Google Cloud Digital Leader: Data Processing Pipelines

Google Cloud 2024    |    Beginner
  • 8 videos | 52m 58s
  • Earns a Badge
Optimizing data processing and analytics is crucial for organizations to effectively manage and derive insights from their data. Leveraging Google Cloud's suite of tools enables businesses to build robust, scalable data pipelines and facilitate real-time analytics and workflow automation. In this course, you will explore batch and stream analytics, focusing on their distinct advantages and use cases. You will examine the processes involved in extract, transform, load (ETL) and extract, load, transform (ELT) pipelines. Next, you will investigate the characteristics of Dataproc, which simplifies big data processing on the cloud, as well as various Google Cloud services for ETL, such as Dataflow, which offers real-time stream and batch data processing capabilities. Finally, you will discover real-time messaging with Pub/Sub, Google's service for ingesting event data from multiple sources, supporting real-time analytics and data integration. You will learn about Cloud Composer, a fully managed workflow orchestration service, along with other data-related technologies, gaining insights into how they facilitate the management and automation of complex data workflows. This course helps prepare learners for the Google Cloud Digital Leader certification exam.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Contrast batch and stream processing paradigms
    Differentiate between extract, load, transform (elt) and extract, transform, load (etl) pipelines
    Outline the benefits of dataproc as a managed spark and hadoop service
  • Define unified batch and stream processing and outline the role of dataflow and apache beam
    Outline the role of pub/sub in messaging and streaming use cases
    Describe the architecture and uses of cloud composer
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 48s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 9m 14s
    After completing this video, you will be able to contrast batch and stream processing paradigms. FREE ACCESS
  • Locked
    3.  ETL and ELT Pipelines
    9m 6s
    Upon completion of this video, you will be able to differentiate between extract, load, transform (ELT) and extract, transform, load (ETL) pipelines. FREE ACCESS
  • Locked
    4.  Characteristics of Dataproc
    7m 4s
    After completing this video, you will be able to outline the benefits of Dataproc as a managed Spark and Hadoop service. FREE ACCESS
  • Locked
    5.  Google Cloud Services for ETL
    7m 17s
    Upon completion of this video, you will be able to define unified batch and stream processing and outline the role of Dataflow and Apache Beam. FREE ACCESS
  • Locked
    6.  Real-time Messaging with Pub/Sub
    10m 52s
    After completing this video, you will be able to outline the role of Pub/Sub in messaging and streaming use cases. FREE ACCESS
  • Locked
    7.  Cloud Composer and Other Data-related Technologies
    5m 41s
    Upon completion of this video, you will be able to describe the architecture and uses of Cloud Composer. FREE ACCESS
  • Locked
    8.  Course Summary
    1m 55s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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