| Lesson Code | Course Name | Class | Credit | Lesson Time | Weekly Lesson Hours (Theoretical) | Weekly Lesson Hours (Practice) | Weekly Class Hours (Laboratory) |
|---|---|---|---|---|---|---|---|
| UDOSZhA 4214 | Large Data Processing (Big DATA) | төртінші курс | 5 | 150 | 1 | 2 | 2 |
The discipline provides an opportunity to get acquainted with the basic concepts in the field of analytical processing of big data. It outlines the basics of machine learning, visualization and storage of big data. Based on the results of the course, the student will be able to translate the problems of the subject area into the language of big data processing technologies. In the course of the study, ideas on technical and methodological tools for analyzing big data will be formed.
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Data base and Information Systems
Narrative, exchange of views, discussion, problem methods.
| 1 | Learns to analyze large amounts of information and organize data management. |
| 2 | Implemented using the latest data processing and analysis Technologies. |
| 3 | Will be able to create new models of the organization's information infrastructure, taking into account the capabilities of Big Data Technology. |
| 4 | Intensively learns theoretical and practical aspects in the field of data analysis. |
| 5 | Can develop and design various components of 5 - Distance databases and Information Systems. |
| 6 | Uses and builds internet applications. |
| Haftalık Konu | Evaluation Method | |
|---|---|---|
| 1 | Description of big data. The importance of data. | |
| 2 | Big data tools and their function. | |
| 3 | Basic data about distributions. Advantages and differences of Apache, Cloudera, Horton Works. | |
| 4 | Hadoop architecture. | |
| 5 | Description of the platform. Components of the' HADOOP ' ecosystem. | |
| 6 | The basic principles of Hadoop. Hadoop components. Hadoop 2.0. | |
| 7 | Ways MapReduce functions work with big data. MapReduce-algorithmization in the form of a graph. | |
| 8 | Technology of operation of Hive, Pig components. Execution of requests in components. | |
| 9 | Infrastructure and structural data generation via Hive. | |
| 10 | Framework for scaling Big Data Solutions: RDBMS, NoSQL and HBase. | |
| 11 | Large-scale solutions using real data. MongoDB. | |
| 12 | Data analytics and visualization. Processing specific languages: Apache Kafka. | |
| 13 | Solutions in Apache Falcon and Ozie components. Technologies for the operation of Spark and Storm components | |
| 14 | Machine learning library-Mahout's algorithm for working. Mahout clustering. | |
| 15 | Demonstration of the relationship between big data and artificial intelligence through the 'HADOOP' ecosystem. |
| PÇ1 | PÇ2 | PÇ3 | PÇ4 | PÇ5 | PÇ6 | PÇ7 | PÇ8 | PÇ9 | PÇ10 | PÇ11 | PÇ12 |
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| Textbook / Material / Recommended Resources | ||
|---|---|---|
| 1 | Исахметов Т.И., Шадиева А.А., Жаздыкбаева Д.П., Big Data технологиялар. Алматы -2022. | |
| 2 | A. K. Mukasheva, T. F. Umarov, I. A. Zimin, Big data analytics. Textbook, Almaty, 2022. | |
| 3 | Деректер қоры жүйелері Нур-Принт Алматы, 2012ж. Оқу-әдістемелік құрал | |
| 4 | Технологии и инфраструктура BIG DATA, И. А. Радченко, И. Н. Николаев, 2018, ИТМО, учебник, СПб.52 | |
| 5 | Силен, Д. и др. Основы Data Science и Big Data. Python и наука о данных. / Д. Силен, А. Мейсман, М. Али. |