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Azure Data Engineering 2023 | Day 3 Session - Intro to Sql



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Data Engineering is the foundation for the new world of Big Data. Enroll Now to build production-ready data infrastructure, an essential skill for advancing your data career.

SQL (Structured Query Language) is of paramount importance for a Data Engineer for several reasons:

For Azure data engineering, SQL remains an essential skill, but there are additional Azure-specific technologies and services that are crucial for a Data Engineer working in the Microsoft Azure ecosystem. Here's a concise list:

Azure Data Factory: A cloud-based data integration service that allows you to create data pipelines for orchestrating and automating data movement and transformation from various sources to various destinations.

Azure Databricks: A collaborative Apache Spark-based analytics service that enables big data processing and advanced analytics. It integrates with other Azure services for data engineering workflows.

Azure Synapse Analytics (formerly Azure SQL Data Warehouse): A powerful analytics service that combines big data and data warehousing capabilities to support data integration, exploration, and reporting.

Azure SQL Database: A fully-managed relational database service that provides high availability, security, and scalability for hosting mission-critical applications.

Azure Cosmos DB: A globally distributed NoSQL database service that supports various data models and provides low-latency access for globally distributed applications.

Azure HDInsight: A cloud-based big data service that allows you to deploy and manage popular open-source big data technologies like Hadoop, Spark, Hive, and more.

Azure Blob Storage: A scalable and cost-effective object storage service used for storing unstructured data like files, images, and backups.

Azure Stream Analytics: A real-time data streaming service that enables the processing and analysis of data from IoT devices and other sources.

Azure Data Lake Storage: A scalable and secure data lake service that allows you to store and analyze structured and unstructured data.

Azure SQL Managed Instance: A fully-managed version of SQL Server that provides compatibility with on-premises SQL Server databases and allows for easy migration to the cloud.

In addition to these Azure-specific technologies, it's essential for Azure Data Engineers to be familiar with data security and governance practices in the Azure environment, as well as other tools like PowerShell and Azure DevOps for automation and CI/CD (Continuous Integration/Continuous Deployment) processes.






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Azure Data Engineering is a comprehensive set of cloud-based services and tools provided by Microsoft's Azure platform, designed to support the end-to-end data lifecycle, from data ingestion to data visualization and analysis. As businesses generate vast amounts of data, the need for efficient data engineering solutions becomes paramount. Azure Data Engineering simplifies the process of managing and processing data, allowing organizations to derive valuable insights and make informed decisions.

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Trainer :Mr. Santhosh

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#Azure Data Factory: Microsoft's cloud-based data integration service.
#Azure Data Lake: A scalable data storage and analytics service.
#Azure Synapse Analytics: An integrated analytics service for data warehousing and big data analytics.
#Azure SQL Database: A managed cloud database service based on Microsoft SQL Server.
#Azure Cosmos DB: A globally distributed NoSQL database service.
#Azure Blob Storage: Object storage service for unstructured data.
#Data Pipelines: ETL (Extract, Transform, Load) and data movement processes in Azure.
#Data Orchestration: Managing and coordinating data workflows in Azure.
#Data Transformation: Converting and shaping data for analytical purposes.
#Data Integration: Combining data from multiple sources for analysis and reporting.
#Data Warehousing: Storing and managing large volumes of structured data.
#Big Data Analytics: Analyzing large and complex datasets using Azure services.
#Data Governance: Implementing policies and rules for data management and compliance.
#Data Security: Ensuring data protection and privacy in Azure.
#Azure Machine Learning: Utilizing machine learning capabilities in Azure for data engineering tasks.
#Data Visualization: Presenting data insights and trends in visual formats.
#Azure Databricks: A collaborative workspace for big data analytics and machine learning.
#Data Engineering Best Practices: Tips and strategies for effective data engineering in Azure.
#Azure DevOps: Integrating data engineering processes into CI/CD pipelines.
#Azure Data Catalog: A centralized data discovery and metadata management service
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