Big Data

Syllabus

Within the course, the content will focus on the following three areas: • Introduction to the problem of big data. Current challenges, trends and applications. It also includes topics such as the history of big data, their elements, types, advantages, disadvantages, etc. Definition of big data, enterprise / structured data, social / unstructured data, unstructured data for analytical services, which are large data sets, sources of big data, industries using big data, challenges we face in the field of big data. Use of big data in enterprises and businesses. A Big Data application perspective that covers topics such as the use of big data in marketing, analysts, retail, healthcare, consumer goods, defense, government, and so on. • Algorithms for analyzing big data. Knowledge mining algorithms and UIs that have been developed specifically to solve the problems of processing big data. Data mining algorithms for big data and data streams. • Technologies for managing big data. Big Data technologies and tools, with special emphasis on the Map-Reduce paradigm and the Hadoop ecosystem. This area covers such topics as the introduction to Hadoop, the operation of Hadoop, Cloud computing (features, benefits, applications). Understanding the Hadoop and its ecosystem, which includes HDFS, MapReduce, YARN, HBase, Hive, Pig, Sqoop, Zookeeper, Flume, Oozie, etc. The basics of MapReduce and HBase emphasize the creation of a simple mapreduce framework and the concepts that apply to it. This area also covers the stack of large data files, i. data source layer, receive layer, source layer, security layer, visualization layer, visualization approaches, etc. This area also covers information about NoSQL data management systems, including document databases, relationships, graph databases, schema-free databases, and so on