Lecture 1 – Using IPython/Jupyter Notebook and logging services to play with large-scale data analysis and visualization

Live broadcast: 20:00 — 21:00, Feb 21 (Thursday)

IPython/Jupyter Notebook is very popular, but as the volume of data grows, such as tens of billions of e-commerce platform visit logs, it is a challenge to maintain flexible interactive analysis. As an intelligent operation and maintenance platform of Ali business operating system, Ali cloud log service can quickly complete the collection, consumption, delivery, query and analysis of massive log data without development. The IPython/Jupyter extension can be used for ETL, interactive analysis (via SQL, DataFrame), machine learning and visualization in Python.

PPT download: yq.aliyun.com/download/33… Video review: yq.aliyun.com/live/875

Lecture 2 – Smooth Python data processing and big data processing ETL

Live broadcast: 20:00 — 21:00, March 6th (Wednesday)

Big data analysis often involves data normalization (ETL), and Python’s built-in powerful data structures and syntax (such as derivation, slicing, functional programming, etc.) are very data-friendly. This section describes how to use these features flexibly and smoothly to perform routine ETL operations on large-scale irregular logs in log service scenarios.

PPT download: yq.aliyun.com/download/33… Video review: yq.aliyun.com/live/910

Lecture 3 — Python3 comfort programming with compatible py2/3 practices

Live broadcast: 20:00 — 21:00, March 13 (Wednesday)

Live introduction: Python3 has a lot of “comfortable programming” features, and Python2 is coming to EOL, but the Py2/Py3 co-existence is expected to remain for some time. This section looks at some of the good things about Py3 and some of the practices on how to incorporate Py2/Py3.

PPT download: yq.aliyun.com/download/33… Video review: yq.aliyun.com/live/918

Lecture 4 — Python Concurrent programming and Real-time Big Data Processing Monitoring

Live broadcast: 20:00 — 21:00, March 20 (Wednesday)

How to do multithreaded programming in Python? How do I avoid GIL? This section uses the log service consumption group model as an example to describe related principles and practices and how to process and monitor real-time big data.

Lecture 5 — Python Logging best Practices and Cloud on Logging

Live broadcast: 20:00 — 21:00, March 27 (Wednesday)

Live introduction: Good log practices can greatly improve the efficiency of subsequent development, troubleshooting, operation, maintenance, monitoring and management. This section describes the best practices of using the Python log module to easily access the cloud and use the log service to improve product O&M efficiency.

Lecture 6 — Python module mechanisms and CLI plugins for logging Services

Live broadcast: Wednesday, April 3, 20:00 — 21:00

Live introduction: Python as a dynamic language, plug-in and module mechanism is very powerful, especially useful in writing framework class programs. This section introduces Python’s language extension capability and CLI plug-in mechanism of log service in a simple way.

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