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In der Parallelwelt der ITler wurde das Tool und Ökosystem ApacheHadoop quasi mit Big Data beinahe synonym gesetzt. 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.
For frameworks and languages, there’s SAS, Python, R, ApacheHadoop and many others. The popular tools, on the other hand, include PowerBI, ETL, IBM Db2, and Teradata. SQL programming skills, specific tool experience — Tableau for example — and problem-solving are just a handful of examples.
Dashboards, such as those built using Tableau or PowerBI , provide real-time visualizations that help track key performance indicators (KPIs). Big data platforms such as ApacheHadoop and Spark help handle massive datasets efficiently. Data Scientists require a robust technical foundation.
These frameworks facilitate the efficient processing of Big Data, enabling organisations to derive insights quickly.Some popular frameworks include: ApacheHadoop: An open-source framework that allows for distributed processing of large datasets across clusters of computers. It is known for its high fault tolerance and scalability.
These frameworks facilitate the efficient processing of Big Data, enabling organisations to derive insights quickly.Some popular frameworks include: ApacheHadoop: An open-source framework that allows for distributed processing of large datasets across clusters of computers. It is known for its high fault tolerance and scalability.
Data Visualization: Matplotlib, Seaborn, Tableau, etc. Big Data Technologies: Hadoop, Spark, etc. ETL Tools: Apache NiFi, Talend, etc. Big Data Processing: ApacheHadoop, Apache Spark, etc. Excel, Tableau, PowerBI, SQL Server, MySQL, Google Analytics, etc.
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