Remove Artificial Intelligence Remove Data Discovery Remove Data Warehouse
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Key Highlights from Data Intelligence Day 2025 Amsterdam by Databricks

Analysts Corner

First, the workflow transitioned from ETL to ELT, allowing raw data to be loaded directly into a data warehouse before transformation. Second, they leveraged the Databricks Data Lakehouse, a unified platform combining the best features of data lakes and data warehouses to drive data and AI initiatives.

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Data fabric’s value to the enterprise

Tableau

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

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Data fabric’s value to the enterprise

Tableau

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

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Introduction To The Basic Business Intelligence Concepts

Data Pine

With our introduction to business intelligence, we’re going to dispel the myths surrounding BI, explore the core business intelligence concepts, cover the BI basics, and drill down into a mix of real-life business intelligence examples and use cases. Introduction To Business Intelligence Concepts. The data warehouse.

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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. Predictions As artificial intelligence continues to rapidly advance, its potential applications are constantly expanding.

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What is a Data Catalog? Features, Best Practices, and Benefits

Astera

While a data catalog serves as a centralized inventory of metadata, a data dictionary focuses on defining data elements and attributes, describing their meaning, format, and usage. The former offers a comprehensive view of an organization’s data assets.

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Data Wrangling Techniques: How to Clean, Transform, and Enrich Data for Analysis 

Astera

The Six Steps of Data Wrangling Data wrangling is more than just preparing data for analysis; it is a dynamic process of refining and optimizing data to uncover insights. If you are new to data wrangling, it can be overwhelming to know where to start.