Azure Data Engineer

  Architecture And Usage Of Different Azure Services Azure Microsoft cloud computing platform, offers a wide range of services to help organizations build, deploy, and manage various applications and solutions. Here's an overview of some key Azure services along with their architecture and common usage Azure Data Engineer Training Ameerpet Azure Virtual Machines (VMs): Architecture: Azure VMs provide on-demand computing resources with customizable configurations, including CPU, memory, and storage. They run on Microsoft's Hyper-V hypervisor technology. Usage: Ideal for migrating existing applications to the cloud, hosting websites, running development and test environments, and deploying enterprise applications.                                           - Azure Data Engineer...

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  Azure Blob Storage AzureBlob Storage  is a key component of Microsoft Azure, offering scalable, secure, and cost-effective object storage for a wide range of data types. Designed to handle massive amounts of unstructured data, Azure Blob Storage provides the foundation for storing and managing diverse content, from documents and images to videos and application backups.  - Azure Data Engineer Course Key Features and Concepts: 1.       Scalability: ·      AzureBlob Storage  is highly scalable, allowing organizations to store and retrieve large amounts of data with ease. It seamlessly scales as your storage needs grow. 2.       Blob Types: ·        Azure Blob Storage supports three main types of blobs, each optimized for specific scenarios: Block blobs, Page blobs, and Append blobs. This versatility enables users to choose the right type for their particular use case....

  What is Spark SQL? Spark SQL is an Apache Spark plugin that handles structured and semi-structured data.  It provides a programming interface for interacting with data using SQL queries and extends the capabilities of Spark to handle structured data.  - Azure Data Engineer Course Here are some of the key features of Spark SQL: 1.       Unified Data Processing: ·       Spark SQL unifies the capabilities of  Apache Spark's  batch processing engine with the power of SQL queries. This allows users to seamlessly mix SQL queries with Spark programs written in various languages like Scala, Java, Python, and R. 2.       DataFrame API: ·      Spark SQL  introduces the concept of DataFrames, a distributed collection of data organized into named columns. DataFrames provide a higher-level abstraction for structured data processing and allow users to express complex transfor...