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Big Data – Das Versprechen wurde eingelöst

Data Science Blog

Big Data tauchte als Buzzword meiner Recherche nach erstmals um das Jahr 2011 relevant in den Medien auf. Big Data wurde zum Business-Sprech der darauffolgenden Jahre. In der Parallelwelt der ITler wurde das Tool und Ökosystem Apache Hadoop quasi mit Big Data beinahe synonym gesetzt.

Big Data 147
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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

professionals

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Stay ahead of the curve with these 12 powerful GitHub repositories for learning data science, analytics, and engineering

Data Science Dojo

This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio.  What is GitHub? GitHub is a powerful platform for data scientists, data analysts, data engineers, Python and R developers, and more.

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Sneak peek at Microsoft Fabric price and its promising features

Dataconomy

Microsoft Fabric combines multiple elements into a single platform – Image courtesy of Microsoft The contribution of Power BI The integration of Microsoft Power BI and Microsoft Fabric offers a powerful combination for organizations seeking comprehensive data analytics and insights.

Power BI 194
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Data Mesh Architecture on Cloud for BI, Data Science and Process Mining

Data Science Blog

Data Mesh on Azure Cloud with Databricks and Delta Lake for Applications of Business Intelligence, Data Science and Process Mining. Microsoft Azure Cloud is favored by many companies, especially for European industrial companies, due to its scalability, flexibility, and industry-specific solutions.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

The trend towards powerful in-house cloud platforms for data and analysis ensures that large volumes of data can increasingly be stored and used flexibly. This aspect can be applied well to Process Mining, hand in hand with BI and AI. The creation of this data model requires the data connection to the source system (e.g.

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Top Data Analytics Skills and Platforms for 2023

ODSC - Open Data Science

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential.