Greetings, data enthusiasts!
Our May meeting is scheduled for Monday, May 8, at 5:30 pm! We will meet in person at Rensselaer Chamber of Commerce, 90 4th Street, Troy, NY.
For more information and to RSVP, go to our Meetup event page at https://www.meetup.com/capital-area-sql-server-user-group/events/290678891
Our meeting schedule is usually as follows:
5:30 PM: Food, soft drinks, and networking
6:15 PM: Chapter news and announcements
6:30 PM: Presentation
We usually wrap up between 7:30 PM and 8:00 PM.
Thanks to Datto for sponsoring our event!
Our guest speaker for the month is Deexith Reddy!
Topic: Automating Visualizations with Azure Data Factory, SQL Server, and Power BI: Data to Insights
Introduction to Azure Data Factory, SQL Server, and Power BI: Gain an understanding of these three powerful Microsoft technologies and their roles in automating data visualizations.
Data Integration and Transformation: Learn how to leverage Azure Data Factory to ingest and transform data from various sources, then store the processed data in SQL Server for further analysis.
Creating and Automating Power BI Reports: Discover how to create interactive Power BI reports and dashboards using data from SQL Server, and automate the process of updating these visualizations.
Scheduling and Orchestration: Understand how to use Azure Data Factory’s scheduling and orchestration features to automate the entire data pipeline, from data ingestion to visualization updates.
Real-Time Data Processing and Visualization: Explore the possibilities of real-time data processing and visualization using Azure Data Factory, SQL Server, and Power BI, enabling you to monitor and react to changes in your data as they occur.
Security and Compliance: Learn about the built-in security features and best practices for protecting your data and maintaining compliance throughout the automation process.
Best Practices and Tips: Gain valuable insights into best practices for automating data visualizations, including performance optimization, error handling, and maintaining data quality.