Python Celery for Distributed Tasks and Parallel Programming

Python Celery for Distributed Tasks and Parallel Programming
Video: .mp4 (1280×720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 552 MB

Learn how to Analyze data using Pandas and Spark and how to create background tasks using Celery framework and RabbitMQ

What you’ll learn

Pandas and spark for data analysis
Using Queus for parallel programing
Python celery as pipeline framework
Using Kafka JDBC Connector with Oracle DB

Requirements

Basic knowledge of python and SQL

Description

The aim of this course is learning programming techniques to process and analyze data

Data Analysis

Will learn how to load from different data sources (database/files) into data frames, Will learn both pandas data frame and spark date frame

Will learn how to analyze the data (select, filtering, grouping and sorting) These operations will be done without hit the database which Make our analysis extremely faster

Parallel Programming

Insert the functions that need to be called as tasks in queues to be executed in the background while the main program still

This will enhance the performance, as the functions will be executed in parallel not in sequence as traditional programming executing.

Who this course is for:

Python and Oracle developers and students

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