INTRODUCTION TO AZURE DATA SERVICE

Data fuels the modern internet and there can be no applications without it. The hunger for data has been constantly increasing, which has created a need for businesses to come up with a solution that will make working with data more efficient and effective. Microsoft Azure is an excellent answer to this need. It provides comprehensive end-to-end data services that meet all the needs; for example databases and data management; processing and real-time data collection with data insights and analytics; AI and machine learning. In this article, we will dive deep into the azure data service and try to understand its importance and various uses.

1. What is Azure Data Services?

Microsoft Azure provides solutions to a wide variety of data-related queries. It covers simple needs such as file storage and relational databases, but also more specialized services like text searching and time-series data.

Azure SQL Database is a managed service that hosts SQL Server databases. The SQL database is maintained by Microsoft, you still need to do some micro-adjustments to make it fail-safe.

Azure Cosmos DB offers a global cloud service. It is the first multi-model database that can store and query documents, graphs, columnar data, and NoSQL tables. To make the most of this service, one needs to understand which consistency and performance guarantees to choose and how to make it reliable on a global scale.

Azure Data Factory lets you create a data processing pipeline that automates the movement of data and its transformation.

Azure Analysis Services allows you to create data models to make sense of existing data. It acts as an intermediary between databases and business intelligence clients.

2. What are the benefits of Azure Data Services?

One can transform their business simply with the application of AI and Azure Data Services. Particularly AI and machine learning have radically transformed how people do their business. Azure Data Services integrates AI and machine learning deep and democratizes their use. For example, it is possible to run machine learning models from the SQL Server itself by using Python or R-based models working off data in SQL Server itself.

Azure Data Service fulfills a wide range of needs

Data is the foundation of all, be it databases, machine learning, or analytical workload. Microsoft’s Azure Services makes sure to cover all of these needs. It does not matter if you prefer a SQL Server or an open-source alternative; Azure Data Services is compatible with either of these platforms. Azure Cosmos DB can provide a wide variety of databases including a singular database, a multi-model database, and a globally distributed database that are compatible with different data formats and APIs. For the end-users, it is easy to adapt to and construct scalable mission-critical applications without worrying about infrastructure management, capacity, and integrations. On the other hand, other service providers spread these functions into different database services, unlike Azure Data Services.

Azure Data Services are built for efficiency

Microsoft has focused for a long time on UI-based services. The increasingly popular notion of low code today has left little value in getting bogged down with software development for business applications when low code can provide an easily achievable solution. Companies, on the other hand, can utilize the power of Azure Data Services and Power Platform, integrating to build solutions quickly and efficiently which can save a lot of time and resources and can be reallocated to focus on critical business needs.

Maintainability is at the core of Azure Data Services

Building an app or a data solution is a one-time effort but companies often don’t account for the cost and effort involved in maintaining and expanding the solution. Solutions that are code-heavy take up a lot of time and effort to keep the integrations up to date. Microsoft Azure Data Services comes with a solution with a low code approach to make things easy for you. It uses the always updated Data Factory’s prebuilt connectors to ensure the integration of data from a few hundred sources and target services. These services keep updating their APIs and integration points.

Azure Data Service prize integration

Data has become very important in all industries, especially the healthcare industry. It helps in patient management, managing customer and patient relationships, collaboration within a team, business applications, analytics, and AI. One excellent example of Microsoft’s comprehensive service integration is Microsoft Cloud for Healthcare. This leverages Azure Data Services, Microsoft Power Platform on Azure, Dynamics 365, and Office 365. The combination of all these apps can immensely help a business be more efficient and innovative.

3. How to create an Azure Data Service?

Microsoft Azure Data Services is a service that is based on a cloud computing platform. The user is able to use a cloud-based data service for a vast variety of functions and benefits. To access these services the user first has to create an Azure Cloud Service.

The process of creating such a cloud service is simple and convenient. The interested user can access the Azure portal through their browser.

In the portal, the user will find the option to Create a Resource> Compute > Cloud Service. In the DNS name input box, the user has to enter the URL prefix for the cloud service. The user then has to specify a new Resource group for the service and choose the region in which the user wishes to use such an application and then select the option to create. With the help of this cloud, the User can access and use a large range of Azure Data Services that Azure provides.

The Azure data services are designed for different ranges as well as different needs. Some of these Azure data services are the Azure SQL Data Warehouse, the Azure Data Lake, and the Azure SQL Database elastic pool. These services each provide a specific range of services and of different magnitudes as demanded by the user. All services however are user-friendly, capable of processing and performing large workload, and provides optimum security for the best user experience.

4. How to create a data source?

Before creating a data source, finalize the database connection parameters for the data source you want to connect to. Following are the steps to create a data source:

Adding the Driver to the Classpath

The addition of the required driver to the WebLogic classpath is the first step in creating a data source. To add a driver to the WebLogic classpath:

1. Locate the <bea_home>weblogic81samplesdomains<domain name> directory.
2. Open the setDomainEnv.cmd and scroll to the bottom of the file where it reads: @REM SET THE CLASSPATH Set CLASSPATH=%PRE_CLASSPATH%;%WEBLOGIC_CLASSPATH…
3. Add the following statement immediately AFTER @REM SET THE CLASSPATH: set CLASSPATH=%WL_HOME%serverlibdriver.jar;%CLASSPATH% where driver.jar is the jar file for the database driver that you want to add to the classpath. For example, for an Oracle driver, the command would read: set CLASSPATH=%WL_HOME%serverlibojdbc14.jar;%CLASSPATH%
4. Save the file.

Creating a new data source connection

To create a data source connection:

1. Start the WebLogic Server using the start WebLogic.cmd command or from WebLogic Workshop.
2. Launch the WebLogic Server Console by entering http://localhost:7001/console in a Web browser or by selecting Tools—>WebLogic Server—>WebLogic Console…
3. Enter your username and password and click Sign In. The default username and password are both “weblogic.”
4. Click Connection Pools under JDBC in the Service Configurations section.
5. Click on Configure a new JDBC Connection Pool.
6. Select the database type that you are going to connect to from the Database Type drop-down list.
7. Select the database driver you want to use from the Database Driver drop-down list.
8. Click Continue.
9. In the Name field, enter a name for your connection.
10. Enter the remaining connection details and click on continue.
11. Make sure the details displayed on this page are correct and click Test Driver Configuration.

Defining the Connection Pool Configuration

To define the connection pool configuration:

1. On the JDBC Connection, Pools page click the name of your new connection.
2. Click the Connections tab.
3. Enter your configuration details in the fields provided.
4. If you are using a XA driver, click Show in the Advanced Options section.
5. Make sure that the Supports Local Transaction option is selected.
6. Click Apply.

Configuring a New Data Source

To create a new Data Source:

1. On the WebLogic Server Home page, click Data Sources under JDBC in the Service Configurations section.
2. Click Configure a new JDBC Data Source.
3. Enter a name for the new data source in the Name field.
4. In the JNDI Name field, enter the JNDI path to where the data source is bound.
5. If you are using a non-XA driver, check the Emulate Two-Phase Commit for the non-XA Driver checkbox.
6. Click Continue.
7. Select the connection you created in Creating your Data Source Connection from the Pool Name drop-down list.
8. Click Continue.
9. Select the servers and clusters on which you want to deploy this JDBC data source. In most cases, you should deploy the data source to the same servers and clusters as the associated connection pool. The deployment targets of the associated connection pool are selected by default.
10. Click Create.

The new data source is created and you are returned to the JDBC Data Sources page.

5. What is a data model?

A data model is an abstract model which organizes the different elements of data and classifies how they relate to one another as well as how these data models relate to properties of real-world entities. The term data model can refer to two different concepts which in turn are closely related to one another. The term may refer to an abstract formalization of the objects and relationships which can be found in any specific application domain. These may include data like products, customers, and orders of a manufacturing organization. On the other hand, the term Data Model could also refer to a set of concepts that are used to define these formalizations.

These concepts can be in the form of tables, relations, attributes, etc. A data model performs the role of determining the structure of data and is mostly modeled by data specialists. A data model is created mainly based on data, the relationship between data, its semantics, and constraints. According to the American National Standards Institute, there exists mainly three kinds of data models

• Conceptual data model– These data models deal with the semantics of domains which are the scope of the data model.
• Logical data model– These data models describe the semantics as have been represented by any particular technology used for data manipulation.
• Physical data mode– These data models describe the physical means using which the data is stored.

 A database model describes how a database is structured and utilized. There can be various types of these models based on the specifications and utilities of the users. Based on the shape and structure of the data model, some of the different types of data models are:

• Flat model
• Hierarchical model
• Network model
• Relational model
• Object-relational model
• Object-role modeling
• Star schema

Further data models can also be classified with respect to other properties like:

• Entity-relationship model
• Geographic data model
• Generic data model
• Semantic data model

6. How to use data models in Azure Data Service?

In the Azure Analysis services which is a new preview service offered by Microsoft Azure, the user can host semantic data models. This will allow the users in one’s organization to easily connect to the user’s data and use functions like Excel, Power BI, etc. to create reports and analyses. The main application of these data models is in large corporations where the user who has created the model needs to share it with the employees, co-workers, or even clients. The people who are sharing this information may even have a number of queries regarding the reports or even the SQL used.

The Azure Analysis Service allows you to incorporate all of this information into one semantic model which you can then share. The other users can also have their queries addressed via easy features in the model. The data used in such models include relationships between tables, easily understandable labeling for tables and columns, descriptions, calculations as well as row-level security. To create and use such a data model the user will need an Azure Subscription, SQL Server Data Tools, and Power BI Desktop. The steps to create a server in the Azure Analysis Services are:

1. User should go to the online portal of Azure.

2. They will find the option New in the Menu blade.

3.  Expand the Intelligence + Analytics tabs and click on Analysis Services.

4. Under the Analysis Services blade the user will find the create tab where they have to mention:

• Name of Server
• Subscription
• Resource group
• Location
• Pricing Tier

Now to create a Data Model in Analysis Services the user will require Visual Studio and an extension known as SQL Server Data Tools (SSDT).

1. The user has to create a new Analysis Services Tabular Project in SSDT. The type of workplace would be integrated.
2. Then the user has to select the Import from Data Source icon on the toolbar.
3. Data Source Type would be Microsoft SQL Azure.
4. Next, the user has to fill in the connection information used for the sample SQL Azure Database. These will include the Server name, User Name, Password, and Name of the Database.
5. Next select Service Account, followed by Impersonation mode.
6. Then the user can select the tables he wishes to incorporate into the cache and click on finish.
7. The user also has the option to improve the data model after creating it. He can use a variety of features to enhance the data model for example The user can create or edit relationships in the data model. This means he can change the relationships as mentioned by him among tables or diagrams or any details provided by him.
8. The user can also edit the properties of the tables or columns in the data model. He can find the option of updating values in the properties pane.
9. The user also has the option to incorporate further business logic into the model by including newer calculations and measures
10. Once the data model has been completed the user can easily upload it to the Azure Analysis Services Server.

CONCLUSION

With the increasing hunger for data in businesses, Microsoft Azure Data Services lets you handle data efficiently and effectively. It fulfills a wide variety of data-related needs and does so under one platform.