O TYM PODKAŚCIE 🔗
The amount of data that can be generated and stored in academic and industrial projects and applications is increasing rapidly. Big data analytics technologies have established themselves as a solution for big data challenges to the scalability problems of traditional database systems. The vast amounts of new data that is collected, however, usually is not as easily analyzed as curated, structured data in a data warehouse is. Typically, these data are noisy, of varying format and velocity, and need to be analyzed with techniques from statistics and machine learning rather than pure SQL-like aggregations and drill-downs. Moreover, the results of the analyzes frequently are models that are used for decision making and prediction. The complete process of big data analysis is described as a pipeline, which includes data recording, cleaning, integration, modeling, and interpretation.
In this lecture, we will discuss big data systems, i.e., the infrastructures that are used to handle all steps in typical big data processing pipelines.
In this lecture, we will discuss big data systems, i.e., the infrastructures that are used to handle all steps in typical big data processing pipelines.
Częstotliwość aktualizacji:
every 5 days
Średnia długość dźwięku:
84 minutes
Podcast wideo
Język angielski
Stany Zjednoczone
20 odcinków
od 14 października 2025
episodic
NAJNOWSZY ODCINEK 🔗
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POPRZEDNIE ODCINKI 🔗
Zastrzeżenie: Podcast i grafika osadzone na tej stronie pochodzą z Prof. Dr. Tilmann Rabl, który jest własnością jego właściciela i nie jest powiązany ani wspierany przez Listen Notes, Inc.
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