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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.
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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.
中文简介正在准备中。当前显示英语简介。
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.
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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.
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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.
中文简介正在准备中。当前显示原文说明。