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所在平台: Udemy |
课程主页: https://www.udemy.com/course/databricks-stream-processing-with-pyspark/
课程评论：没有评论
课程名称：15天内掌握PySpark的Databricks流处理 课程概述： 在当今数据驱动的世界中，实时流处理对于软件工程师、数据架构师和数据工程师而言是至关重要的技能。此课程专为学习者提供使用Apache Spark、Databricks Cloud和PySpark API进行实时数据流处理的实践经验。不论您是初学者还是经验丰富的专业人士，本课程将为您提供在Databricks上构建实时数据处理管道所需的实用知识和技能，充分利用Apache Spark结构化流处理实现高性能的数据处理。 通过现场编码的方式，您将深入了解流处理架构、消息队列、事件驱动应用以及现实世界的数据处理场景。 学习实时流处理的原因： 随着物联网设备、金融交易、社交媒体平台和电子商务网站等生成海量数据的企业增多，实时流处理技术日益成为关键技术。企业需要即时洞察和决策，而Apache Spark结构化流处理是高效处理大规模流数据的最佳工具。随着Lakehouse架构的崛起和Databricks等平台的兴起，企业正在向统一的数据分析迈进，该架构能够实时处理结构化和非结构化数据。本课程确保您掌握流处理技术，构建可伸缩、容错的流处理应用程序，在行业中保持领先。 您将学习的内容： 该课程以案例为驱动，教授实时流处理，课程内容包括： - 流处理基础：实时流处理及其用例简介，批处理与流处理数据的理解，Apache Spark结构化流处理概述，Databricks Cloud和Lakehouse架构的核心组件。 - Apache Spark与Databricks入门：设置Databricks工作区进行实时流处理，理解Databricks Runtime和优化的Spark执行，使用Delta Lake和Databricks文件系统管理数据。 - 使用PySpark构建实时流处理管道：PySpark API的流处理介绍，利用Kafka、事件中心和Azure存储进行数据摄取，实现实时数据转换和聚合，将流数据写入Delta Lake和其他存储格式，处理延迟到达的数据和水印。 - 优化Databricks上的流处理性能：调整Spark结构化流处理应用以降低延迟，实现检查点和状态处理，理解容错和恢复策略。 - 将流处理与Databricks生态系统集成：使用Databricks SQL进行实时分析，连接Power BI、Tableau和其他可视化工具，使用Databricks工作流自动化实时数据管道，使用Databricks作业部署流处理应用。 - 毕业项目：从头设计一个实时数据处理管道，实现从Kafka或事件中心进行数据摄取，使用PySpark转换处理流数据，使用Delta Lake和Databricks SQL存储和分析实时洞察，通过Databricks工作流和CI/CD管道部署解决方案。 适合人群： 本课程适合： - 希望开发可扩展实时应用程序的软件工程师。 - 设计和构建企业级流处理管道的数据工程师和架构师。 - 希望处理实时数据以进行AI/ML模型的机器学习工程师。 - 使用Kafka、Flink或Spark等流处理框架的大数据专业人士。 - 监督实时数据实施的经理和解决方案架构师。 选择本课程的理由： 本课程采用实践动手的方法设计，确保您不仅学习概念，还能够在现实场景中实施。 - 现场编码课程：通过一步一步的实现来学习。 - 现实案例：将知识应用于与行业相关的示例。 - 针对Databricks进行优化：部署流处理应用的最佳实践。 - 毕业项目：获得构建端到端流处理管道的实践经验。 技术栈与环境： 本课程使用最新技术构建： - Apache Spark 3.5：最强大的结构化流处理版本。 - Databricks Runtime 14.1：云上的优化Spark性能。 - Azure Databricks：可扩展的无服务器数据分析。 - Delta Lake：结构化流的可靠存储。 - Kafka与事件中心：实时消息和事件驱动架构。 - CI/CD管道：高效部署实时应用。 现在报名，开始您的实时数据流处理之旅！ 通过本课程的学习，您将能够自信地构建、部署和管理使用Apache Spark结构化流处理的实时流处理应用。在您的职业中迈出下一步，今天就掌握实时流处理技术。
Course OverviewIn today's data-driven world, real-time stream processing is a crucial skill for software engineers, data architects, and data engineers. This course, Apache Spark and Databricks - Stream Processing in Lakehouse, is designed to equip learners with hands-on experience in real-time data streaming using Apache Spark, Databricks Cloud, and the PySpark API.Whether you're a beginner or an experienced professional, this course will provide you with the practical knowledge and skills needed to build real-time data processing pipelines on Databricks, utilizing Apache Spark Structured Streaming for high-performance data processing.With a live coding approach, you'll gain deep insights into streaming architecture, message queues, event-driven applications, and real-world data processing scenarios.Why Learn Real-Time Stream Processing?Real-time stream processing is becoming a critical technology for businesses handling vast amounts of data generated by IoT devices, financial transactions, social media platforms, e-commerce websites, and more. Companies need instant insights and decisions, and Apache Spark Structured Streaming is the best tool for handling large-scale streaming data efficiently.With the rise of Lakehouse Architecture and platforms like Databricks, enterprises are moving towards unified data analytics where structured and unstructured data can be processed in real time. This course ensures that you stay ahead in the industry by mastering streaming technologies and building scalable, fault-tolerant stream processing applications.What You'll Learn?This course takes an example-driven approach to teach real-time stream processing. Here's what you'll learn:Foundations of Stream Processing- Introduction to real-time stream processing and its use cases- Understanding batch vs. streaming data processing- Overview of Apache Spark Structured Streaming- Core components of Databricks Cloud and Lakehouse ArchitectureGetting Started with Apache Spark & Databricks- Setting up a Databricks workspace for real-time streaming- Understanding Databricks Runtime and optimized Spark execution- Managing data with Delta Lake and Databricks File System (DBFS)Building Real-Time Streaming Pipelines with PySpark- Introduction to PySpark API for streaming- Working with Kafka, Event Hubs, and Azure Storage for data ingestion- Implementing real-time data transformations and aggregations- Writing streaming data to Delta Lake and other storage formats- Handling late-arriving data and watermarking- Optimizing Streaming Performance on Databricks- Tuning Spark Structured Streaming applications for low latency- Implementing checkpointing and stateful processing- Understanding fault tolerance and recovery strategies- Using Databricks Job Clusters for real-time workloadsIntegrating Stream Processing with Databricks Ecosystem- Using Databricks SQL for real-time analytics- Connecting Power BI, Tableau, and other visualization tools- Automating real-time data pipelines with Databricks Workflows- Deploying streaming applications with Databricks JobsCapstone Project - End-to-End Real-Time Streaming Application- Design a real-time data processing pipeline from scratch- Implement data ingestion from Kafka or Event Hubs- Process streaming data using PySpark transformations- Store and analyze real-time insights using Delta Lake & Databricks SQL- Deploy your solution using Databricks Workflows & CI/CD PipelinesWho Should Take This Course?This course is perfect for:- Software Engineers who want to develop scalable, real-time applications.- Data Engineers & Architects who design and build enterprise-level streaming pipelines.- Machine Learning Engineers looking to process real-time data for AI/ML models.- Big Data Professionals who work with streaming frameworks like Kafka, Flink, or Spark.- Managers & Solution Architects who oversee real-time data implementations.Why Choose This Course?This course is designed with a practical, hands-on approach, ensuring you not only learn the concepts but also implement them in real-world scenarios.- Live Coding Sessions - Learn by doing, with step-by-step implementations.- Real-World Use Cases - Apply your knowledge to industry-relevant examples.- Optimized for Databricks - Best practices for deploying streaming applications on Azure Databricks.- Capstone Project - Get hands-on experience building an end-to-end streaming pipeline.Technology Stack & EnvironmentThis course is built using the latest technologies:- Apache Spark 3.5 - The most powerful version for structured streaming.- Databricks Runtime 14.1 - Optimized Spark performance on the cloud.- Azure Databricks - Scalable, serverless data analytics.- Delta Lake - Reliable storage for structured streaming.- Kafka & Event Hubs - Real-time messaging and event-driven architecture.- CI/CD Pipelines - Deploying real-time applications efficiently.Enroll Now & Start Your Journey in Real-Time Data Streaming!By the end of this course, you will be confident in building, deploying, and managing real-time streaming applications using Apache Spark Structured Streaming on Databricks Cloud.Take the next step in your career and master real-time stream processing today.
