Results for “large-files”
17 skills
matlab matlab-use-duckdb
Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.
920 · bundle
nvidia cupynumeric-hdf5
Read and write large cuPyNumeric arrays to HDF5 files using Legate's parallel, distributed HDF5 I/O.
2.2k · bundle
levalencia vaex
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.
3 · bundle
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jackychenlu vaex
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 don't fit in memory.
0 · bundle
neuralblitz big-data
Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.
1
leandrobenjaminl file-formats
Lee y escribe datos en múltiples formatos con Pandas — CSV, Excel, Parquet, JSON, Feather — y elige el formato óptimo según el caso.
0
google bigtable-basics
Provision Bigtable instances, design performant schemas, and query data using gcloud, cbt, or client libraries.
14.4k · bundle
machenjie bigdata-product-extension
For analysis/task/review agents using a Professional Skill on batch, stream, warehouse, lineage, replay, or data quality; not for transactions without distributed-data impact.
4 · bundle
mukul975 analyzing-macro-malware-in-office-documents
Extracts and analyzes malicious VBA macros, XLM macros, DDE, and remote template injections in Microsoft Office documents using olevba, oledump, and deobfuscation techniques to identify download cradles, payload execution, and persistence mechanisms.
24.6k · bundle
orchestra-research ray-data
Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.
10.4k · bundle
scoheart firecrawl-crawl
Bulk extract content from an entire website or site section by crawling pages that follow links, with configurable depth, path filters, and concurrency.
2
orchestra-research faiss
Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.
10.4k · bundle
metinduraktr-44 vaex
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 don't fit in memory.
0 · bundle
chen-yu-hao vaex
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 don't fit in memory.
5 · bundle
gtynnn060110-hash file-operations
Guidance on reading, parsing, and processing various file formats (CSV, JSON, YAML) commonly used in legal and compliance tasks
6 · bundle
mariadb-corporation mariadb-schema-management-develop
Develop the next version of a MariaDB Schema Management (MSM) schema by editing the development script and splitting large sections into SOURCE-included files.
0
alterlab-ieu alterlab-vaex
Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.
60 · bundle