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DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Certification Video Training Course

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  • 121 Students Enrolled
  • 160 Lectures
  • 16 Hours
  • 5.0 Rating
$27.49
$24.99

Curriculum For This Course

  • Introduction

    Video Name Time
    1. Introduction 1:28
    2. Curriculum 2:32

  • A look arounf Fabric

    Video Name Time
    1. Signing into Microsoft Fabric 5:12
    2. Why do I need a Work email address? And how can I get one, if I don't have it? 13:49
    3. Creating a Fabric capacity and configure Fabric-enabled workspace settings 7:02
    4. Identify requirements for a Fabric solution and manage Fabric capacity 6:56
    5. A quick tour of Fabric 8:55

  • Using Dataflow Gen2

    Video Name Time
    1. Ingest data by using a dataflow 8:22
    2. Add a destination to a dataflow 7:08
    3. Saving as a template and scheduling a dataflow 4:52
    4. Implement Fast Copy when using dataflows 3:22
    5. Monitor data transformation, identify and resolve errors using dataflows 6:21
    6. 35. Optimize a dataflow 7:04

  • Transforming data using Dataflow Gen2

    Video Name Time
    1. The first part of the Home menu, including converting column data types 7:06
    2. Removing rows/columns, and filtering and sorting data 8:30
    3. Grouping and aggregating data, and duplicating and referencing queries 5:52
    4. Denormalize data by Joining data together using Merge Queries 6:34
    5. Unioning data using Append Queries 5:50
    6. Identify and resolve duplicate data, missing data (null values) 8:51
    7. Transforming data and adding additional columns 6:55
    8. Practice Activity Number 1 - The Solution 9:21

  • Transforming data by using Power Query (M)

    Video Name Time
    1. Introducing the M language 8:42
    2. M Number functions 9:14
    3. M Text functions 6:51
    4. M Date, Time and Duration functions 7:15
    5. 27. M Group functions and removing rows 6:29
    6. M Table functions 9:31

  • Using pipelines

    Video Name Time
    1. Ingest data by using a data pipeline, and adding other activities 7:05
    2. Copy data by using a data pipeline 9:29
    3. Schedule data pipelines and monitor data pipeline runs 4:27
    4. Identifying and resolving pipeline errors, and optimizing a pipeline 6:41
    5. Exploring sample data (including copy data assistant) + data pipeline templates 01:57
    6. Practice Activity Number 2 - The Solution 9:03

  • Loading and saving data using notebooks

    Video Name Time
    1. Ingesting data into a lakehouse using a local upload 5:28
    2. Choose an appropriate method for copying to a Lakehouse or Warehouse 3:03
    3. Ingesting data using a notebook, and copying to a table 9:00
    4. Saving data to a file or Lakehouse table 8:20
    5. Loading data from a table in PySpark and SQL, and manipulating the results 7:16
    6. Practice Activity Number 3 - The Solution 03:31

  • Manipulating dataframes- choosing columns and rows

    Video Name Time
    1. Reducing the number of columns shown 05:53
    2. Filtering data with: where, limit and tail 07:14
    3. Enriching data by adding new columns 3:07
    4. Using Functions 7:41
    5. More advanced filtering 7:28
    6. Practice Activity Number 4 using PySpark - The Solution 8:37
    7. Practice Activity Number 5 using SQL - The Solution 3:28

  • Converting data types, aggregating and sorting dataframes

    Video Name Time
    1. Converting data types 6:08
    2. Importing data using an explicit data structure 3:58
    3. Formatting dates as strings 6:42
    4. Aggregating and re-filtering data 4:37
    5. Sorting the results 5:52
    6. Using all 6 SQL Clauses 4:49
    7. Practice Activity Number 6 using PySpark - The Solution 6:35
    8. Practice Activity Number 7 using SQL - The Solution 5:55

  • Transformation data in a lakehouse

    Video Name Time
    1. Merging data 8:12
    2. Identifying and resolving duplicate data 5:11
    3. Joining data using an Inner join 6:31
    4. Joining data using other joins 6:44
    5. Identifying missing data or null values 7:33
    6. Practice Activity Number 8 using PySpark - The Solution 3:00
    7. Practice Activity Number 9 using PySpark - The Solution 7:34
    8. Practice Activity Number 10 using SQL - The Solution 8:38

  • Improving notebook performance and automating notebooks

    Video Name Time
    1. Schedule notebooks 02:50
    2. Process data by using Spark structured streaming in a notebook 08:12
    3. Testing the processing of streaming data in a notebook 04:00
    4. Process data by using a Spark Job Definition 09:09
    5. Choosing between a pipeline, a dataflow and a notebook 4:11
    6. Implement parameters with notebooks and pipelines 7:19
    7. Implement dynamic expressions with notebooks and pipelines 6:14
    8. Practice Activity Number 11 - The Solution 8:03

  • Creating objects

    Video Name Time
    1. Create and manage shortcuts 5:55
    2. Identify and resolve Shortcut errors 3:49
    3. Configure OneLake workspace settings 3:02
    4. Creating a Microsoft Azure SQL Database as a source 2:23
    5. Implement file partitioning for analytics workloads using a pipeline 8:25
    6. Implement file partitioning for analytics workloads - data is in a lakehouse 3:10
    7. Implement mirroring of external databases 7:34
    8. Practice Activity Number 12 - The Solution 3:41

  • Optimize performance in notebooks

    Video Name Time
    1. Identify and resolve data loading performance bottlenecks in notebooks 03:11
    2. Implement performance improvements in notebooks, inc. V-Order 3:54
    3. Identify and resolve issues with Delta table file: optimized writes 2:42
    4. Optimize Spark performance 5:10

  • Transform data in a data warehouse

    Video Name Time
    1. Creating tables in a data warehouse 5:52
    2. Inserting data into tables and transforming data in a Data Warehouse 6:58
    3. Choose between dataflows, notebooks, and T-SQL for data transformation 3:26
    4. Slowly changing dimensions - Theory 7:06
    5. Implement Type 0 slowly changing dimensions - Practical Example 4:56
    6. Implement Type 1 and Type 2 slowly changing dimensions - Practical Example 7:29

  • Creating incremental data loads

    Video Name Time
    1. Design an incremental data load from a Data Warehouse using a pipeline 5:00
    2. Implement an incremental data load from a Data Warehouse using a pipeline 9:46
    3. Test an incremental data load from a Data Warehouse using a pipeline 4:50
    4. Implementing an incremental data loads using a Dataflow Gen2 9:02

  • Mange and Optimize Data Warehouse

    Video Name Time
    1. Creating a Premium Per User (PPU) workspace and Azure DevOps repos 6:53
    2. Implement version control for a workspace 7:56
    3. Implement database projects, including in source control 7:06
    4. Implement dynamic data masking in a Data Warehouse - Video 1 6:32
    5. Implement dynamic data masking in a Data Warehouse - Video 2 6:14
    6. Optimize a data warehouse 7:10
    7. Practice Activity Number 13 - The Solution 5:59

  • Creating an eventhouse

    Video Name Time
    1. Creating an eventhouse, exploring the environment, and getting data 5:39
    2. Creating sample KQL and SQL queries, and exploring the query environment 7:52

  • Selecting, filtering and aggregating data using KQL

    Video Name Time
    1. Selecting data using KQL 7:12
    2. Further selecting columns and ordering data using KQL 4:29
    3. Limiting the number of rows 5:23
    4. Practice Activity Number 14 - The Solution 9:04
    5. Creating a string literal 4:57
    6. Filtering for the entirety of a string 8:00
    7. Filtering for part of a string 7:13
    8. Aggregating data 8:05
    9. Practice Activity Number 15 - The Solution 7:01

  • 25 KQL Functions

    Video Name Time
    1. Empty strings, concatenating and trimming strings 8:51
    2. Manipulating strings 8:25
    3. Other string functions 1:16
    4. Practice Activity Number 16 - The Solution 7:27
    5. Number Data Types 6:37
    6. Other Math Functions 4:05
    7. datetime and timespan Data Types 5:12
    8. datetime and timespan Functions 8:10
    9. Practice Activity Number 17 - The Solution 6:04

  • Transforming data using KQL

    Video Name Time
    1. Merging data 4:07
    2. Joining data 10:33
    3. Practice Activity Number 18 - The Solution 5:18
    4. Identify and resolve duplicate data, missing data, or null values 6:13
    5. The iif/iff and case conditional functions 3:48
    6. The OneLake data and real-time hubs + implementing OneLake integration 5:39
    7. Practice Activity Number 19 - The Solution 4:57

  • Ingest and transform streaming data -eventstreams

    Video Name Time
    1. Choose an appropriate streaming engine 3:02
    2. Processing data by using an eventstream 8:39
    3. The Manage fields transform event in an eventstream 6:59
    4. The Group by transform event, including Creating windowing functions 7:02
    5. Completing our eventstream 7:24

  • Ingest and transform streaming data- Other objects

    Video Name Time
    1. Revising KQL Syntax 7:50
    2. Creating a Fabric activator to run based on an event-based trigger 8:00
    3. Ingest data by using continuous integration from OneLake - Part 2 9:49
    4. Designing and implement an event-based trigger based on Azure Blob storage 2:50
    5. Optimizing eventstreams and eventhouses 5:03
    6. Native storage, mirrored storage, or shortcuts in Real-Time Intelligence 8:47
    7. Choose between accelerated shortcuts and non-accelerated shortcuts 3:49

  • Workspace settings and Monitoring

    Video Name Time
    1. Spark workspace settings: starter and custom pools, and environments 8:51
    2. Other Spark workspace settings 6:06
    3. Configure domain workspace settings 10:23
    4. Configure data workflow workspace settings 2:11
    5. Recommend settings in the Fabric admin portal 4:04
    6. Implement workspace and item-level access controls for Fabric items 5:03
    7. Installing the Microsoft Fabric Capacity Metrics app 03:55
    8. Using the Microsoft Fabric Capacity Metrics app - Manage Fabric capacity 7:13
    9. Monitor semantic model refresh 3:26
    10. Implement workspace logging 5:27
    11. Workspace logging dashboards 6:06
    12. Querying Workspace logs in KQL 6:55

  • Configuring security and governance, and deployment pipelines

    Video Name Time
    1. Apply sensitivity labels to items 7:04
    2. Endorse items 4:57
    3. Row-level security in a Data Warehouse 8:54
    4. Column-level security in a Data Warehouse 6:40
    5. Object-level security in a Data Warehouse 8:38
    6. Folder-/File-level access controls in a Lakehouse 5:15
    7. Creating a deployment pipeline 10:35
    8. Configuring a deployment pipeline 6:43

  • Congratulations for completing the course

    Video Name Time
    1. What's Next? 1:13
    2. Congratulations for completing the course 0:44

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