UiPath Training in Chennai

Task 4:

• Design a scalable data storage solution using Azure Data Lake and Blob Storage for a company that processes large volumes of unstructured data.

Task 5:

• Explain how data replication can improve data availability and redundancy in Azure.

Task 6:

• Develop a cost optimization strategy for managing storage costs in Azure Blob Storage for archival purposes.

Task 7:

• Build a data pipeline using Azure Data Factory to extract, transform, and load data from multiple sources.

Task 8:

• Compare real-time vs batch processing pipelines and their use cases in different industries.

Task 9:

• Design a custom data pipeline that integrates data from on-premise and cloud sources, detailing the tools and services used.

Task 10:

• Create a big data solution using Azure Synapse Analytics and HDInsight for processing terabytes of data.

Task 11:

• Explain how to manage costs when scaling big data solutions on Azure.

Task 12:

• Design a batch and stream processing system for real-time and historical data analysis using Spark and Synapse Analytics.

Task 13:

• Implement a security strategy using Azure Key Vault to store and manage encryption keys for sensitive data.

Task 14:

• Outline how to ensure GDPR compliance when handling user data on Azure.

Task 15:

• Design a Role-Based Access Control (RBAC) model for a medium-sized company using Azure Active Directory.

Task 16:

• Optimize queries for an Azure SQL Data Warehouse to reduce execution time and improve performance.

Task 17:

• Explain how to scale Azure Blob Storage for a growing business that requires faster data access.

Task 18:

• Develop a data sharding strategy to optimize partitioning for a globally distributed database system.

Task 19:

• Design a real-time analytics solution using Azure Stream Analytics and Event Hubs for IoT data ingestion.

Task 20:

• Build a real-time dashboard with Power BI that updates dynamically using streaming data.

Task 21:

• Implement a system that handles real-time queries and analysis for a financial trading platform using Azure Functions.

Task 22:

• Develop an ETL process using Azure Data Factory and Databricks to integrate and clean data from multiple sources.

Task 23:

• Automate data transformation workflows in Azure to process data on a daily schedule.

Task 24:

• Create a Power BI report that visualizes transformed data and provides actionable insights.

Task 25:

• Build a predictive model using Azure Machine Learning and deploy it to a data pipeline for real-time analytics.

Task 26:

• Implement a machine learning solution to forecast sales for a retail company using Databricks.

Task 27:

• Design an end-to-end AI-powered data processing pipeline using Azure Cognitive Services.

Task 28:

• Design and implement a comprehensive data architecture that includes storage, pipelines, and security on Azure.

Task 29:

• Develop and present a real-time data solution that integrates machine learning and advanced analytics.