Azure Data Engineering with ADF, ADB, Synapse and PowerBI | Udemy
English | Size: 1.62 GB
Genre: eLearning[/center]

Step by Step Data Integration and Reporting

What you'll learn
Azure Data Engineer End to End Project Execution
Within 2 weeks you can learn how to implement projects
You can implement your own projects after completion of this course

Project Description:

In today's data-driven world, organizations need robust platforms to efficiently collect, process, analyze, and visualize data to drive informed decision-making. This project aims to design and implement a unified data platform leveraging Microsoft Azure services including Azure Data Factory (ADF), Azure Synapse Analytics, Azure Databricks, and Power BI.

Project Goals:

Data Ingestion and Integration: Utilize Azure Data Factory to orchestrate the ingestion of data from various sources including databases, files, and streaming sources. Implement data integration pipelines to cleanse, transform, and load data into a centralized data lake or data warehouse.

Scalable Data Warehousing: Deploy Azure Synapse Analytics (formerly SQL Data Warehouse) to create a scalable and performant data warehouse solution. Design and optimize data models to support complex analytical queries and reporting requirements.

Advanced Analytics and Data Processing: Leverage Azure Databricks to perform advanced analytics, machine learning, and data processing tasks on large volumes of data. Develop Spark-based ETL processes, build predictive models, and perform data exploration and visualization using Python or Scala.

Real-time Analytics and Streaming: Implement real-time data processing and analytics using Azure Stream Analytics or Azure Databricks streaming capabilities. Analyze streaming data from IoT devices, sensors, or social media platforms to gain insights and detect patterns in real-time.

Business Intelligence and Reporting: Develop interactive dashboards and reports using Power BI to visualize insights derived from the unified data platform. Create rich visualizations, drill-down reports, and executive dashboards to empower business users with actionable insights.

Project Deliverables:

Azure Data Factory pipelines for data ingestion, transformation, and loading.

Azure Synapse Analytics data warehouse with optimized data models.

Azure Databricks notebooks for advanced analytics, ETL, and machine learning.

Real-time analytics solutions using Azure Stream Analytics or Azure Databricks streaming.

Power BI dashboards and reports for business intelligence and decision support.

Project Benefits:

Streamlined data integration and processing workflows.

Improved data accuracy, consistency, and reliability.

Enhanced scalability and performance for analytics workloads.

Real-time insights for proactive decision-making.

Empowered business users with self-service analytics and reporting capabilities.

Conclusion:

This project aims to demonstrate the capabilities of Microsoft Azure services for building a modern data platform that integrates data from disparate sources, performs advanced analytics, and delivers actionable insights to drive business success.

Who this course is for:
Azure Data Engineering Lovers

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