Microsoft Azure (formerly Windows Azure) is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. Azure Provides Three services: software as a service (SaaS), platform as a service (PaaS).infrastructure as a service (IaaS) Azure supports many different programming languages, tools, and frameworks, including both Microsoft-specific and third-party software and systems. Azure was announced in October 2008, started with the codename “Project Red Dog”, and released on February 1, 2010, as “Windows Azure” before being renamed “Microsoft Azure” on March 25, 2014.In this course, you will learn: How To Use The Azure Data Factory. How To Use The Azure SQL Database. How To Use Azure Blob Storage. How To Use Azure Data Lake. How To Use Azure DataBricks. How To Use Different Azure Data Services In Different applications. Exam DP-200: Implementing an Azure Data Solution: Candidates for this exam must be able to implement data solutions that use the following Azure services: Azure Cosmos DB. Azure SQL Database. Azure Synapse Analytics (formerly Azure SQL DW).Azure Data Lake Storage. Azure Data Factory. Azure Stream Analytics. Azure Databricks. Azure Blob storage. Topics We Cover In This Course: Azure SQL Database. Azure Cosmos DB. Azure Data Lake Storage. Azure Data Factory. Azure Databricks. Azure Blob storage. Azure Synapse Analytics (formerly Azure SQL DW).Upcoming Modules;Azure SQL failover groupsAzure Data Lake AnalyticsIntroduction To Power BIHDinsight. Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions. Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. This course covers, how to provisioning data storage services like Azure SQL, Storage account, Data lakes. In the Azure Data factory section, we cover how to transform your data, identifying performance bottlenecks, and accessing external data sources including on-premise SQL server and file systems. Azure Data Factory (ADF):The Azure Data Factory service is a fully managed service for composing data storage, processing, and movement services into streamlined, scalable, and reliable data production pipelines. The Azure Data Factory (ADF) is a service designed to allow developers to integrate disparate data sources. ADF or Azure Data Factory is a platform somewhat like SSIS or Alteryx in the Azure environment to manage the data you have both on-prem and in the cloud. It provides access to on-premises data with the help of a software. By using this link we could connect to the on-premise file system as well as to on-premise SQL databases. From Azure Data Factory, you could access almost all azure services without any difficulties. Access to on-premises data is provided through a data management gateway that connects to on-premises SQL Server databases and we will show you how to install this software and how to connect your on-premises environment with Azure cloud. If you ever created any data transfer activities in Azure, or in SSIS, you will find it a similar tool. If you use ADF, you could focus on your datathe serverless integration service does the rest. Topics In Azure Data Factory: Append Variable ActivityExecute Pipeline activityForEach activityGet Metadata activityIf Condition activityLookup activitySet variable activityUntil activityValidation activityData Flow activityMapping data flowAggregate transformation. Alter row transformation. Conditional split transformation. Derived column transformation. Exists transformation. Join transformation. Lookup transformation. The new branch mapping data flow transformation. Select transformation. Sink transformation. Source transformation. Azure Data Factory union transformation. ParameterizingTrigger In Azure Data Factory. Manual Trigger. Scheduled Trigger. Tumbling window Event Trigger. Dynamic Data processing And Pipeline Execution Based on External Event. and many more (with real-life scenarios). Check out our course descriptions for updated information. SQL Database -Cloud Database as a Service: Azure SQL Database is a fully managed relational database with built-in intelligence supporting self-driving features such as performance tuning and threat alerts. According to Wiki, Microsoft Azure SQL Database is a managed cloud database provided as part of Microsoft Azure. A cloud database is a database that runs on a cloud computing platform, and access to it is provided as a service. Managed database services take care of scalability, backup, and high availability of the database. Azure SQL Database: Azure SQL Database is a relation
