Hello again. You are now halfway through your practice exams and your next step is to recap Course 3, Non relational data in Microsoft Azure. In this lesson you will recap key concepts and take a practice exam on the topics covered in Course 3. In Course 3, you learned about non relational data in Microsoft Azure. You study the benefits of Azure Table storage, Azure Blob storage, Azure File storage and Azure Cosmos DB. You saw that Azure Table storage is a scalable model held in the cloud that implements the no SQL key value model. In an Azure Table storage table, items are referred to as rows and fields are known as columns. An Azure table enables you to store semi structured data. All rows in a table must have a key, but apart from that, the columns in each row can vary. The primary advantages of using Azure table storage tables over other ways of storing data include, it's simpler to scale. It takes the same time to insert data in an empty table or a table with billions of entries. A Microsoft Azure Storage account can hold up to 500 terabytes of data. A table can hold semi structured data. There's no need to map and maintain the complex relationships typically required by a normalized relational database. Row insertion is fast and data retrieval is fast, if you specify the partition and rockies as query criteria. There are disadvantages to storing data this way though, including consistency needs to be given consideration as transactional updates across multiple entities aren't guaranteed. There is no referential integrity. Any relationships between rows need to be maintained externally to the table. It's difficult to filter and sort on non-key data. Queries that search based on non-key fields, could result in full table scans. You also explored Azure Blob storage which enables you to store massive amounts of unstructured data or blobs in the cloud. You saw that there are three types of blobs. They are block blobs, page blobs and the pen blobs. You create blobs using a Microsoft Azure Storage account and blobs are created within containers. Blob storage provides three different access tears, which helps to balance storage costs and latency. The hot tier is for blobs that are accessed frequently. The blob data is stored on high performance media. The cool tier has lower performance and encourage reduced storage charges compared to the hot tier. It is for data that is accessed infrequently. The archive tier provides the lower storage costs, but with increased latency. The archive tier is intended for historical data. Common uses of Azure Blob storage include serving images or documents directly to a browser in the form of a static website, storing files for distributed access, streaming video and audio, storing data for backup and restore, disaster recovery and archiving and storing data for analysis by an on premises or Azure hosted service. You also saw that Azure File storage enables you to create file shares in the cloud and access these file shares from anywhere with an Internet connection. Azure File storage is designed to support scenarios such as migrate existing applications to the cloud, share server data across on premises and cloud, integrate modern applications with Azure File storage. And Azure File storage can offer support to simplify hosting high availability workload data. You explored Azure Cosmos DB, a multi model, no SQL database management system. Cosmos DB manages data as a partition set of documents. A document is a collection of fields identified by a key. The fields in each document can vary and the field can contain child documents. Many document databases use JSON to represent the document structure. Cosmos DB provides API's that enable you to access these documents using a set of well known interfaces. These interfaces include SQL API, Table API, Mongo DB API, Cassandra API and Gremlin API. You also saw that graph is a collection of data objects and directed relationships. Data is still held as a set of documents in Cosmos DB. But the Gremlin API enables you to perform graph queries over data. Cosmos DB is highly suitable for the following scenarios, IOT and telematics, retail and marketing, gaming and web and mobile applications. You also learned how to provision non relational data services. You can use the Azure portal, the Azure CLI, Powershell or Azure Resource Manager templates to provision Azure Cosmos DB, create a database and the container. You also learned you can use the Azure portal, the Azure CLI, as your Powershell and Azure Resource Manager templates to provision other non relational data services such as Data lake storage, Blob storage and File storage. Additionally, you saw that you can create a storage account using the Azure portal, the Azure CLI or PowerShell. You also explored how to configure the services you provision to meet the needs of your applications and environment. You learned how to enable network access to your resources and how you can prevent accidental exposure of your resources to third parties. You also saw how to use authentication and access control to protect the data managed by your resources. You also learned how security implements threat protection and assessment. Threat protection tracks security incidents and alerts across your service. You saw how to use shared access signatures, SAS, to grant limited rights to resources in an Azure storage account for a specified time period. Finally, you learned how to manage non relational data stores in Azure. You explored how to upload data to a Cosmos DB database and how to configure Cosmos DB to support bulk loading. Cosmos DB provides several options for uploading data to a Cosmos DB database and querying that data, including Data Explorer, Cosmos DB Data Migration tool, Azure Data Factory, Cosmos DB BulkExecuter library and Cosmos DB SQL API client library. You also saw that although Azure Cosmos DB is described as a no SQL database management system, the SQL API enables you to run SQL like queries against Cosmos DB databases. These queries uses syntax similar to that of SQL, but there are some differences. This is because the data in a Cosmos DB is structured as documents rather than tables. The SQL API returns results in the form of JSON documents. All queries are executed in the context of a single container. You also learned how to create and manage blobs and the containers that hold them. You can use the Azure portal, the Azure CLI, and Azure PowerShell for managing blobs and Blobs storage. You can also use the AzCopy utility to upload and download files, including blobs. You also saw that you can use Azure File storage to store shared files. Users can connect to a shared folder, also known as a file share and read and write files if they have the appropriate privileges in much the same way as they would use a folder on the local machine. You focused on the tools available in the Azure portal and the AzCopy Command. Microsoft provides two graphical tools you can use to create and manage file shares in Azure storage, the Azure portal and Azure Storage Explorer. You have recapped Course 3 where you learned how to provision non-relational data services, configure non-relational data services, explore basic connectivity issues, explore data security components, upload data to Cosmos DB database and query this data and upload and download data in an Azure storage account.
