Dp700: Implementing Data Engineering Solutions Using Fabric 2025

Dp700: Implementing Data Engineering Solutions Using Fabric 2025 - Printable Version

+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials)
+--- Thread: Dp700: Implementing Data Engineering Solutions Using Fabric 2025 (/Thread-Dp700-Implementing-Data-Engineering-Solutions-Using-Fabric-2025)

Dp700: Implementing Data Engineering Solutions Using Fabric 2025 - AD-TEAM - 01-17-2025

[Image: 39a371b4e180b05849688c6fed13bf2a.jpg]

Dp-700: Implementing Data Engineering Solutions Using Fabric
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.59 GB | Duration: 4h 28m

Build on your existing DP-600 skills, learn how to manipulate PySpark dataframes in notebooks.

What you'll learn

Implement and manage an analytics solution

Configure security and governance

Ingest and transform data

Monitor and optimize an analytics solution

Requirements

You will need to already be familiar with all of the requirements of Microsoft's DP-600 exam.

This includes SQL and KQL

Description

This course covers the additional content required for the DP-700 "Fabric Data Engineer Engineer Associate" certification exam, building on your existing knowledge gained for the DP-600 exam.First of all, we will take a quick look around Fabric,Then we will look at using data pipelines - ingesting and copying data, and scheduling and monitoring data pipeline runs.Most of this Part 1 course is about manipulating data using PySpark and SQL.We'll have a look at loading and saving data using notebooks.We'll then manipulating dataframes, by choosing which columns and rows to show.We'll then convert data types, aggregating and sorting dataframes,We will then be transforming data in a lakehouse, merging and joining data, together with identifying missing data or null values.We will then be creating objects, such as shortcuts and file partitioning.We will optimize performance in dataflows and notes.Finally, we will look at other Fabric topics, including recommending settings in the Fabric admin portal.Prior knowledge of all of the topics in the DP-600 exam is assumed. This content is available in "DP-600: Implement Analytics Solutions using Microsoft Fabric", which is available on Udemy.Once you have completed the course, you will have a good knowledge of using notebooks to manipulate data using PySpark. And with some practice and knowledge of some additional topics, you could even go for the official Microsoft certification DP-700 - wouldn't the "Microsoft Certified: Fabric Data Engineer Associate" certification look good on your CV or resume?I hope to see you in the course - why not have a look at what you could learn?

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Welcome to Udemy

Lecture 3 The Udemy Interface

Lecture 4 Do you want auto-translated subtitles in more languages?

Lecture 5 Curriculum

Lecture 6 Resources

Section 2: A look around Fabric

Lecture 7 Creating a Fabric capacity and configure Fabric-enabled workspace settings

Lecture 8 Identify requirements for a Fabric solution and manage Fabric capacity

Lecture 9 A quick tour of Fabric

Section 3: Using data pipelines

Lecture 10 24. Ingest data by using a data pipeline, and adding other activities

Lecture 11 24. Copy data by using a data pipeline

Lecture 12 Schedule data pipelines and monitor data pipeline runs

Section 4: Loading and saving data using notebooks

Lecture 13 Ingesting data into a lakehouse using a local upload

Lecture 14 Choose an appropriate method for copying to a Lakehouse or Warehouse

Lecture 15 Ingesting data using a notebook, and copying to a table

Lecture 16 Saving data to a file or Lakehouse table

Lecture 17 Loading data from a table in PySpark and SQL, and manipulating the results

Lecture 18 Practice Activity Number 1

Lecture 19 Practice Activity Number 1 - The Solution

Section 5: 25. Manipulating dataframes - choosing columns and rows

Lecture 20 Reducing the number of columns shown

Lecture 21 Filtering data with: where, limit and tail

Lecture 22 Enriching data by adding new columns

Lecture 23 Using Functions

Lecture 24 More advanced filtering

Section 6: 25. Converting data types, aggregating and sorting dataframes

Lecture 25 Converting data types

Lecture 26 Importing data using an explicit data structure

Lecture 27 Formatting dates as strings

Lecture 28 27. Aggregating and re-filtering data

Lecture 29 Sorting the results

Lecture 30 Using all 6 SQL Clauses

Section 7: Transform data in a lakehouse

Lecture 31 Merging data

Lecture 32 28a. Identifying and resolving duplicate data

Lecture 33 Joining data using an Inner join

Lecture 34 Joining data using other joins

Lecture 35 28b. Identifying missing data or null values

Lecture 36 Practice Activity Number 6 - Implementing bridge tables for a lakehouse

Lecture 37 Practice Activity Number 6 - Solution

Lecture 38 Schedule notebooks

Section 8: Transform data in a data warehouse

Lecture 39 Implement Type 1 and Type 2 slowly changing dimensions - Theory

Lecture 40 Implement Type 0 slowly changing dimensions - Practice Example

Lecture 41 Implement Type 1 and Type 2 slowly changing dimensions - Practical Example

Section 9: Create objects

Lecture 42 22. Create and manage shortcuts

Lecture 43 44. Implement file partitioning for analytics workloads using a pipeline

Lecture 44 44. Implement file partitioning for analytics workloads - data is in a lakehouse

Section 10: Optimize performance

Lecture 45 39. Identify and resolve data loading performance bottlenecks in dataflows

Lecture 46 39. Implement performance improvements in dataflows

Lecture 47 40. Identify and resolve data loading performance bottlenecks in notebooks

Lecture 48 40. Implement performance improvements in notebooks, inc. V-Order

Lecture 49 44. Identify and resolve issues with Delta table file: optimized writes

Section 11: Other Fabric topics

Lecture 50 Recommend settings in the Fabric admin portal

Lecture 51 Implement workspace and item-level access controls for Fabric items

Lecture 52 Installing the Microsoft Fabric Capacity Metrics app

Lecture 53 Using the Microsoft Fabric Capacity Metrics app - Manage Fabric capacity

Section 12: Congratulations for completing the course

Lecture 54 What's Next?

Lecture 55 Congratulations for completing the course

This course is for you if you want to implement data engineering solutions using Microsoft Fabric,You will able to able to use PySpark to query streaming data,By the end of this course, after entering the official Practice Tests, you could enter (and hopefully pass) Microsoft's official DP-700 exam.,Wouldn't the "Implementing Data Engineering Solutions Using Microsoft Fabric" certification look good on your CV or resume?

[Image: dVFRxFPU_o.jpg]