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(Sample) Enterprise ESG Ready Investment Data Model with Streamlit Analytics

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Snowflake2023-06-26 更新2024-05-01 收录
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https://app.snowflake.com/marketplace/listing/GZT0ZYIELTE
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资源简介:
Robust Investment Data Model (IDM) ready to go link your Positions, Instruments and Accounts with ESG data from multiple sources. Out of the box FactSet ESG Snowflake Data cloud data share is enabled and a sample Streamlit application is provided to see your holdings and securities of interest data extended with FactSet's scores in minutes. Developed by DisruptivData and Exafluence, the DisruptivData Investment Model is proven, built for BI data consumption and logically organized by domain: -Instruments -Parties -Portfolios -Transactions -Positions and Position Split (tax lots, strategy) -Derivatives -Performance -Risk -Performance Attribution -Market Data -History Sample Data Share only includes Instruments, Positions, Portfolio and ESG tables. Contact DisruptivData to access the full model. The IDM offers Intra-day Cash Updates as well as Fund of Funds, FX/Cash Overlay and Derivative look-throughs. Easily extensible by your team or ours this is a full featured enterprise model/warehouse ready to use the Snowflake Data Cloud and other data sources. No upfront license fee, long term commitments or mandatory support fees offering the lowest TCO in the business. We are able to provide Metadata and Rules catalogs, Codeless Data Quality and Gold Copy capabilities as well. Structured and semi-structured data can be sourced, curated and deployed via automated data pipelines with full lineage, logging and audit in mind. Support, Migration and Initialization services are available. DisruptivData provides a new way forward without lock in leveraging the power of Snowflake and its Data Cloud ecosystem. Learn more at info@disruptivdata.com
提供机构:
DisruptivData
创建时间:
2023-06-09
搜集汇总
数据集介绍
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背景与挑战
背景概述
该数据集是一个企业级ESG投资数据模型,由DisruptivData和Exafluence开发,基于Snowflake数据云整合多源ESG数据,并提供Streamlit应用进行快速可视化分析。样本数据包含Instruments、Positions、Portfolio和ESG表,完整模型需联系获取,模型支持低总拥有成本、可扩展性及自动化数据管道。
以上内容由遇见数据集搜集并总结生成
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