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Analytics databases

Dataconomy

Analytics databases play a crucial role in driving insights and decision-making in today’s data-driven world. By providing a structured way to analyze historical data, these databases empower organizations to uncover trends and patterns that inform strategies and optimize operations. What are analytics databases?

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Object-oriented database management system (OODBMS)

Dataconomy

An Object-Oriented Database Management System (OODBMS) represents a striking shift in how data is organized and manipulated, aligning closely with the principles of object-oriented programming. What is an object-oriented database management system (OODBMS)? Object identity: Provides unique identifiers for database objects.

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Structured data

Dataconomy

This type of data maintains a clear structure, usually in rows and columns, which makes it easy to store and retrieve using database systems. Definition and characteristics of structured data Structured data is typically characterized by its organization within fixed fields in databases.

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Entity

Dataconomy

In technology and business, entities often represent either real objects or abstract concepts, allowing clarification in data modeling and communication. Definition of an entity At its core, an entity signifies a distinct unit with independent existence. The term itself stems from the Latin word “ens,” meaning being.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

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Canonical schema

Dataconomy

As organizations increasingly rely on diverse databases and applications, maintaining a consistent model for data interchange becomes essential. This consistency is crucial not only for seamless integration but also for sustaining data integrity across different platforms. What is canonical schema?

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. Tool use is a powerful feature in Amazon Bedrock that allows models to access external tools or functions to enhance their response generation capabilities.

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