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Description of  Microsoft Fabric End To End Data Engineering Project

Published 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.45 GB | Duration: 3h 39m Build Bing News Data Analytics platform using different Data Engineering components of Microsoft Fabric[DP-600][DP-203] What you'll learn You will learn to use Microsoft Fabric for building a Bing News Data Analytics platform, enabling seamless integration with Azure Data Engineering components You will learn the process of ingesting data from external sources, specifically utilizing Bing API, using Data Factory. You will learn to perform data transformation techniques to shape and refine raw JSON data into curated Delta Tables using Synapse Data Engineering component You will learn how to perform sentiment analysis using Synapse Data Science component You will learn how to orchestrate data workflows with Data Factory pipelines. You will learn how to perform Incremental Load using spark notebooks. You will learn how to visualize data effectively using Power BI. You will learn how to configure alerts within Power BI visuals with Data Activator. Requirements Basic Programming Skills Description In this project, we will build a Bing News Data Analytics platform! This would be a complete end to end Azure Data Engineering project that's done using Microsoft Fabric. We'll pull raw data from Bing API, transform the raw data to clean data with Synapse Data Engineering, analyze sentiment with Synapse Data Science, set up workflows with Data Factory, make cool reports with Power BI, set alerts with Data Activator, and test everything well. Let's get started on Bing News Analytics! The Topics covered in this Project are, 1. Data Ingestion from Bing API using Data Factory: Learn how to seamlessly pull in data from external sources, setting the foundation for your analytics project. 2. Data Transformation using Synapse Data Engineering: Dive into the process of shaping and refining your raw JSON data to a curated Delta Table, including techniques like incremental loading to keep your processes efficient. 3. Sentiment Analysis using Synapse Data Science: Uncover insights hidden within the news description by predicting the sentiment of the news classified as Positive, Negative or Neutral. 4. Orchestration using Data Factory via pipelines: Discover the art of orchestrating your data workflows, ensuring smooth and efficient operations. 5. Data Reporting using Power BI: Visualize your data in a compelling and actionable manner, empowering stakeholders with valuable insights. 6. Configuring Alerts using the Data Activator: Stay ahead of potential issues by setting up alerts and notifications within your Power BI visuals using a new tool called Data Activator. 7. End to End Pipeline Testing: The complete flow will be tested right from the data ingestion to the data transformation and until the report gets updated with the incoming new data to Validate the integrity and performance of your pipelines, ensuring reliability and accuracy. This project revolves around Bing News Data Analytics, a practical application that involves ingesting news data daily and generating insightful reports. By walking through each step in a simplified manner, I aim to make Azure Data Engineering accessible to all enthusiasts, regardless of their background. Pre-requisitesYou don't need to have any skills to do this project. Even if you are an absolute beginner, if you follow the entire course, you will be able to implement this project, The only pre-requisite for this project is, you need to two introductory videos of Microsoft Fabric which is included as part of this course in the section 1. All the very best and ! Overview Section 1: Introduction (Pre-requisites) Lecture 1 Introduction to Microsoft Fabric Lecture 2 Create and Enable Microsoft Fabric Section 2: Build an End to End Project using Microsoft Fabric Lecture 3 Project Overview Lecture 4 Environment Setup Lecture 5 Data Ingestion using Data Factory Lecture 6 Data Transformation using Synapse Data Engineering Lecture 7 Incremental Load using Spark Notebook Lecture 8 Sentiment Analysis using Synapse Machine Learning Lecture 9 Building Reports using Power BI Lecture 10 Read Me (*Important*) Lecture 11 Building Pipelines using Data Factory for Orchestration Lecture 12 Setting up Alerts using Data Activator Lecture 13 End to End Pipeline Testing