Category: Uncategorized

Azure Database for PostgreSQL is a platform as a service (PaaS) database available in several versions, and different server configurations. Each server option offers a choice of versions for PostgreSQL community edition.

Azure Database for PostgreSQL is available as the following deployment option:

• Flexible server – with the option to install PostgreSQL version 11, 12, 13, 14, 15 or 16.

Azure Database for PostgreSQL flexible servers can be started and stopped, so that you aren’t charged for time you aren’t using the server.

Azure Database for PostgreSQL flexible server is a fully managed database service in Azure, with a friendly user interface to configure a wide range of parameters and offers several PostgreSQL versions, including the most recent. There’s no access to the underlying operating system or file system.

Flexible server has a fully qualified domain name (FQDN) which should be used when connecting.

Flexible server provides fine grained control and flexibility with database configuration settings. For new implementations, flexible server provides the best value including the ability to stop/start the server, and a burstable compute tier that is optimized for workloads that don’t require continuous high capacity. Flexible server is also designed for zone resilient high availability, with data files residing on Azure storage and maintained in three locally redundant locations. If the service fails, a new Linux VM (virtual machine) is provisioned, and the data files are mapped to the new VM. The PostgreSQL database engine is then brought online with the new VM.

Flexible server also offers: more predictable performance, more control in configuring the server, a custom maintenance window, cost optimization controls, and a simplified developer experience.

supply chain

Microsoft offers previews of Azure features for evaluation purposes. With Azure Preview Features, you can try out prerelease features and services before they’re rolled out to everyone.

Some common types of previews that you might see include:

• New storage types
• New Azure services, such as Machine Learning enhancements
• New or enhanced integration with other platforms
• New APIs for services

Azure feature previews are available under certain terms and conditions that are specific to each particular Azure preview. Some previews aren’t covered by customer support.

Once a feature has been evaluated and tested successfully, it might be released to customers as part of Azure’s default product set. This release is referred to as General Availability (GA).

staff augmentation service

Let’s look at how to create and modify dashboards in the Azure portal. In this unit, you learn about creating and using dashboards. In the next unit, you’ll try out the things you learned.

What is a dashboard?

A dashboard is a customizable collection of UI tiles displayed in the Azure portal. You can add, remove, and position tiles to create the exact view you want, then save that view as a dashboard. You can configure multiple dashboards, and you can switch among them as needed. You can even share your dashboards with other team members.

Dashboards give you flexibility in what information to display. For example, you can create dashboards for specific roles within the organization, then use role-based access control (RBAC) to control who can access each dashboard.

Dashboards are stored as JavaScript Object Notation (JSON) files. This format means you can download a dashboard and edit the file directly, then upload it again to Azure or share it with other users. Azure stores dashboards within resource groups, just like any other resource that you can manage within the portal.

software development

With an Azure account, you can sign into the Azure portal. The portal is a web-based administration site that lets you interact with Azure services and the resources you’ve created. Almost everything you do with Azure can be done through this web interface.

Azure portal layout

When you first sign in to the Azure portal, the default page is Home.

You can change your default view to the customizable Dashboard in the portal’s settings. You’ll learn more about dashboards later in this module.

Portal menu

In the portal’s left-hand sidebar is the portal menu, which lists different Azure services and resource types.

Azure has many more services than what’s shown in this menu. The services listed here are the ones in your Favorites.

You can customize your Favorites with the specific resource types that you tend to create or administer most often.

Copilot in Azure

Near the top of the screen, you see the Copilot button.

If your organization has made Microsoft Copilot in Azure (preview) available, you can select this button to ask questions, quickly navigate to services and resources, or get help with tasks. For example, Copilot in Azure can help you design, operate, optimize, and troubleshoot your Azure apps and infrastructure. Copilot for Azure can also help you gain new insights about which Azure services can help with your objectives.

Azure portal global controls

The Azure portal displays several icons in the status bar at the top-right side of the screen. These icons are global controls that are always available, no matter what service you’re working with. Each icon provides quick access to useful functionality, such as configuring settings or viewing notifications about portal activity.

If you’re viewing the Azure portal on a screen with reduced horizontal space, you might need to expand an ellipsis (…) menu to view the global controls.

Let’s walk through each of these controls to understand them better.

school management

You can configure and manage Azure using a broad range of tools and platforms. There are tools available for the command line, language-specific Software Development Kits (SDKs), developer tools, tools for migration, and many others.

Tools that are commonly used for day-to-day management and interaction include:

• Azure portal for interacting with Azure via a Graphical User Interface (GUI)
• Azure PowerShell and Azure Command-Line Interface (CLI) for command-line and automation-based interactions with Azure
• Azure Cloud Shell for a web-based command-line interface
• Azure mobile app for monitoring and managing your resources from your mobile device

There are also Azure SDKs for a range of languages and frameworks and REST APIs to help manage and control Azure resources programmatically.

When you’re starting out with Azure, you’ll probably use the Azure portal most often.

quote

Analytical data processing typically uses read-only (or read-mostly) systems that store vast volumes of historical data or business metrics. Analytics can be based on a snapshot of the data at a given point in time, or a series of snapshots.

The specific details for an analytical processing system can vary between solutions, but a common architecture for enterprise-scale analytics looks like this:

1. Operational data is extracted, transformed, and loaded (ETL) into a data lake for analysis.
2. Data is loaded into a schema of tables – typically in a Spark-based data lakehouse with tabular abstractions over files in the data lake, or a data warehouse with a fully relational SQL engine.
3. Data in the data warehouse may be aggregated and loaded into an online analytical processing (OLAP) model, or cube. Aggregated numeric values (measures) from fact tables are calculated for intersections of dimensions from dimension tables. For example, sales revenue might be totaled by date, customer, and product.
4. The data in the data lake, data warehouse, and analytical model can be queried to produce reports, visualizations, and dashboards.

Data lakes are common in large-scale data analytical processing scenarios, where a large volume of file-based data must be collected and analyzed.

Data warehouses are an established way to store data in a relational schema that is optimized for read operations – primarily queries to support reporting and data visualization. Data Lakehouses are a more recent innovation that combine the flexible and scalable storage of a data lake with the relational querying semantics of a data warehouse. The table schema may require some denormalization of data in an OLTP data source (introducing some duplication to make queries perform faster).

An OLAP model is an aggregated type of data storage that is optimized for analytical workloads. Data aggregations are across dimensions at different levels, enabling you to drill up/down to view aggregations at multiple hierarchical levels; for example to find total sales by region, by city, or for an individual address. Because OLAP data is pre-aggregated, queries to return the summaries it contains can be run quickly.

Different types of user might perform data analytical work at different stages of the overall architecture. For example:

• Data scientists might work directly with data files in a data lake to explore and model data.
• Data Analysts might query tables directly in the data warehouse to produce complex reports and visualizations.
• Business users might consume pre-aggregated data in an analytical model in the form of reports or dashboards.

mobile app development

A transactional data processing system is what most people consider the primary function of business computing. A transactional system records transactions that encapsulate specific events that the organization wants to track. A transaction could be financial, such as the movement of money between accounts in a banking system, or it might be part of a retail system, tracking payments for goods and services from customers. Think of a transaction as a small, discrete, unit of work.

Transactional systems are often high-volume, sometimes handling many millions of transactions in a single day. The data being processed has to be accessible very quickly. The work performed by transactional systems is often referred to as Online Transactional Processing (OLTP).

managed it services

A database is used to define a central system in which data can be stored and queried. In a simplistic sense, the file system on which files are stored is a kind of database; but when we use the term in a professional data context, we usually mean a dedicated system for managing data records rather than files.

Relational databases

Relational databases are commonly used to store and query structured data. The data is stored in tables that represent entities, such as customers, products, or sales orders. Each instance of an entity is assigned a primary key that uniquely identifies it; and these keys are used to reference the entity instance in other tables. For example, a customer’s primary key can be referenced in a sales order record to indicate which customer placed the order. This use of keys to reference data entities enables a relational database to be normalized; which in part means the elimination of duplicate data values so that, for example, the details of an individual customer are stored only once; not for each sales order the customer places. The tables are managed and queried using Structured Query Language (SQL), which is based on an ANSI standard, so it’s similar across multiple database systems.

loyalty

The ability to store data in files is a core element of any computing system. Files can be stored in local file systems on the hard disk of your personal computer, and on removable media such as USB drives; but in most organizations, important data files are stored centrally in some kind of shared file storage system. Increasingly, that central storage location is hosted in the cloud, enabling cost-effective, secure, and reliable storage for large volumes of data.

The specific file format used to store data depends on a number of factors, including:

• The type of data being stored (structured, semi-structured, or unstructured).
• The applications and services that will need to read, write, and process the data.
• The need for the data files to be readable by humans, or optimized for efficient storage and processing.

it support

Data is a collection of facts such as numbers, descriptions, and observations used to record information. Data structures in which this data is organized often represent entities that are important to an organization (such as customers, products, sales orders, and so on). Each entity typically has one or more attributes, or characteristics (for example, a customer might have a name, an address, a phone number, and so on).

You can classify data as structured, semi-structured, or unstructured.

Structured data

Structured data is data that adheres to a fixed schema, so all of the data has the same fields or properties. Most commonly, the schema for structured data entities is tabular – in other words, the data is represented in one or more tables that consist of rows to represent each instance of a data entity, and columns to represent attributes of the entity. For example, the following image shows tabular data representations for Customer and Product entities.

it consulting