Azure Data Engineer-AI-powered Data Engineering

Example

Implementing scalable solutions using Azure Blob Storage and Data Lake for storing massive datasets.

Scenario

A healthcare company needs to securely store patient data across various formats and structures, requiring flexible, scalable storage and easy integration with analytics tools.

Example

Creating data pipelines in Azure Data Factory to integrate and transform data from multiple sources.

Scenario

A retail business gathers data from sales, customer feedback, and supply chain systems, necessitating efficient pipelines that can preprocess and consolidate data for analytical reports.

Example

Leveraging Azure Databricks for big data analytics and machine learning to derive insights from large volumes of data.

Scenario

An online retailer uses machine learning models on customer data to predict buying patterns and recommend products in real-time, requiring robust big data processing capabilities.

Example

Using Azure Stream Analytics for real-time analytics on data streamed from IoT devices.

Scenario

A manufacturing firm monitors equipment health through sensors. Real-time data processing allows for immediate alerts and automated responses to potential failures or maintenance needs.

Example

Configuring Azure security features like Advanced Data Security and role-based access controls to ensure data protection and compliance with regulations.

Scenario

A financial services provider needs to comply with GDPR and other regulations, requiring stringent data security measures and audit capabilities.