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AI DATA CLOUDFUNDAMENTALS
Learn about some of the most relevant topics around cloud data warehousing, AI, data lakes, data engineering and other areas of interest related to cloud data analytics, AI and cloud data platforms.
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What Is Data Governance?
Data governance is a structured, organizational approach to managing, organizing & controlling data asset and includes compliance, stewardship & data security.
What Is RAG (Retrieval-Augmented Generation)? A Full Guide
Discover how to build and deploy retrieval-augmented generation (RAG) apps for customer service, sales, marketing, and more using Snowflake's managed service.
AI in Business Intelligence: Benefits and Use Cases
AI augments BI so any user can analyze data without needing to be proficient in writing SQL, the language of analytics and databases where data is stored.
What Is Document Processing? A Complete Guide
Learn how text and document processing tools help to easily analyze and gain insights from large volumes of text while saving time and resources.
Data Analytics for Manufacturing: Key Use Cases
Manufacturers are under constant pressure to do more with less — streamline operations, cut costs and meet evolving customer expectations. With modern data analytics, manufacturers can unlock hidden efficiencies across the supply chain, enhance production quality and make smarter, faster decisions powered by real-time insights.
How AI Is Shaping the Future of Investment Banking
Artificial intelligence is redefining investment banking by unlocking new efficiencies and decision-making capabilities across the industry. From personalized client insights to real-time risk forecasting, AI technologies — especially generative AI — are enabling banks to turn vast data stores into competitive advantage at unprecedented speed and scale.
The Role of Feature Extraction in Machine Learning
Feature extraction is a core component of modern machine learning workflows. By isolating and transforming the most relevant variables in a dataset, it helps reduce noise, improve model accuracy and make more efficient use of compute resources.
Building a Scalable Data Ingestion Framework
A well-designed data ingestion framework forms the foundation of scalable data architecture, enabling businesses to collect, move and prepare data for analysis at speed and scale.
What Is Online Analytical Processing (OLAP)? Use Cases and Examples
Designed for speed, scalability and flexibility, OLAP helps teams explore data across multiple dimensions to uncover trends, outliers and actionable insights — powering smarter decisions in today’s data-driven organizations.
How Predictive Analytics Drives Smarter Marketing
Artificial intelligence is redefining investment banking by unlocking new efficiencies and decision-making capabilities across the industry. From personalized client insights to real-time risk forecasting, AI technologies — especially generative AI — are enabling banks to turn vast data stores into competitive advantage at unprecedented speed and scale.
Scala vs Java: What’s the Difference?
Scala and Java are two powerful languages widely used in big data development. While Java is known for its stability and simplicity, Scala offers concise syntax and functional programming features that make it ideal for scalable, data-intensive applications. Rather than choosing one over the other, developers often combine the strengths of both to streamline complex data workflows.
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