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Azure Spark Databricks Essential Training

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Apache Spark and Microsoft Azure are two of the most in-demand platforms and technology sets in use by today's data science teams. These two platforms join forces in Azure Databricks‚ an Apache Spark-based analytics platform designed to make the work of data analytics easier and more collaborative. In this course, Lynn Langit digs into patterns, tools, and best practices that can help developers and DevOps specialists use Azure Databricks to efficiently build big data solutions on Apache Spark. Lynn covers how to set up clusters and use Azure Databricks notebooks, jobs, and services to implement big data workloads. She also explores data pipelines with Azure Databricks—including how to use ML Pipelines—as well as architectural patterns for machine learning.


01 - Introduction
  • 01 - Optimize data pipelines
  • 02 - What you should know
  • 03 - About using cloud services

  • 02 - 1. Big Data on Azure Databricks
  • 01 - Meet Databricks Apache Spark clusters
  • 02 - Business scenarios for Spark
  • 03 - Understand Spark key components
  • 04 - Azure Databricks concepts
  • 05 - Quick start Use a notebook

  • 03 - 2. Core Azure Databricks Workloads
  • 01 - Review Databricks Azure cluster setup
  • 02 - Use a Python notebook with dashboards
  • 03 - Use an R notebook
  • 04 - Use a Scala notebook for visualization
  • 05 - Use a notebook with scikit-learn
  • 06 - Use a Spark Streaming notebook
  • 07 - Use an external Scala library variant-spark

  • 04 - 3. Scaling Azure Databricks Workloads
  • 01 - Understand data engineering workload steps
  • 02 - Understand cluster configurations
  • 03 - Understand Spark job execution overhead
  • 04 - Explore optimization control planes
  • 05 - Optimize a cluster and job
  • 06 - Run a production-size job

  • 05 - 4. Data Pipelines with Azure Databricks
  • 01 - Use Databricks jobs and role-based control
  • 02 - Use Databricks Runtime ML
  • 03 - Understand ML Pipelines API
  • 04 - Use ML Pipelines API
  • 05 - Use distributed ML training
  • 06 - Understand Databricks Delta
  • 07 - Use Databricks Delta
  • 08 - Use Azure Blob storage
  • 09 - Understand MLflow

  • 06 - 5. Machine Learning Architectures
  • 01 - Azure Databricks pipeline considerations
  • 02 - Azure Databricks for data warehousing
  • 03 - Azure Databricks and machine learning
  • 04 - Azure Databricks for churn analysis
  • 05 - Azure Databricks for intrusion detection

  • 07 - Conclusion
  • 01 - Next steps
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