?What is Azure Data Factory, and how does it contribute to Azure Data Engineering

Azure Data Factory is a cloud-based data integration service that allows users to create, schedule, and orchestrate data workflows across various data sources and destinations. It enables data engineers to efficiently ingest, transform, and load data from disparate sources into Azure data services like Azure Synapse Analytics or Azure SQL Database. With its visual interface and code-free development environment, Azure Data Factory simplifies the creation of complex data pipelines, facilitating data integration and analytics tasks in Azure Data Engineering.

? How does Azure Databricks enhance data engineering capabilities on Azure

Azure Databricks is a unified analytics platform that combines the power of Apache Spark with the scalability and reliability of Azure cloud services. It provides a collaborative environment for data engineers, data scientists, and analysts to work together on big data processing and machine learning tasks. Azure Databricks enables data engineers to process large volumes of data efficiently, perform complex data transformations, and build scalable data pipelines for batch and real-time processing. With its integrated development environment and built-in support for languages like Python, Scala, and SQL, Azure Databricks empowers data engineers to accelerate data engineering workflows and deliver actionable insights faster in Azure Data Engineering.

Azure Data Engineering Fundamentals Tutorial

What will you learn in this course?

Design and implement scalable data pipelines using Azure Data Factory and Azure Databricks for large-scale data processing
Optimize data storage and retrieval with Azure Data Lake Storage and Azure Synapse Analytics for efficient analytics workflows
Perform data transformations and cleansing to prepare high-quality data for analytics and machine learning applications

Ensure data security, compliance, and governance within Azure Data Engineering environments
Utilize Azure tools to monitor, troubleshoot, and improve data pipeline performance and reliability
Apply best practices for cloud-based data architecture and cost management in Azure Data Engineering

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Lessons | 6