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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bigdata-hadoop-and-pyspark-in-telugu/
课程评论：没有评论
**课程名称:** BigData Hadoop and PySpark full course in Telugu (తెలుగులో) **课程概述:** 本课程旨在帮助您顺利转型进入大数据领域，专注于Hadoop和Spark技术的学习。完成本课程后，您将能够深入理解Hadoop、HDFS、YARN、MapReduce、Hive、Sqoop、Linux以及PySpark、Spark SQL和PySpark Streaming等关键技术。这门课程是“一站式”学习方案，方便您快速入门。讲师将提供全程支持，并欢迎随时通过平台提问。所有教学程序和材料均已提供。 **关于Hadoop生态系统和Spark:** * **Hadoop及生态系统:** Hadoop是一个开源框架，用于分布式存储和处理大规模数据集。其核心组件包括用于数据存储的Hadoop分布式文件系统（HDFS）和用于数据处理的MapReduce编程模型。Hadoop的生态系统包含一系列工具和框架，旨在拓展其功能。重要的组件包括用于数据脚本的Apache Pig、用于数据仓库的Apache Hive、用于NoSQL数据库功能的Apache HBase以及用于快速内存数据处理的Apache Spark。这些工具共同构建了一个强大的生态系统，使组织能够高效应对大数据挑战，使Hadoop成为数据分析和处理领域的重要基石。 * **Spark:** Apache Spark是一个开源的、极速（lightning-fast）的数据处理框架，专为大数据分析而设计。它提供内存处理能力，显著加速数据分析和机器学习任务。Spark支持Java、Scala和Python等多种编程语言，使其能够被广泛的开发者群体所使用。凭借处理批处理和流式数据的能力，Spark已成为追求高性能数据分析和机器学习能力的组织的优选，在许多用例中其性能优于传统的基于MapReduce的解决方案。 **学习目标:** * 掌握Hadoop核心概念和组件（HDFS, YARN, MapReduce）。 * 熟悉Hadoop生态系统中的重要工具（Hive, Sqoop）。 * 了解Linux基础知识，为大数据环境做准备。 * 熟练运用PySpark进行数据处理和分析。 * 学习Spark SQL进行结构化数据查询。 * 掌握PySpark Streaming处理实时数据流。 **课程特点:** * 提供详尽的Hadoop和Spark知识体系。 * 重点关注PySpark在实际应用中的使用。 * 全 Telugu 授课，方便本地语言学习者。 * 提供所有必要的程序和学习材料。 * 讲师提供全程支持和答疑。
This course prepares you for a career change in Big Data Hadoop and Spark.After watching it, you will understand Hadoop, HDFS, YARN, Map reduce, hive, sqoop, Linux, PySpark, Spark sql, PySpark streaming.This is a one stop course. So don't worry and get started.You will get all possible support from my side.For any queries, feel free to message me here.Note: All programs and materials are provided.About Hadoop Ecosystem and Spark:Hadoop and its Ecosystem: Hadoop is an open source framework for distributed storage and processing of large data sets. Its core components include the Hadoop Distributed File System (HDFS) for data storage and the MapReduce programming model for data processing. Hadoop's ecosystem consists of various tools and frameworks designed to enhance its capabilities. Important components include Apache Pig for data scripting, Apache Hive for data warehousing, Apache HBase for NoSQL database functionality, and Apache Spark for fast, in-memory data processing. These tools collectively form a robust ecosystem that enables organizations to efficiently tackle big data challenges, making Hadoop a cornerstone in the world of data analytics and processing.Spark: Apache Spark is an open source, lightning-fast data processing framework designed for big data analytics. It provides in-memory processing that significantly speeds up data analysis and machine learning tasks. Spark supports a variety of programming languages, including Java, Scala, and Python, making it accessible to a wide range of developers. With the ability to process both batch and streaming data, Spark has become the preferred choice for organizations seeking high-performance data analytics and machine learning capabilities, outperforming traditional MapReduce-based solutions in many use cases.
