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Today a new breed of financial institutions, from banking institutions to insurance companies are turning to alternative data to augment their financial decision-making process. The emergence of third-party vendors offering access to valuable datasets including geolocation, credit card transactions, email receipts, and web scraping datasets are accelerating the adoption of this kind of alternative data in decision making.
In Part I of this webinar series, “Alternative Data Analytics with Python: How Rappers and Plant-based Meat Impact Restaurant Stocks” we covered how to ingest and optimize your alternative data sources using Delta Lake. In this webinar (Part II), you’ll learn how to build BI dashboards that provide historical and predictive analytics using SQL on Delta Lake.
This webinar will feature data from 7Park. Companies around the world depend on 7Park Data to transform data and provide insights for superior business decisions. 7Park creates analytics-ready data and performance indicators by applying machine learning and AI-powered algorithms to raw, unstructured datasets.
In this webinar, you will learn
- How to ingest 7Park datasets (email receipt data) as well as other data sources in Databricks unified analytics platform
- How to organize and optimize your Delta Lake tables for better SQL performance
- Using SQL to run text and predictive analytics at scale on news articles, websites and credit card transaction data to understand the impact of exogenous shocks to the economy.
- Integrate the data lake with a near real-time BI dashboards (on Tableau, Power BI etc) to provide the latest information to all business stakeholders.