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The Future of AI in Data Warehousing: Trends and Predictions 

Astera

Data management can be a daunting task, requiring significant time and resources to collect, process, and analyze large volumes of information. AI is a powerful tool that goes beyond traditional data analytics. Smart Data Modeling Another trend in data warehousing is the use of AI-powered tools for smart data modeling.

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Introduction To The Basic Business Intelligence Concepts

Data Pine

Business intelligence concepts refer to the usage of digital computing technologies in the form of data warehouses, analytics and visualization with the aim of identifying and analyzing essential business-based data to generate new, actionable corporate insights. They enable powerful data visualization. 1) The raw data.

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Cloud Data Warehouse: A Comprehensive Guide

Astera

What is a Cloud Data Warehouse? Simply put, a cloud data warehouse is a data warehouse that exists in the cloud environment, capable of combining exabytes of data from multiple sources. A cloud data warehouse is critical to make quick, data-driven decisions.

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What is Data Pipeline? A Detailed Explanation

Smart Data Collective

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

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16 Best Business Intelligence Books To Get You Off the Ground With BI

Data Pine

This growth brings a wealth of insight, and if you’re BI-savvy, you’ll be able to squeeze every single drop of value from the mobile data available at your fingertips. Expanding on our previous point, presently, 42% of companies plan to deploy mobile-based BI as part of their growth strategy. click for book source**.

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What Is Data Management and Why Is It Important?

Astera

Free Download Here’s what the data management process generally looks like: Gathering Data: The process begins with the collection of raw data from various sources. Once collected, the data needs a home, so it’s stored in databases, data warehouses , or other storage systems, ensuring it’s easily accessible when needed.

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Should You Have Separate Document, Time-Series, NoSQL and SQL Databases or Can a Single Database Support All of These Data Types and Requirements?

Actian

At one time, data was largely transactional and Online Transactional Processing (OLTP) and Enterprise resource planning (ERP) systems handled it inline, and it was heavily structured. They are generating the entire range of structured and unstructured data, but with two-thirds of it in a time-series format.