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Data Abstraction for Data Engineering with its Different Levels

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A data model is an abstraction of real-world events that we use to create, capture, and store data in a database that user applications require, omitting unnecessary details.

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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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Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Data Science Dojo

Top Employers Microsoft, Facebook, and consulting firms like Accenture are actively hiring in this field of remote data science jobs, with salaries generally ranging from $95,000 to $140,000. Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes.

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Data Modeling Fundamentals in Power BI

phData

While the front-end report visuals are important and the most visible to end users, a lot goes on behind the scenes that contribute heavily to the end product, including data modeling. In this blog, we’ll describe data modeling and its significance in Power BI. What is Data Modeling?

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Visualizing graph data without a graph database

Cambridge Intelligence

Visualizing graph data doesn’t necessarily depend on a graph database… Working on a graph visualization project? You might assume that graph databases are the way to go – they have the word “graph” in them, after all. Do I need a graph database? It depends on your project. Unstructured?

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How to choose a graph database: we compare 6 favorites

Cambridge Intelligence

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. There have been a lot of new entrants and innovations in the graph database category, with some vendors slowly dipping below the radar, or always staying on the periphery.

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Knowledge Graph QA using Gemini and NebulaGraph Lite

Towards AI

Graph databases and knowledge graphs are among the most widely adopted solutions for managing data represented as graphs, consisting of nodes (entities) and edges (relationships). Knowledge graphs extend the capabilities of graph databases by incorporating mechanisms to infer and derive new knowledge from the existing graph data.

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