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所在平台: Udemy |
课程主页: https://www.udemy.com/course/apache-spark-streaming-with-python-and-pyspark/
课程评论：没有评论
课程名称：使用Python和PySpark进行Apache Spark流处理 课程概述：本课程涵盖了使用Python进行Apache Spark流处理的基本知识，教您如何使用PySpark开发Spark流处理应用程序。通过完成本课程，您将深入了解Spark流处理及一般的大数据处理技能，帮助您的公司适应Spark流处理，构建大数据处理管道和数据分析应用程序。这门课程对于希望在数据科学领域成功的人来说至关重要。 学习内容： - Apache Spark的架构概述。 - 使用PySpark开发Apache Spark流处理应用程序，包括RDD转换、操作和Spark SQL。 - 利用弹性分布式数据集（RDDs）处理和分析大数据集。 - 通过分区、缓存和持久化RDDs来优化和调优Apache Spark作业的高级技术。 - 使用数据集和数据框分析结构化和半结构化数据，并深入理解Spark SQL。 - 扩展Spark流处理应用程序以提升带宽和处理速度。 - 将Spark流处理与集群计算工具如Apache Kafka集成。 - 将Spark流连接到数据源，如亚马逊网络服务（AWS）Kinesis。 - 在实际操作中使用Apache Spark流处理的最佳实践。 - 大数据生态系统概述。 学习Apache Spark流处理的理由：Spark流处理变得越来越流行，原因不言而喻。根据IBM的数据显示，世界上90％的数据是在过去两年内生成的。我们每天大约产出2.5 quintillion字节的数据。随着数据量的不断增加，分析静态数据框变得越来越不切实际。这就是数据流处理的意义，几乎可以在数据生成的同时进行处理，识别数据的时间依赖性。Apache Spark流处理为构建尖端应用程序提供了无限可能，同时也是过去十年对大数据世界产生重大影响的技术之一。Spark提供内存集群计算，大大提升了迭代算法和交互式数据挖掘任务的速度。同时，Spark也是一个强大的流数据处理引擎。两者的协同作用使Spark成为处理庞大数据流的理想工具。 编程语言：本课程使用Python进行教学。Python是当前世界上最流行的编程语言之一，其丰富的数据社区和工具包使其成为强大的数据处理工具。通过使用PySpark（Spark的Python API），您将能够与Apache Spark流处理的主要抽象RDDs及其他Spark组件如Spark SQL进行交互。 退款保证：您将获得Udemy提供的30天退款保证。如果在30天内不满意，请求退款，您将获得全额退款，无需说明理由。 准备好提升您的大数据分析技能和职业生涯了吗？立即报名参加这门课程！在4小时内，您将从新手变成Spark流处理专家。
What is this course about? This course covers all the fundamentals about Apache Spark streaming with Python and teaches you everything you need to know about developing Spark streaming applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Spark streaming and general big data manipulation skills to help your company to adapt Spark Streaming for building big data processing pipelines and data analytics applications. This course will be absolutely critical to anyone trying to make it in data science today. What will you learn from this Apache Spark streaming cour? In this Apache Spark streaming course, you'll learn the following: An overview of the architecture of Apache Spark.How to develop Apache Spark streaming applications with PySpark using RDD transformations and actions and Spark SQL.How to work with Spark's primary abstraction, resilient distributed datasets(RDDs), to process and analyze large data sets.Advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.Analyzing structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Spark SQL.How to scale up Spark Streaming applications for both bandwidth and processing speedHow to integrate Spark Streaming with cluster computing tools like Apache KafkaHow to connect your Spark Stream to a data source like Amazon Web Services (AWS) KinesisBest practices of working with Apache Spark streaming in the field.Big data ecosystem overview. Why should you learn Apache Spark streaming? Spark streaming is becoming incredibly popular, and with good reason. According to IBM, Ninety percent of the data in the world today has been created in the last two years alone. Our current output of data is roughly 2.5 quintillion bytes per day. The world is being immersed in data, moreso each and every day. As such, analyzing static dataframes of non-dynamic data becomes the less practical approach to more and more problems. This is where data streaming comes in, the ability to process data almost as soon as it's produced, recognizing the time-dependency of the data. Apache Spark streaming gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world. Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks. Spark also is a powerful engine for streaming data as well as processing it. The synergy between them makes Spark an ideal tool for processing gargantuan data firehoses. Tons of companies, including Fortune 500 companies, are adapting Apache Spark streaming to extract meaning from massive data streams, today you have access to that same big data technology right on your desktop. What programming language is this Apache Spark streaming course taught in? This Apache Spark streaming course is taught in Python. Python is currently one of the most popular programming languages in the world! It's rich data community, offering vast amounts of toolkits and features, makes it a powerful tool for data processing. Using PySpark (the Python API for Spark) you will be able to interact with Apache Spark Streaming's main abstraction, RDDs, as well as other Spark components, such as Spark SQL and much more! Let's learn how to write Apache Spark streaming programs with PySpark Streaming to process big data sources today! 30-day Money-back Guarantee! You will get 30-day money-back guarantee from Udemy for this Apache Spark streaming course.If not satisfied simply ask for a refund within 30 days. You will get a full refund. No questions whatsoever asked.Are you ready to take your big data analysis skills and career to the next level, take this course now!You will go from zero to Spark streaming hero in 4 hours.
