment", "scaffold a DPIA from this processing description" or "what would the DPO need for a DPIA on this". Do not use for redacting or de-identifying a document, use document-deidentification-pass instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Drafts for human review; never approves, authorises or signs off.

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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.

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Java의 Quarkus 서비스 설계와 검토를 지원합니다. REST API 구성, Panache 데이터 접근, Camel 메시지 연계와 비동기 처리의 구현 패턴을 제공합니다.

• REST API의 책임 분리
• Camel과 RabbitMQ 연계
• Panache 저장 처리 검토

Node.js, Express, Next.js의 API와 서버 데이터 처리를 설계하고 검토합니다. 데이터베이스 조회, 캐시, 인증, 오류 처리를 정리합니다.

• API 설계와 업무 로직 및 데이터 조회 분리
• 중복 데이터베이스 조회 줄이기
• 캐시와 비동기 처리 도입

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

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• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Build Java applications that store, query, update, and delete data in Azure Cosmos DB. Covers synchronous and asynchronous clients, partition keys for distributing data, consistency settings, request usage, and error handling.

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• Implementing database operations in a Java application
• Building asynchronous data processing
• Reviewing partition keys and consistency settings

Improve slow Apache Spark jobs by reviewing data partitions, caching, memory settings, and shuffles, which move data between workers. Use the guidance to investigate bottlenecks and tune pipelines for larger datasets.

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• Investigating slow Spark jobs
• Tuning memory and executor settings
• Reducing data skew and shuffle traffic

Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.

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• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

Write complex SQL, improve slow queries, and design database schemas for transaction processing and analytics. Uses schema details and query execution plans to guide indexing, validate results, and assess performance with realistic data volumes.

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• Investigate and improve slow analytical queries
• Design a database schema for a SaaS application
• Write customer retention and cohort analysis queries

Triage proposed personal data uses to determine whether they can proceed, need a privacy impact assessment, require a legally mandated assessment, or must stop. Results explain policy conflicts, applicable triggers, and conditions to address before proceeding.

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• Checking whether a new feature needs a privacy assessment
• Reviewing proposed data uses involving a vendor
• Finding conflicts with privacy policy commitments

Develop and troubleshoot Apache Beam's YAML SDK. Understand its source files, run data processing pipelines locally or on Dataflow, test changes, and investigate provider issues or mismatched record fields.

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• Find source files for Beam YAML changes
• Run a YAML pipeline locally or on Dataflow
• Run unit tests and external service integration tests

Extract details from TikTok video URLs, including captions, author profiles, view and engagement counts, music, and hashtags. Process individual posts or a list of URLs, with image links available for slideshow posts.

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• Check views and likes for a specific video
• Archive TikTok post metadata
• Collect details from multiple video URLs

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

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Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

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Best practices for polars data processing with dataframely. Covers definitions of Schema and Collection, usage of .validate() and .filter(), type hints, and testing. Use when writing or modifying code involving dataframely or polars data frames.

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Guides the creation and review of data processing agreements under GDPR Article 28(3), covering all eight mandatory clauses. References the 2021 Standard Contractual Clauses and provides a compliance checklist for processor contracts. Activate when onboarding processors, reviewing DPAs, or auditing processor compliance. Keywords: DPA, data processing agreement, Article 28, processor, mandatory clauses, standard contractual clauses.

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GDPR-compliant Data Processing Agreement drafting per Article 28(3). Covers all 8 mandatory provisions including subject matter, duration, nature and purpose, data types, categories of data subjects, controller and processor obligations, and sub-processor cascade requirements.

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Governs employee health data processing for fitness-for-work assessments, occupational health surveillance, COVID testing legacy programmes, and absence management. Applies Art. 9(2)(b) employment obligations and Art. 9(2)(h) health professional exceptions. Covers data minimisation, occupational health provider relationships, and return-to-work procedures. Keywords: health data, Art. 9, occupational health, fitness-for-work, special category, employment, sickness absence.

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Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

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Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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