Remove Data Discovery Remove Data Modelling Remove Data Warehouse Remove Innovation
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The Future of AI in Data Warehousing: Trends and Predictions 

Astera

By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes.

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Data Vault vs. Data Mesh: Choosing the Right Data Architecture?

Astera

A solid data architecture is the key to successfully navigating this data surge, enabling effective data storage, management, and utilization. Enterprises should evaluate their requirements to select the right data warehouse framework and gain a competitive advantage.

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Data fabric’s value to the enterprise

Tableau

At Tableau, we’re leading the industry with capabilities to connect to a wide variety of data, and we have made it a priority for the years to come. Connector library for accessing databases and applications outside of Tableau regardless of the data source (data warehouse, CRM, etc.)

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Data fabric’s value to the enterprise

Tableau

At Tableau, we’re leading the industry with capabilities to connect to a wide variety of data, and we have made it a priority for the years to come. Connector library for accessing databases and applications outside of Tableau regardless of the data source (data warehouse, CRM, etc.)

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What Is Embedded Analytics?

Insight Software

CEO Priorities Grow revenue and “hit the number” Manage costs and meet profitability goals Attract and retain talent Innovate and out-perform the competition Manage risk Connect the Dots Present embedded analytics as a way to differentiate from the competition and increase revenue.