What Is Data Ingestion? Full Guide 2025
Explore data ingestion, including its process, types, architecture and leading tools to efficiently collect, prepare and analyze data in 2025.
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.
90 Results
Newest - Oldest
Filter
What Is a Machine Learning (ML) Model? Full Guide
Learn what ML models are and how machine learning works. Explore types of machine learning models, see common algorithms and review real-world examples.
What is a Data Warehouse? A Complete Guide
Explore what makes data warehouses unique, the components that go into creating them and how they offer organizations a competitive advantage.
How to Implement a Data Governance Strategy in 2025
Learn how to create and implement an effective data governance strategy with practical steps, examples and best practices.
A Guide to Data Classification for Security and Governance
Discover the benefits of data classification. Learn about data classification levels, explore examples and follow best practices for your own policy.
A Guide to Automated Data Processing
Explore how ADP works and how your organization can use it to save time, reduce costs and maintain an edge against the competition.
Cloud Integration: Everything You Need To Know
Learn about cloud integration, including its process, types, solutions and benefits to optimize data flow and improve efficiency across cloud platforms.
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.
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.
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.
What Is Generative AI and How Is It Used Today?
Generative AI is ushering in a new era of creativity, automation and problem-solving. Unlike traditional AI systems that focus on classification or prediction, generative AI creates entirely new content — text, images, code, even product designs — based on the patterns it learns from existing data.
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.
1
2
3
4
5
...
8
