Data Engineering on Microsoft Azure Video Course

DP-203: Data Engineering on Microsoft Azure

Your Microsoft DP-203 exam is just around the corner, right? So, it's high time to find an effective preparation tool! Our training course is what you really need! This is a series of videos led by the experienced IT instructors who will provide you with a detailed overview of the DP-203 certification test. Ace your Microsoft DP-203 at the first attempt and obtain the Data Engineering on Microsoft Azure credential with ease.

# of Students
133
# of Lectures
262
Course Length
10 h
Course Rating
5.0
Price $27.49
Today $24.99

Curriculum for DP-203 Video Course

Introduction

  Video Name Time
  1. IMPORTANT - How we are going to approach the exam objectives 3:00
  2. OPTIONAL - Overview of Azure 2:00
  3. OPTIONAL - Concepts in Azure 4:00
  4. Azure Free Account 5:00
  5. Creating an Azure Free Account 5:00
  6. OPTIONAL - Quick tour of the Azure Portal 6:00
  Video Name Time
  1. Section Introduction 2:00
  2. Understanding data 4:00
  3. Example of data storage 2:00
  4. Lab - Azure Storage accounts 6:00
  5. Lab - Azure SQL databases 15:00
  6. A quick note when it comes to the Azure Free Account 4:00
  7. Lab - Application connecting to Azure Storage and SQL database 11:00
  8. Different file formats 7:00
  9. Azure Data Lake Gen-2 storage accounts 3:00
  10. Lab - Creating an Azure Data Lake Gen-2 storage account 9:00
  11. Using PowerBI to view your data 7:00
  12. Lab - Authorizing to Azure Data Lake Gen 2 - Access Keys - Storage Explorer 6:00
  13. Lab - Authorizing to Azure Data Lake Gen 2 - Shared Access Signatures 8:00
  14. Azure Storage Account - Redundancy 11:00
  15. Azure Storage Account - Access tiers 9:00
  16. Azure Storage Account - Lifecycle policy 3:00
  17. Note on Costing 5:00
  Video Name Time
  1. Section Introduction 2:00
  2. The internals of a database engine 4:00
  3. Lab - Setting up a new Azure SQL database 3:00
  4. Lab - T-SQL - SELECT clause 3:00
  5. Lab - T-SQL - WHERE clause 3:00
  6. Lab - T-SQL - ORDER BY clause 1:00
  7. Lab - T-SQL - Aggregate Functions 1:00
  8. Lab - T-SQL - GROUP BY clause 4:00
  9. Lab - T-SQL - HAVING clause 1:00
  10. Quick Review on Primary and Foreign Keys 4:00
  11. Lab - T-SQL - Creating Tables with Keys 3:00
  12. Lab - T-SQL - Table Joins 5:00
  Video Name Time
  1. Section Introduction 2:00
  2. Why do we need a data warehouse 10:00
  3. Welcome to Azure Synapse Analytics 2:00
  4. Lab - Let's create a Azure Synapse workspace 3:00
  5. Azure Synapse - Compute options 3:00
  6. Using External tables 4:00
  7. Lab - Using External tables - Part 1 9:00
  8. Lab - Using External tables - Part 2 12:00
  9. Lab - Creating a SQL pool 7:00
  10. Lab - SQL Pool - External Tables - CSV 9:00
  11. Data Cleansing 4:00
  12. Lab - SQL Pool - External Tables - CSV with formatted data 3:00
  13. Lab - SQL Pool - External Tables - Parquet - Part 1 4:00
  14. Lab - SQL Pool - External Tables - Parquet - Part 2 7:00
  15. Loading data into the Dedicated SQL Pool 2:00
  16. Lab - Loading data into a table - COPY Command - CSV 11:00
  17. Lab - Loading data into a table - COPY Command - Parquet 3:00
  18. Pausing the Dedicated SQL pool 3:00
  19. Lab - Loading data using PolyBase 5:00
  20. Lab - BULK INSERT from Azure Synapse 6:00
  21. My own experience 6:00
  22. Designing a data warehouse 11:00
  23. More on dimension tables 5:00
  24. Lab - Building a data warehouse - Setting up the database 6:00
  25. Lab - Building a Fact Table 8:00
  26. Lab - Building a dimension table 6:00
  27. Lab - Transfer data to our SQL Pool 15:00
  28. Other points in the copy activity 2:00
  29. Lab - Using Power BI for Star Schema 6:00
  30. Understanding Azure Synapse Architecture 7:00
  31. Understanding table types 7:00
  32. Understanding Round-Robin tables 5:00
  33. Lab - Creating Hash-distributed Tables 5:00
  34. Note on creating replicated tables 1:00
  35. Designing your tables 4:00
  36. Designing tables - Review 4:00
  37. Lab - Example when using the right distributions for your tables 10:00
  38. Points on tables in Azure Synapse 2:00
  39. Lab - Windowing Functions 4:00
  40. Lab - Reading JSON files 5:00
  41. Lab - Surrogate keys for dimension tables 6:00
  42. Slowly Changing dimensions 4:00
  43. Type 3 Slowly Dimension dimension 2:00
  44. Creating a heap table 3:00
  45. Snowflake schema 1:00
  46. Lab - CASE statement 6:00
  47. Partitions in Azure Synapse 2:00
  48. Lab - Creating a table with partitions 11:00
  49. Lab - Switching partitions 7:00
  50. Indexes 6:00
  51. Quick Note - Modern Data Warehouse Architecture 2:00
  52. Quick Note on what we are taking forward to the next sections 2:00
  53. What about the Spark Pool 2:00
  Video Name Time
  1. Section Introduction 1:00
  2. Extract, Transform and Load 2:00
  3. What is Azure Data Factory 5:00
  4. Starting with Azure Data Factory 2:00
  5. Lab - Azure Data Lake to Azure Synapse - Log.csv file 13:00
  6. Lab - Azure Data Lake to Azure Synapse - Parquet files 13:00
  7. Lab - The case with escape characters 8:00
  8. Review on what has been done so far 6:00
  9. Lab - Generating a Parquet file 5:00
  10. Lab - What about using a query for data transfer 6:00
  11. Deleting artefacts in Azure Data Factory 3:00
  12. Mapping Data Flow 5:00
  13. Lab - Mapping Data Flow - Fact Table 14:00
  14. Lab - Mapping Data Flow - Dimension Table - DimCustomer 15:00
  15. Lab - Mapping Data Flow - Dimension Table - DimProduct 10:00
  16. Lab - Surrogate Keys - Dimension tables 4:00
  17. Lab - Using Cache sink 9:00
  18. Lab - Handling Duplicate rows 8:00
  19. Note - What happens if we don't have any data in our DimProduct table 4:00
  20. Changing connection details 1:00
  21. Lab - Changing the Time column data in our Log.csv file 8:00
  22. Lab - Convert Parquet to JSON 5:00
  23. Lab - Loading JSON into SQL Pool 5:00
  24. Self-Hosted Integration Runtime 3:00
  25. Lab - Self-Hosted Runtime - Setting up nginx 9:00
  26. Lab - Self-Hosted Runtime - Setting up the runtime 7:00
  27. Lab - Self-Hosted Runtime - Copy Activity 7:00
  28. Lab - Self-Hosted Runtime - Mapping Data Flow 16:00
  29. Lab - Processing JSON Arrays 8:00
  30. Lab - Processing JSON Objects 6:00
  31. Lab - Conditional Split 6:00
  32. Lab - Schema Drift 12:00
  33. Lab - Metadata activity 14:00
  34. Lab - Azure DevOps - Git configuration 11:00
  35. Lab - Azure DevOps - Release configuration 11:00
  36. What resources are we taking forward 1:00
  Video Name Time
  1. Batch and Real-Time Processing 5:00
  2. What are Azure Event Hubs 5:00
  3. Lab - Creating an instance of Event hub 7:00
  4. Lab - Sending and Receiving Events 10:00
  5. What is Azure Stream Analytics 2:00
  6. Lab - Creating a Stream Analytics job 4:00
  7. Lab - Azure Stream Analytics - Defining the job 10:00
  8. Review on what we have seen so far 8:00
  9. Lab - Reading database diagnostic data - Setup 4:00
  10. Lab - Reading data from a JSON file - Setup 6:00
  11. Lab - Reading data from a JSON file - Implementation 5:00
  12. Lab - Reading data from the Event Hub - Setup 7:00
  13. Lab - Reading data from the Event Hub - Implementation 8:00
  14. Lab - Timing windows 10:00
  15. Lab - Adding multiple outputs 4:00
  16. Lab - Reference data 5:00
  17. Lab - OVER clause 8:00
  18. Lab - Power BI Output 10:00
  19. Lab - Reading Network Security Group Logs - Server Setup 3:00
  20. Lab - Reading Network Security Group Logs - Enabling NSG Flow Logs 8:00
  21. Lab - Reading Network Security Group Logs - Processing the data 13:00
  22. Lab - User Defined Functions 9:00
  23. Custom Serialization Formats 3:00
  24. Lab - Azure Event Hubs - Capture Feature 7:00
  25. Lab - Azure Data Factory - Incremental Data Copy 11:00
  26. Demo on Azure IoT Devkit 5:00
  27. What resources are we taking forward 1:00
  Video Name Time
  1. Section Introduction 2:00
  2. Introduction to Scala 2:00
  3. Installing Scala 6:00
  4. Scala - Playing with values 3:00
  5. Scala - Installing IntelliJ IDE 5:00
  6. Scala - If construct 3:00
  7. Scala - for construct 1:00
  8. Scala - while construct 1:00
  9. Scala - case construct 1:00
  10. Scala - Functions 2:00
  11. Scala - List collection 4:00
  12. Starting with Python 3:00
  13. Python - A simple program 2:00
  14. Python - If construct 1:00
  15. Python - while construct 1:00
  16. Python - List collection 2:00
  17. Python - Functions 2:00
  18. Quick look at Jupyter Notebook 4:00
  19. Lab - Azure Synapse - Creating a Spark pool 8:00
  20. Lab - Spark Pool - Starting out with Notebooks 9:00
  21. Lab - Spark Pool - Spark DataFrames 4:00
  22. Lab - Spark Pool - Sorting data 6:00
  23. Lab - Spark Pool - Load data 8:00
  24. Lab - Spark Pool - Removing NULL values 8:00
  25. Lab - Spark Pool - Using SQL statements 3:00
  26. Lab - Spark Pool - Write data to Azure Synapse 11:00
  27. Spark Pool - Combined Power 2:00
  28. Lab - Spark Pool - Sharing tables 4:00
  29. Lab - Spark Pool - Creating tables 5:00
  30. Lab - Spark Pool - JSON files 6:00
  Video Name Time
  1. What is Azure Databricks 4:00
  2. Clusters in Azure Databricks 6:00
  3. Lab - Creating a workspace 3:00
  4. Lab - Creating a cluster 14:00
  5. Lab - Simple notebook 3:00
  6. Lab - Using DataFrames 4:00
  7. Lab - Reading a CSV file 4:00
  8. Databricks File System 2:00
  9. Lab - The SQL Data Frame 3:00
  10. Visualizations 1:00
  11. Lab - Few functions on dates 2:00
  12. Lab - Filtering on NULL values 2:00
  13. Lab - Parquet-based files 2:00
  14. Lab - JSON-based files 3:00
  15. Lab - Structured Streaming - Let's first understand our data 3:00
  16. Lab - Structured Streaming - Streaming from Azure Event Hubs - Initial steps 8:00
  17. Lab - Structured Streaming - Streaming from Azure Event Hubs - Implementation 10:00
  18. Lab - Getting data from Azure Data Lake - Setup 7:00
  19. Lab - Getting data from Azure Data Lake - Implementation 5:00
  20. Lab - Writing data to Azure Synapse SQL Dedicated Pool 5:00
  21. Lab - Stream and write to Azure Synapse SQL Dedicated Pool 5:00
  22. Lab - Azure Data Lake Storage Credential Passthrough 10:00
  23. Lab - Running an automated job 6:00
  24. Autoscaling a cluster 2:00
  25. Lab - Removing duplicate rows 3:00
  26. Lab - Using the PIVOT command 4:00
  27. Lab - Azure Databricks Table 5:00
  28. Lab - Azure Data Factory - Running a notebook 6:00
  29. Delta Lake Introduction 2:00
  30. Lab - Creating a Delta Table 5:00
  31. Lab - Streaming data into the table 3:00
  32. Lab - Time Travel 2:00
  33. Quick note on the deciding between Azure Synapse and Azure Databricks 2:00
  34. What resources are we taking forward 1:00
  Video Name Time
  1. Section Introduction 1:00
  2. What is the Azure Key Vault service 5:00
  3. Azure Data Factory - Encryption 5:00
  4. Azure Synapse - Customer Managed Keys 3:00
  5. Azure Dedicated SQL Pool - Transparent Data Encryption 2:00
  6. Lab - Azure Synapse - Data Masking 10:00
  7. Lab - Azure Synapse - Auditing 6:00
  8. Azure Synapse - Data Discovery and Classification 4:00
  9. Azure Synapse - Azure AD Authentication 3:00
  10. Lab - Azure Synapse - Azure AD Authentication - Setting the admin 4:00
  11. Lab - Azure Synapse - Azure AD Authentication - Creating a user 8:00
  12. Lab - Azure Synapse - Row-Level Security 7:00
  13. Lab - Azure Synapse - Column-Level Security 4:00
  14. Lab - Azure Data Lake - Role Based Access Control 7:00
  15. Lab - Azure Data Lake - Access Control Lists 7:00
  16. Lab - Azure Synapse - External Tables Authorization via Managed Identity 8:00
  17. Lab - Azure Synapse - External Tables Authorization via Azure AD Authentication 5:00
  18. Lab - Azure Synapse - Firewall 7:00
  19. Lab - Azure Data Lake - Virtual Network Service Endpoint 7:00
  20. Lab - Azure Data Lake - Managed Identity - Data Factory 6:00
  Video Name Time
  1. Best practices for structing files in your data lake 3:00
  2. Azure Storage accounts - Query acceleration 2:00
  3. View on Azure Monitor 7:00
  4. Azure Monitor - Alerts 8:00
  5. Azure Synapse - System Views 2:00
  6. Azure Synapse - Result set caching 6:00
  7. Azure Synapse - Workload Management 4:00
  8. Azure Synapse - Retention points 2:00
  9. Lab - Azure Data Factory - Monitoring 7:00
  10. Azure Data Factory - Monitoring - Alerts and Metrics 4:00
  11. Lab - Azure Data Factory - Annotations 3:00
  12. Azure Data Factory - Integration Runtime - Note 7:00
  13. Azure Data Factory - Pipeline Failures 3:00
  14. Azure Key Vault - High Availability 2:00
  15. Azure Stream Analytics - Metrics 3:00
  16. Azure Stream Analytics - Streaming Units 2:00
  17. Azure Stream Analytics - An example on monitoring the stream analytics job 11:00
  18. Azure Stream Analytics - The importance of time 7:00
  19. Azure Stream Analytics - More on the time aspect 6:00
  20. Azure Event Hubs and Stream Analytics - Partitions 5:00
  21. Azure Stream Analytics - An example on multiple partitions 7:00
  22. Azure Stream Analytics - More on partitions 4:00
  23. Azure Stream Analytics - An example on diagnosing errors 4:00
  24. Azure Stream Analytics - Diagnostics setting 6:00
  25. Azure Databricks - Monitoring 7:00
  26. Azure Databricks - Sending logs to Azure Monitor 3:00
  27. Azure Event Hubs - High Availability 6:00

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