Azure Synapse Analytics Training in Ameerpet | Azure

1
Advanced Analytics and Machine Learning in Azure
Synapse
2
Introduction to Azure Synapse Analytics

• Overview
• Brief introduction to Azure Synapse Analytics
• Key features integrated analytics service, data
  integration, big data, and data warehousing
• Importance of advanced analytics and machine
  learning in modern data platforms

3
Components of Azure Synapse Analytics

• Synapse Studio
• Unified workspace for data integration,
  exploration, preparation, management, and serving
• SQL Analytics
• Traditional and on-demand serverless querying
• Spark Pools
• Big data processing using Apache Spark
• Data Integration
• Pipelines for ETL/ELT processes

4
Advanced Analytics in Azure Synapse

• Capabilities
• Integrating big data and data warehousing
• Real-time analytics
• Data exploration and visualization
• Tools
• Power BI for visualization
• Synapse Studio for data exploration and
  management

5
Machine Learning in Azure Synapse

• Integration with Azure Machine Learning
• Building, training, and deploying machine
  learning models
• Use of Synapse Spark and SQL pools for data
  preparation and model scoring
• Use Cases
• Predictive analytics
• Anomaly detection
• Customer segmentation

6
Workflow for Machine Learning in Azure Synapse

• Steps
• Data Ingestion Ingest data from various sources
• Data Preparation Clean and transform data using
  Synapse Spark and SQL
• Model Training Use Azure Machine Learning to
  train models
• Model Deployment Deploy models using Synapse and
  Azure ML integration
• Model Scoring Score data within Synapse using
  deployed models
• Tools Involved
• Synapse Pipelines
• Synapse Spark
• Azure Machine Learning

7
Case Study Implementing Advanced Analytics and
ML in Azure Synapse

• Scenario
• Industry Retail
• Problem Demand forecasting for inventory
  management
• Solution
• Data ingestion from sales, inventory, and
  external data sources
• Data preparation and feature engineering using
  Synapse Spark
• Model training for demand forecasting using Azure
  ML
• Real-time scoring and dashboarding in Power BI
• Results
• Improved inventory management
• Reduced stockouts and overstock situations
• Enhanced decision-making with real-time insights

8
QA and Next Steps

• Questions and Discussion
• Invite questions from the audience
• Next Steps
• Encourage attendees to explore Azure Synapse
  Analytics
• Provide resources for further learning (links to
  documentation, tutorials, and workshops)
• Offer a demo or trial access to Azure Synapse
  Analytics

9
CONTACT

• Azure Synapse Analytics
• Address- Flat no 205, 2nd Floor,
• Nilgiri Block, Aditya Enclave,
• Ameerpet, Hyderabad-1 
• Ph. No 91-9989971070 
• Visit www.visualpath.in 
• E-Mail online_at_visualpath.in

10
THANK YOU

• Visitwww.visualpath.in