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所在平台: Udemy |
课程主页: https://www.udemy.com/course/apache-spark-in-depth-spark-with-scala/
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**课程名称：** Apache Spark 深度解析 (Spark with Scala) **课程概览：** 本课程旨在帮助您从零开始深入学习 Apache Spark。由拥有成功大数据工程课程（“Hadoop 和 Spark 与 Scala”以及“Scala 编程深度解析”）的讲师授课，内容覆盖从简单的单词计数程序到批处理，再到 Spark 结构化流处理；从 Spark 应用程序的开发与部署到调试；从性能调优、优化到故障排除。无论您是想深入学习 Apache Spark 还是准备 Spark 面试，本课程都将为您提供所需的一切。课程采用非常简单的英语，易于理解。 **先修要求：** * 对 Hadoop 和 Scala 有基本了解会更好。 * 没有强制性先修要求，是学习 Apache Spark 的理想起点。 **Apache Spark 简介：** Apache Spark 是一个统一的大数据分析引擎，内置流处理、SQL、机器学习和图处理模块。 * **高速运行：** Spark 使用先进的 DAG 调度器、查询优化器和物理执行引擎，能够将工作负载的速度提升 100 倍，同时处理批处理和流式数据。 * **易于使用：** 支持 Java、Scala、Python、R 和 SQL，提供超过 80 个高级操作符，方便构建并行应用程序，并且可以通过 Scala、Python、R 和 SQL 交互式使用。 * **通用性强：** 能够无缝地将 SQL、流处理和复杂分析结合在同一应用程序中，并支持 Spark SQL 和 DataFrames、MLlib（机器学习）、GraphX（图计算）和 Spark Streaming 等库。 * **部署灵活：** 可在 Hadoop、Apache Mesos、Kubernetes、独立模式或云环境中运行，并能访问各种数据源。
Learn Apache Spark From Scratch To In-DepthFrom the instructor of successful Data Engineering courses on "Big Data Hadoop and Spark with Scala" and "Scala Programming In-Depth"From Simple program on word count to Batch Processing to Spark Structure Streaming. From Developing and Deploying Spark application to debugging. From Performance tuning, Optimization to TroubleshootingContents all you need for in-depth study of Apache Spark and to clear Spark interviews.Taught in very simple English language so any one can follow the course very easily.No Prerequisites, Good to know basics about Hadoop and ScalaPerfect place to start learning Apache SparkApache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.SpeedRun workloads 100x faster.Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.Ease of UseWrite applications quickly in Java, Scala, Python, R, and SQL.Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.GeneralityCombine SQL, streaming, and complex analytics.Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.Runs EverywhereSpark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.
