DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Exam – Complete Certification Training Course & Exam Dumps

DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric

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141 Students Enrolled 160 Lectures 16:43:14 Hours

Curriculum For This Course

Introduction

2Lectures

Time 00:04:00

A look arounf Fabric

5Lectures

Time 00:41:54

Using Dataflow Gen2

6Lectures

Time 00:37:09

Transforming data using Dataflow Gen2

8Lectures

Time 00:58:59

Transforming data by using Power Query (M)

6Lectures

Time 00:48:02

Using pipelines

6Lectures

Time 00:38:42

Loading and saving data using notebooks

6Lectures

Time 00:36:38

Manipulating dataframes- choosing columns and rows

7Lectures

Time 00:43:28

Converting data types, aggregating and sorting dataframes

8Lectures

Time 00:44:36

Transformation data in a lakehouse

8Lectures

Time 00:53:23

Improving notebook performance and automating notebooks

8Lectures

Time 00:49:58

Creating objects

8Lectures

Time 00:37:59

Optimize performance in notebooks

4Lectures

Time 00:14:57

Transform data in a data warehouse

6Lectures

Time 00:35:47

Creating incremental data loads

4Lectures

Time 00:28:38

Mange and Optimize Data Warehouse

7Lectures

Time 00:47:50

Creating an eventhouse

2Lectures

Time 00:13:31

Selecting, filtering and aggregating data using KQL

9Lectures

Time 01:01:24

25 KQL Functions

9Lectures

Time 00:56:07

Transforming data using KQL

7Lectures

Time 00:40:35

Ingest and transform streaming data -eventstreams

5Lectures

Time 00:33:06

Ingest and transform streaming data- Other objects

7Lectures

Time 00:46:08

Workspace settings and Monitoring

12Lectures

Time 01:09:40

Configuring security and governance, and deployment pipelines

8Lectures

Time 00:58:46

Congratulations for completing the course

2Lectures

Time 00:01:57

Introduction
	1. Introduction	1:28
	2. Curriculum	2:32

A look arounf Fabric
	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
	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
	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)
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	1. What's Next?	1:13
	2. Congratulations for completing the course	0:44

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