| Lesson Code | Course Name | Class | Credit | Lesson Time | Weekly Lesson Hours (Theoretical) | Weekly Lesson Hours (Practice) | Weekly Class Hours (Laboratory) |
|---|---|---|---|---|---|---|---|
| UDOSZhA 4214 | Development Of Big Data Processing And Storage Systems | төртінші курс | 5 | 150 | 1 | 2 | 2 |
The subject is to teach students the skills of finding and interpreting big data sources, managing large amounts of data, combining data sources, ensuring the coherence of data sets and creating visualization that helps to understand data, building mathematical models using data, as well as practical work in the field of prerequisite: Post-Requisite: in particular, in the field of natural language processing.
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Algorithms and data structures, database theory
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. |