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Azure Data Engineer Associate

سرفصل های دوره

01 Introduction
  • 001 Course Introduction
  • 001 Study Guide.pdf
  • 002 About the Training Architect
  • 003 About the Exam
  • 004 Programming Background Needed
  • 004 database administration and sql language basics.zip
  • 004 introduction to python scripting.zip
  • 004 sql deep dive.zip

  • 02 Data Engineering Crash Course
  • 001 Section Introduction
  • 002 The View from Mars Data Engineering at a Glance
  • 003 What Does Azure Have to Offer
  • 004 Introduction to Data Lakes
  • 005 Introduction to Azure Data Factory
  • 005 what is azure data factory.zip
  • 006 Introduction to Azure Synapse Analytics
  • 006 what is azure synapse analytics.zip
  • 007 Introduction to Azure Stream Analytics
  • 008 Introduction to Azure Databricks
  • 008 what is azure databricks.zip
  • 009 Section Recap

  • 03 Data Storage
  • 001 Section Introduction
  • 002 Using Azure Data Lakes
  • 003 Getting the Folder Structure Right
  • 004 Understanding File Types
  • 005 Partitioning Data
  • 006 Partitioning Best Practices Part 1
  • 007 Partitioning Best Practices Part 2
  • 008 Distributing Data
  • 009 Archiving Data
  • 011 Pruning Data
  • 012 Compressing Data
  • 013 Sharding Data
  • 014 Implementing Data Redundancy
  • 016 Section Recap

  • 04 Data Ingestion and Transformation
  • 001 Section Introduction
  • 002 Meeting the Tools of the Trade Azure Data Factory
  • 003 Meeting the Tools of the Trade Transact-SQL
  • 004 Meeting the Tools of the Trade Azure Synapse Pipelines
  • 004 data integration in azure synapse analytics versus azure data factory.zip
  • 005 Meeting the Tools of the Trade Scala
  • 005 introduction to dataframes - scala.zip
  • 006 Meeting the Tools of the Trade Apache Spark
  • 007 Creating Data Pipelines
  • 008 Designing and Creating Tests for Data Pipelines
  • 009 Integrating JupyterPython Notebooks into a Data Pipeline
  • 010 Cleansing Data
  • 011 Splitting Data
  • 012 Shredding JSON
  • 013 Encoding and Decoding Data
  • 014 Configuring Error Handling for Transformations
  • 015 Normalizing and Denormalizing Values
  • 016 Performing Data Exploratory Analysis
  • 018 Section Recap

  • 05 Batch Processing Solutions
  • 001 Section Introduction
  • 002 Identifying Azure Services for Batch Processing
  • 004 Designing and Implementing Incremental Data Loads
  • 005 Designing and Implementing Incremental Data Loads Part 2
  • 006 Introduction to Data Factory Flow
  • 007 Handling Schema Drift
  • 008 Handling Duplicate and Missing Data with Data Flow
  • 008 sample data flow scripts.zip
  • 009 Upserting Data with Data Flow
  • 010 Designing and Configuring Exception Handling
  • 011 Triggering Batches
  • 012 Implementing Azure Databricks
  • 013 Managing Data Pipelines
  • 014 Implementing Version Control for Pipeline Artifacts
  • 015 Managing Spark Jobs in a Pipeline
  • 016 Section Recap

  • 06 Stream Processing Solutions
  • 001 Section Introduction
  • 002 Identifying Azure Services for Stream Processing
  • 003 Designing a Stream Processing Solution
  • 005 Processing Time Series Data
  • 006 Handling Late-Arriving Data
  • 007 Designing and Creating Windowed Aggregates Part 1
  • 007 introduction to stream analytics windowing functions.zip
  • 008 Designing and Creating Windowed Aggregates Part 2
  • 009 Processing Data by Using Spark Structured Streaming
  • 010 Monitoring for Performance and Functional Regressions
  • 011 Processing 1 or More Partitions (Repartitioning)
  • 012 Handling Interruptions
  • 013 Designing and Configuring Exception Handling
  • 014 Upserting Data
  • 015 Replaying Archived Stream Data
  • 016 Section Recap

  • 07 Data Serving Layer
  • 001 Section Introduction
  • 002 Understanding Schema and Table Design
  • 003 Understanding Dimensions
  • 004 Building External Tables
  • 005 Designing Analytical Stores
  • 006 Designing Metastores
  • 007 Maintaining Metadata
  • 008 Section Recap

  • 08 Configuring Security and Compliance
  • 001 Section Introduction
  • 002 Understanding Data Protection
  • 003 Implementing Data Protection with Data Masking
  • 004 Auditing Data
  • 005 Configuring Data Retention
  • 006 Purging Data Based on Business Requirements
  • 007 Securing Azure Data Lake Through RBAC and ACLs
  • 008 Managing Identities Keys and Secrets
  • 009 Implementing Secure Endpoints
  • 011 Implementing Resource Tokens in Azure Databricks
  • 012 Managing Sensitive Information
  • 013 Loading a DataFrame with Sensitive Information
  • 014 Section Recap

  • 09 Monitoring Data Storage and Data Processing
  • 001 Section Introduction
  • 002 Configuring Monitoring Services
  • 003 Measuring Performance of Data Movement
  • 004 Monitoring and Updating Statistics about Data across a System
  • 004 more example syntax to update statistics.zip
  • 005 Measuring Query Performance
  • 006 Monitoring Cluster Performance
  • 007 Understanding Custom Logging Options
  • 008 Interpreting a Spark Directed Acyclic Graph (DAG)
  • 009 Section Recap

  • 10 Optimizing and Troubleshooting Data Storage and Processing
  • 001 Section Introduction
  • 002 Compacting Small Files
  • 003 Rewriting User-Defined Functions
  • 004 Handling Skew in Data
  • 005 Handling Data Spill
  • 006 Finding Shuffling in a Pipeline
  • 007 Tuning Shuffle Partitions
  • 008 Optimizing Resource Management
  • 009 Tuning Queries by Using Cache
  • 010 Troubleshooting a Failed Spark Job
  • 011 Debugging Spark Jobs by Using the Spark UI
  • 012 Optimizing Pipelines for Analytical or Transactional Purposes
  • 014 Troubleshooting a Failed Pipeline Run
  • 014 troubleshoot pipeline orchestration and triggers in azure data factory.zip
  • 015 Section Recap

  • 11 Conclusion
  • 001 Bringing It All Together
  • 002 Exam Tips
  • 003 Final Notes
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