Data Engineering with Microsoft Azure is to cover essential concepts and practical skills related to designing, building, and managing data infrastructure and pipelines using Microsoft Azure's cloud services. Participants may learn about Azure data storage, processing, and integration tools, including Azure Data Factory, Azure SQL Database, Azure Databricks, Azure Synapse Analytics, and more. The course might emphasize best practices for data engineering in a cloud environment and provide hands-on experience with real-world scenarios.
Possible Careers:
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Data Engineer:
- Designing and implementing data pipelines.
- Managing and optimizing data storage solutions.
- Ensuring data quality and integrity.
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Azure Data Engineer:
- Specializing in data engineering tasks specifically within the Microsoft Azure ecosystem.
- Leveraging Azure services for data processing, analytics, and storage.
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Big Data Engineer:
- Working with large-scale data processing frameworks such as Apache Spark on Azure Databricks.
- Handling and optimizing big data workflows.
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Cloud Data Architect:
- Designing and overseeing the architecture of data solutions on cloud platforms.
- Ensuring scalability, security, and performance of data systems.
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Business Intelligence Developer:
- Using Azure services to create business intelligence solutions.
- Designing dashboards and reports for data visualization.
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Data Warehouse Developer:
- Building and maintaining data warehouses using Azure Synapse Analytics (formerly SQL Data Warehouse).
- Optimizing data storage and retrieval for analytical purposes.
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Machine Learning Engineer (with a focus on data):
- Integrating data engineering processes into machine learning workflows.
- Managing and preprocessing data for machine learning models.
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Data Integration Specialist:
- Focusing on seamless integration of data from various sources using Azure Data Factory and other tools.
- Ensuring data consistency and coherence across the organization.