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Computational aspects of AI for environmental sciences
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Modern design patterns
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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

Modern design patterns

Here, we discuss some modern design patterns for data management with particular focus on distributed architectures. Key concepts that are introduced in this section with examples are asynchronous processing, caching, messaging, and sharding. These are important concepts to allow for parallel data processing in heterogeenous environments.

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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