Organizations are increasingly overwhelmed by the vast amounts of structured and unstructured data from various sources, making traditional BI tools inadequate for effective analytics. As users demand quicker insights, the pressure on IT teams escalates, highlighting the urgent need for data democratization. A semantic lakehouse emerges as a solution, combining the organizational structure of a data warehouse with the vastness of a data lake. By implementing a semantic layer that clarifies metrics and business rules, organizations can enhance trust in data and improve operational efficacy, ultimately facilitating better decision-making and business intelligence.
Organizations today generate large volumes of structured and unstructured data, leading to a challenge in analytics as traditional BI tools struggle to keep pace.
The need for data democratization places an ongoing burden on IT and data teams, necessitating a shift towards a more capable architectural approach for modern data.
A semantic lakehouse organizes and catalogs data like a bookstore while providing a unified semantic layer for better scalability and performance in analytics.
The semantic layer within a data lake centralizes and standardizes metrics, hierarchies, and business rules, enhancing users' trust in data and operational efficiency.
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