Describe features of structured data

Structured data is a key component in the world of data management and analysis. It refers to data that is organized into predefined structures, with clearly defined categories, relationships, and attributes. In the context of Microsoft Azure, structured data plays a crucial role in various services and tools that enable data storage, processing, and analytics. In this article, we will explore some of the key features of structured data related to the Microsoft Azure Data Fundamentals exam.

Relational Databases

Relational databases are widely used to store structured data in Azure. Azure provides a fully managed relational database service called Azure SQL Database. It offers high availability, scalability, and security features for hosting relational database workloads. With Azure SQL Database, you can easily provision and configure databases, manage schemas, and perform CRUD operations using SQL queries. The data stored in relational databases is organized into tables, with columns representing attributes and rows representing records.

To create a relational database in Azure SQL Database, you can use the following steps:

1. Sign in to the Azure portal.
2. In the left-hand menu, click on "Create a resource" and search for "Azure SQL Database".
3. Click on "Azure SQL Database" and then click "Create" to start the configuration process.
4. Provide the necessary details such as subscription, resource group, server details, and pricing tier.
5. Configure the connection security, firewall rules, and authentication methods.
6. Specify the database name and select the collation and data source.
7. Configure advanced database settings such as data retention, backup, and geo-replication.
8. Finally, review the configuration and click on "Create" to provision the relational database in Azure SQL Database.


Blob Storage

Azure Blob Storage is another feature-rich service for storing structured data in Azure. It provides highly scalable and durable storage for various types of data, including structured data in the form of files. Blob storage supports three types of blobs: block blobs, append blobs, and page blobs. Block blobs are commonly used to store large amounts of structured data, such as text files, CSV files, or XML files.

To store structured data in Azure Blob Storage, you can use the following steps:

1. Sign in to the Azure portal.
2. In the left-hand menu, click on "Create a resource" and search for "Azure Storage account".
3. Click on "Azure Storage account" and then click "Create" to start the configuration process.
4. Provide the necessary details such as subscription, resource group, storage account name, and location.
5. Configure the performance and redundancy options.
6. Specify the storage account settings such as access tier and secure transfer required.
7. Enable or disable advanced options such as hierarchical namespace and file share encryption.
8. Finally, review the configuration and click on "Create" to provision the Azure Blob Storage account.


Azure Data Lake Storage

Azure Data Lake Storage is a scalable and secure cloud-based storage solution designed for big data analytics workloads. It offers two storage tiers: hot storage and cool storage. Structured data can be stored in Azure Data Lake Storage in various formats such as Parquet, Avro, JSON, or delimited text files. These formats enable efficient data querying and analysis using various Azure services like Azure Databricks, Azure Data Factory, or Azure HDInsight.

To create Azure Data Lake Storage, you can follow these steps:

1. Sign in to the Azure portal.
2. In the left-hand menu, click on "Create a resource" and search for "Azure Data Lake Storage Gen2".
3. Click on "Azure Data Lake Storage Gen2" and then click "Create" to start the configuration process.
4. Provide the necessary details such as subscription, resource group, storage account name, and location.
5. Configure the performance and redundancy options.
6. Specify the storage account settings such as access tier and secure transfer required.
7. Enable or disable advanced options such as hierarchical namespace and file share encryption.
8. Finally, review the configuration and click on "Create" to provision the Azure Data Lake Storage account.


Azure Synapse Analytics

Azure Synapse Analytics provides a unified analytics service that blends big data and data warehousing capabilities. It allows you to analyze structured data along with unstructured data using familiar tools and frameworks. With its seamless integration with Azure Data Lake Storage and Azure SQL Data Warehouse, it enables efficient data exploration, transformation, and visualization.

To start using Azure Synapse Analytics, you can follow these steps:

1. Sign in to the Azure portal.
2. In the left-hand menu, click on "Create a resource" and search for "Azure Synapse Analytics (formerly SQL Data Warehouse)".
3. Click on "Azure Synapse Analytics (formerly SQL Data Warehouse)" and then click "Create" to start the configuration process.
4. Provide the necessary details such as subscription, resource group, workspace name, and region.
5. Configure the security settings such as virtual network and firewall rules.
6. Specify the compute and scale settings based on your workload requirements.
7. Enable or disable advanced options such as vulnerability assessment and backup storage.
8. Finally, review the configuration and click on "Create" to provision Azure Synapse Analytics.


In summary, Microsoft Azure provides a wide range of services and tools to work with structured data. Whether it is storing data in relational databases, blob storage, data lake storage, or analyzing it using Azure Synapse Analytics, Azure offers a comprehensive ecosystem for managing and analyzing structured data. Familiarizing yourself with these features can help you succeed in the Microsoft Azure Data Fundamentals exam.