Data Engineering, Big Data, and Machine Learning on GCP Specialization

开始时间: 06/21/2022 持续时间: Approximately 4 months to complete Suggested pace of 4 hours/week

所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew

课程主页: https://www.coursera.org/specializations/gcp-data-machine-learning

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This online specialization provides participants a hands-on introduction to designing and building data pipelines on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and derive insights. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data pipelines on Google Cloud Platform • Lift and shift your existing Hadoop workloads to the Cloud using Cloud Dataproc. • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Manage your data Pipelines with Data Fusion and Cloud Composer. • Derive business insights from extremely large datasets using Google BigQuery • Learn how to use pre-built ML APIs on unstructured data and build different kinds of ML models using BigQuery ML. • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Integrating analytics and machine learning capabilities into data pipelines • Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

GCP专业化方面的数据工程，大数据和机器学习：此在线专业化课程向参与者提供了动手介绍，以介绍如何在Google Cloud Platform上设计和构建数据管道。通过演示，演示和动手实验的结合，参与者将学习如何设计数据处理系统，建立端到端数据管道，分析数据并获得见解。该课程涵盖结构化，非结构化和流数据。 本课程教授以下技能： •在Google Cloud Platform上设计和建立数据管道 •使用Cloud Dataproc将现有的Hadoop工作负载提升并转移到云中。 •通过在Cloud Dataflow上实现自动扩展数据管道来处理批处理和流数据 •使用数据融合和Cloud Composer管理数据管道。 •使用Google BigQuery从庞大的数据集中获得业务见解 •了解如何在非结构化数据上使用预构建的ML API，以及如何使用BigQuery ML构建不同种类的ML模型。 •从流数据中获得即时见解 此类适用于负责以下工作的开发人员： •提取，加载，转换，清洁和验证数据 •设计用于数据处理的管道和架构 •将分析和机器学习功能集成到数据管道中 •查询数据集，可视化查询结果并创建报告 ＆gt;＆gt;＆gt;通过注册该专业，您同意FAQ中列出的Qwiklabs服务条款，该条款位于：https://qwiklabs.com/terms_of_service＆lt;＆lt;＆lt;

课程大纲

Course: 1

Course Link: https://www.coursera.org/learn/gcp-big-data-ml-fundamentals?specialization=gcp-data-machine-learning

Title:Google Cloud Big Data and Machine Learning Fundamentals

Description:This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Course: 2

Course Link: https://www.coursera.org/learn/data-lakes-data-warehouses-gcp?specialization=gcp-data-machine-learning

Title:Modernizing Data Lakes and Data Warehouses with Google Cloud

Description:The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.

Course: 3

Course Link: https://www.coursera.org/learn/batch-data-pipelines-gcp?specialization=gcp-data-machine-learning

Title:Building Batch Data Pipelines on GCP

Description:Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

Course: 4

Course Link: https://www.coursera.org/learn/streaming-analytics-systems-gcp?specialization=gcp-data-machine-learning

Title:Building Resilient Streaming Analytics Systems on Google Cloud

Description:Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud using QwikLabs.

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