SRE Data Pipelines & Integrity: Data Pipelines

SRE
  • 21 Videos | 1h 20m 42s
  • Includes Assessment
  • Earns a Badge
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Site reliability engineers often find data processing complex as demands for faster, more reliable, and extra cost-effective results continue to evolve. In this course, you'll explore techniques and best practices for managing a data pipeline. You'll start by examining the various pipeline application models and their recommended uses. You'll then learn how to define and measure service level objectives, plan for dependency failures, and create and maintain pipeline documentation. Next, you'll outline the phases of a pipeline development lifecycle's typical release flow before investigating more challenging topics such as managing data processing pipelines, using big data with simple data pipelines, and using periodic pipeline patterns. Lastly, you'll delve into the components of Google Workflow and recognize how to work with this system.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    describe the characteristics of and rationale for using data processing pipelines
    recognize characteristics of the Extract Transform Load (ETL) pipeline model
    define business intelligence and data analytics in the context of data processing and give an example data analytics use case
    list characteristics of machine learning (ML) applications
    define what is meant by service-level objectives (SLOs) and describe how they relate to pipeline data
    outline how to plan for dependency failures
    recognize how to create and maintain pipeline documentation
    outline the stages of a typical development lifecycle
    describe how to reduce hotspotting
    recognize how to implement autoscaling to handle spikes in workloads
  • describe how to adhere best to access control and security policies
    plan escalation paths that ensure quick and proactive communication
    describe the effect big data can have on simple pipeline patterns
    list the challenges with using the periodic pipeline pattern
    describe the issues that can occur due to uneven work distribution
    list the potential drawbacks of periodic pipelines in distributed environments
    describe what comprises Google Workflow and outline how it works
    outline the stages of execution in Google Workflow, describing what they entail
    recognize the key factors to ensuring business continuity in big data pipelines using Google Workflow
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 53s
    UP NEXT
  • Playable
    2. 
    Data Processing Pipelines
    4m 38s
  • Locked
    3. 
    The Extract Transform Load (ETL) Pipeline Model
    3m 8s
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    4. 
    Business Intelligence and Data Processing
    2m 52s
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    5. 
    Features of Machine Learning Apps
    4m 18s
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    6. 
    Service-level Objectives (SLOs) and Data Pipelines
    4m 35s
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    7. 
    Planning for Dependency Failure
    3m 32s
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    8. 
    Managing System Documentation
    5m 3s
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    9. 
    Development Lifecycle Stages
    5m 39s
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    10. 
    Reducing Hotspotting
    4m 8s
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    11. 
    Implementing Autoscaling for Workload Spikes
    3m 38s
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    12. 
    Adhering to Security Policies
    2m 59s
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    13. 
    Planning Escalation Paths
    2m 16s
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    14. 
    Big Data and Simple Pipelines
    2m 23s
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    15. 
    The Periodic Pipeline Pattern
    2m 25s
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    16. 
    Issues with Uneven Work Distribution
    2m 51s
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    17. 
    Periodic Pipelines in Distributed Environments
    3m 21s
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    18. 
    Google Workflow's Composition
    3m 17s
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    19. 
    Google Workflow's Stages of Execution
    1m 43s
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    20. 
    Ensuring Business Continuity with Google Workflow
    4m 50s
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    21. 
    Course Summary
    1m 42s

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