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Computational aspects of AI for environmental sciences
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Hadoop & MapReduce
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Lessons

Intro Part I - Data science and big data analytics

2:35

Web accessible data & data publications

8:58

Pythons request library

7:27

Some hints for good data management

10:41

The netCDF file format

6:39

The role of metadata

4:00

Work with netCDF data in Python

6:32

Intro Part II - Data science and big data analytics

1:07

Types of data in Earth system science

6:29

5 "V" of Earth system data types

10:56

How to cope with > 1 TByte of data

1:33

Intro Part III - Data science and big data analytics

1:50

Challenges of large-scale data analysis and data system architectures

6:15

Data structures, data models & data patterns

4:45

Classic design patterns

14:10

Modern design patterns

15:20

Hadoop & MapReduce

6:29

Hadoop & MapReduce

Hadoop and Map (and) Reduce are two examples of modern, sophisticated designs for the asynchronous parallel processing of massive amounts of data. This section of the lecture provides an overview on the Hadoop architecture and describes the map-reduce algorithm with an example.

Lecturer

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PD Dr. Martin Schultz
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Additional Material

Additional Material

Additional Material

Git Repository with Jupyter Notes

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