Search Results: etl-pipelines

Found 19 Skills

Data Processinggemini-cli-extensions/dat...

gcp-spark

Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.

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Data Processingaradotso/data-skills

harvard-artifacts-data-engineering-analytics

Build ETL pipelines and analytics dashboards for Harvard Art Museums API data using Python, SQL, and Streamlit

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Data Processingaradotso/data-skills

harvard-artifacts-data-engineering-pipeline

Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit

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Data Processingletta-ai/skills

multi-source-data-merger

This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.

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Data Processingaradotso/data-skills

harvard-artifacts-etl-pipeline

Build ETL pipelines and analytics dashboards for Harvard Art Museums API data with MySQL storage and Streamlit visualization

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Data Processingaradotso/data-skills

harvard-artifacts-etl-analytics

Build ETL pipelines and analytics dashboards using Harvard Art Museums API with SQL and Streamlit

🇺🇸|EnglishTranslated

Data Processingaradotso/data-skills

harvard-artifacts-collection-etl-analytics

Build end-to-end ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit

🇺🇸|EnglishTranslated