9건 · 관련도순
Automate Big Data Cloud operations through Rube MCP, a connection for agent tools. The skill finds available actions, checks their required inputs and connection status, and executes them using Composio's toolkit.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Automate Big Data Cloud operations through Rube MCP, a connection for agent tools. The skill finds available actions, checks their required inputs and connection status, and executes them using Composio's toolkit.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).
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Analyze large-scale construction datasets. Process thousands of projects for patterns, benchmarks, and predictive insights.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apache Spark is a distributed engine for batch, SQL and streaming data processing, used from Python through PySpark. Use when a user asks to process big data, run distributed computations, build ETL pipelines, analyse data at scale, read Parquet from S3, or stream from Kafka with PySpark.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
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Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.
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Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads
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Automate Big Data Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.