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DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Certification Video Training Course
The complete solution to prepare for for your exam with DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric certification video training course. The DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric certification video training course contains a complete set of videos that will provide you with thorough knowledge to understand the key concepts. Top notch prep including Microsoft DP-700 exam dumps, study guide & practice test questions and answers.
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16:43:14 Hours
DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Certification Video Training Course Exam Curriculum
1
Introduction
2 Lectures
Time 00:04:00
2
A look arounf Fabric
5 Lectures
Time 00:41:54
3
Using Dataflow Gen2
6 Lectures
Time 00:37:09
4
Transforming data using Dataflow Gen2
8 Lectures
Time 00:58:59
5
Transforming data by using Power Query (M)
6 Lectures
Time 00:48:02
6
Using pipelines
6 Lectures
Time 00:38:42
7
Loading and saving data using notebooks
6 Lectures
Time 00:36:38
8
Manipulating dataframes- choosing columns and rows
7 Lectures
Time 00:43:28
9
Converting data types, aggregating and sorting dataframes
8 Lectures
Time 00:44:36
10
Transformation data in a lakehouse
8 Lectures
Time 00:53:23
11
Improving notebook performance and automating notebooks
8 Lectures
Time 00:49:58
12
Creating objects
8 Lectures
Time 00:37:59
13
Optimize performance in notebooks
4 Lectures
Time 00:14:57
14
Transform data in a data warehouse
6 Lectures
Time 00:35:47
15
Creating incremental data loads
4 Lectures
Time 00:28:38
16
Mange and Optimize Data Warehouse
7 Lectures
Time 00:47:50
17
Creating an eventhouse
2 Lectures
Time 00:13:31
18
Selecting, filtering and aggregating data using KQL
9 Lectures
Time 01:01:24
19
25 KQL Functions
9 Lectures
Time 00:56:07
20
Transforming data using KQL
7 Lectures
Time 00:40:35
21
Ingest and transform streaming data -eventstreams
5 Lectures
Time 00:33:06
22
Ingest and transform streaming data- Other objects
7 Lectures
Time 00:46:08
23
Workspace settings and Monitoring
12 Lectures
Time 01:09:40
24
Configuring security and governance, and deployment pipelines
8 Lectures
Time 00:58:46
25
Congratulations for completing the course
2 Lectures
Time 00:01:57
Introduction
-
1:28
1. Introduction
-
2:32
2. Curriculum
A look arounf Fabric
-
5:12
1. Signing into Microsoft Fabric
-
13:49
2. Why do I need a Work email address? And how can I get one, if I don't have it?
-
7:02
3. Creating a Fabric capacity and configure Fabric-enabled workspace settings
-
6:56
4. Identify requirements for a Fabric solution and manage Fabric capacity
-
8:55
5. A quick tour of Fabric
Using Dataflow Gen2
-
8:22
1. Ingest data by using a dataflow
-
7:08
2. Add a destination to a dataflow
-
4:52
3. Saving as a template and scheduling a dataflow
-
3:22
4. Implement Fast Copy when using dataflows
-
6:21
5. Monitor data transformation, identify and resolve errors using dataflows
-
7:04
6. 35. Optimize a dataflow
Transforming data using Dataflow Gen2
-
7:06
1. The first part of the Home menu, including converting column data types
-
8:30
2. Removing rows/columns, and filtering and sorting data
-
5:52
3. Grouping and aggregating data, and duplicating and referencing queries
-
6:34
4. Denormalize data by Joining data together using Merge Queries
-
5:50
5. Unioning data using Append Queries
-
8:51
6. Identify and resolve duplicate data, missing data (null values)
-
6:55
7. Transforming data and adding additional columns
-
9:21
8. Practice Activity Number 1 - The Solution
Transforming data by using Power Query (M)
-
8:42
1. Introducing the M language
-
9:14
2. M Number functions
-
6:51
3. M Text functions
-
7:15
4. M Date, Time and Duration functions
-
6:29
5. 27. M Group functions and removing rows
-
9:31
6. M Table functions
Using pipelines
-
7:05
1. Ingest data by using a data pipeline, and adding other activities
-
9:29
2. Copy data by using a data pipeline
-
4:27
3. Schedule data pipelines and monitor data pipeline runs
-
6:41
4. Identifying and resolving pipeline errors, and optimizing a pipeline
-
01:57
5. Exploring sample data (including copy data assistant) + data pipeline templates
-
9:03
6. Practice Activity Number 2 - The Solution
Loading and saving data using notebooks
-
5:28
1. Ingesting data into a lakehouse using a local upload
-
3:03
2. Choose an appropriate method for copying to a Lakehouse or Warehouse
-
9:00
3. Ingesting data using a notebook, and copying to a table
-
8:20
4. Saving data to a file or Lakehouse table
-
7:16
5. Loading data from a table in PySpark and SQL, and manipulating the results
-
03:31
6. Practice Activity Number 3 - The Solution
Manipulating dataframes- choosing columns and rows
-
05:53
1. Reducing the number of columns shown
-
07:14
2. Filtering data with: where, limit and tail
-
3:07
3. Enriching data by adding new columns
-
7:41
4. Using Functions
-
7:28
5. More advanced filtering
-
8:37
6. Practice Activity Number 4 using PySpark - The Solution
-
3:28
7. Practice Activity Number 5 using SQL - The Solution
Converting data types, aggregating and sorting dataframes
-
6:08
1. Converting data types
-
3:58
2. Importing data using an explicit data structure
-
6:42
3. Formatting dates as strings
-
4:37
4. Aggregating and re-filtering data
-
5:52
5. Sorting the results
-
4:49
6. Using all 6 SQL Clauses
-
6:35
7. Practice Activity Number 6 using PySpark - The Solution
-
5:55
8. Practice Activity Number 7 using SQL - The Solution
Transformation data in a lakehouse
-
8:12
1. Merging data
-
5:11
2. Identifying and resolving duplicate data
-
6:31
3. Joining data using an Inner join
-
6:44
4. Joining data using other joins
-
7:33
5. Identifying missing data or null values
-
3:00
6. Practice Activity Number 8 using PySpark - The Solution
-
7:34
7. Practice Activity Number 9 using PySpark - The Solution
-
8:38
8. Practice Activity Number 10 using SQL - The Solution
Improving notebook performance and automating notebooks
-
02:50
1. Schedule notebooks
-
08:12
2. Process data by using Spark structured streaming in a notebook
-
04:00
3. Testing the processing of streaming data in a notebook
-
09:09
4. Process data by using a Spark Job Definition
-
4:11
5. Choosing between a pipeline, a dataflow and a notebook
-
7:19
6. Implement parameters with notebooks and pipelines
-
6:14
7. Implement dynamic expressions with notebooks and pipelines
-
8:03
8. Practice Activity Number 11 - The Solution
Creating objects
-
5:55
1. Create and manage shortcuts
-
3:49
2. Identify and resolve Shortcut errors
-
3:02
3. Configure OneLake workspace settings
-
2:23
4. Creating a Microsoft Azure SQL Database as a source
-
8:25
5. Implement file partitioning for analytics workloads using a pipeline
-
3:10
6. Implement file partitioning for analytics workloads - data is in a lakehouse
-
7:34
7. Implement mirroring of external databases
-
3:41
8. Practice Activity Number 12 - The Solution
Optimize performance in notebooks
-
03:11
1. Identify and resolve data loading performance bottlenecks in notebooks
-
3:54
2. Implement performance improvements in notebooks, inc. V-Order
-
2:42
3. Identify and resolve issues with Delta table file: optimized writes
-
5:10
4. Optimize Spark performance
Transform data in a data warehouse
-
5:52
1. Creating tables in a data warehouse
-
6:58
2. Inserting data into tables and transforming data in a Data Warehouse
-
3:26
3. Choose between dataflows, notebooks, and T-SQL for data transformation
-
7:06
4. Slowly changing dimensions - Theory
-
4:56
5. Implement Type 0 slowly changing dimensions - Practical Example
-
7:29
6. Implement Type 1 and Type 2 slowly changing dimensions - Practical Example
Creating incremental data loads
-
5:00
1. Design an incremental data load from a Data Warehouse using a pipeline
-
9:46
2. Implement an incremental data load from a Data Warehouse using a pipeline
-
4:50
3. Test an incremental data load from a Data Warehouse using a pipeline
-
9:02
4. Implementing an incremental data loads using a Dataflow Gen2
Mange and Optimize Data Warehouse
-
6:53
1. Creating a Premium Per User (PPU) workspace and Azure DevOps repos
-
7:56
2. Implement version control for a workspace
-
7:06
3. Implement database projects, including in source control
-
6:32
4. Implement dynamic data masking in a Data Warehouse - Video 1
-
6:14
5. Implement dynamic data masking in a Data Warehouse - Video 2
-
7:10
6. Optimize a data warehouse
-
5:59
7. Practice Activity Number 13 - The Solution
Creating an eventhouse
-
5:39
1. Creating an eventhouse, exploring the environment, and getting data
-
7:52
2. Creating sample KQL and SQL queries, and exploring the query environment
Selecting, filtering and aggregating data using KQL
-
7:12
1. Selecting data using KQL
-
4:29
2. Further selecting columns and ordering data using KQL
-
5:23
3. Limiting the number of rows
-
9:04
4. Practice Activity Number 14 - The Solution
-
4:57
5. Creating a string literal
-
8:00
6. Filtering for the entirety of a string
-
7:13
7. Filtering for part of a string
-
8:05
8. Aggregating data
-
7:01
9. Practice Activity Number 15 - The Solution
25 KQL Functions
-
8:51
1. Empty strings, concatenating and trimming strings
-
8:25
2. Manipulating strings
-
1:16
3. Other string functions
-
7:27
4. Practice Activity Number 16 - The Solution
-
6:37
5. Number Data Types
-
4:05
6. Other Math Functions
-
5:12
7. datetime and timespan Data Types
-
8:10
8. datetime and timespan Functions
-
6:04
9. Practice Activity Number 17 - The Solution
Transforming data using KQL
-
4:07
1. Merging data
-
10:33
2. Joining data
-
5:18
3. Practice Activity Number 18 - The Solution
-
6:13
4. Identify and resolve duplicate data, missing data, or null values
-
3:48
5. The iif/iff and case conditional functions
-
5:39
6. The OneLake data and real-time hubs + implementing OneLake integration
-
4:57
7. Practice Activity Number 19 - The Solution
Ingest and transform streaming data -eventstreams
-
3:02
1. Choose an appropriate streaming engine
-
8:39
2. Processing data by using an eventstream
-
6:59
3. The Manage fields transform event in an eventstream
-
7:02
4. The Group by transform event, including Creating windowing functions
-
7:24
5. Completing our eventstream
Ingest and transform streaming data- Other objects
-
7:50
1. Revising KQL Syntax
-
8:00
2. Creating a Fabric activator to run based on an event-based trigger
-
9:49
3. Ingest data by using continuous integration from OneLake - Part 2
-
2:50
4. Designing and implement an event-based trigger based on Azure Blob storage
-
5:03
5. Optimizing eventstreams and eventhouses
-
8:47
6. Native storage, mirrored storage, or shortcuts in Real-Time Intelligence
-
3:49
7. Choose between accelerated shortcuts and non-accelerated shortcuts
Workspace settings and Monitoring
-
8:51
1. Spark workspace settings: starter and custom pools, and environments
-
6:06
2. Other Spark workspace settings
-
10:23
3. Configure domain workspace settings
-
2:11
4. Configure data workflow workspace settings
-
4:04
5. Recommend settings in the Fabric admin portal
-
5:03
6. Implement workspace and item-level access controls for Fabric items
-
03:55
7. Installing the Microsoft Fabric Capacity Metrics app
-
7:13
8. Using the Microsoft Fabric Capacity Metrics app - Manage Fabric capacity
-
3:26
9. Monitor semantic model refresh
-
5:27
10. Implement workspace logging
-
6:06
11. Workspace logging dashboards
-
6:55
12. Querying Workspace logs in KQL
Configuring security and governance, and deployment pipelines
-
7:04
1. Apply sensitivity labels to items
-
4:57
2. Endorse items
-
8:54
3. Row-level security in a Data Warehouse
-
6:40
4. Column-level security in a Data Warehouse
-
8:38
5. Object-level security in a Data Warehouse
-
5:15
6. Folder-/File-level access controls in a Lakehouse
-
10:35
7. Creating a deployment pipeline
-
6:43
8. Configuring a deployment pipeline
Congratulations for completing the course
-
1:13
1. What's Next?
-
0:44
2. Congratulations for completing the course
About DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Certification Video Training Course
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