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Infrastructure as a service (IaaS) is the most flexible category of cloud services, as it provides you the maximum amount of control for your cloud resources. In an IaaS model, the cloud provider is responsible for maintaining the hardware, network connectivity (to the internet), and physical security. You’re responsible for everything else: operating system installation, configuration, and maintenance; network configuration; database and storage configuration; and so on. With IaaS, you’re essentially renting the hardware in a cloud datacenter, but what you do with that hardware is up to you.

Shared responsibility model

The shared responsibility model applies to all the cloud service types. IaaS places the largest share of responsibility with you. The cloud provider is responsible for maintaining the physical infrastructure and its access to the internet. You’re responsible for installation and configuration, patching and updates, and security.

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PostgreSQL uses memory that can be classified as:

• Local memory – allocated to each process
• Shared memory – used by all processes

Local memory

Each process in PostgreSQL requires memory for query processing. The following server parameters allow you to define memory usage:

work_mem defines memory required for sorting tuples for ORDER BY and DISTINCT operations. This parameter determines the amount of memory available for internal sort operations and hash tables. If you have large amount of available memory and your workload has queries with complex sorting, increasing this parameter value could improve performance by allowing larger scans in-memory before spilling to disk.

However, one complex query could have many sort and hash operations running concurrently. Each operation uses as much memory as this value allows before it starts writing to disk based temporary files. On a busy system, therefore, total memory usage is many times that of an individual work_mem parameter.

To tune this value, use Total RAM * 0.25 / max_connections as initial value.

maintenance_work_mem is memory required by vacuum and reindex. This parameter determines the amount of memory available for internal sort operations and hash tables. The default value is 64 KB but increasing this value improves performance for vacuuming.

autovacuum_work_mem sets the maximum memory to be used by each autovacuum process.

temp_buffers defines memory for storing temporary tables. This parameter sets the maximum number of temporary buffers used by each database session.

effective_cache_size defines the amount of available memory for disk caching by the operating system and within the database. The PostgreSQL query planner determines whether this memory is fixed in RAM. Index scans are most likely to be used against higher values; otherwise, sequential scans are used if the value is low.

Set effective_cache_size to 50% of the machine’s total RAM.

Shared memory

Shared memory is allocated at startup. Shared memory is used for:

shared_buffers defines the shared memory buffers used by the server. PostgreSQL loads pages of tables and indexes from persistent storage to a shared buffer pool, and then works on them in memory. This shared buffer pool is the major component of the shared memory used by the server. The default value is 128 MB (depending on the compute tier). If you decide to allocate more memory, you need to restart the server.

wal_buffers defines the number of disk page buffers in shared memory for write ahead logging (WAL) before writing it to persistent storage.

In summary, the important server parameters relating to memory that you could want tune are:

• shared_buffers
• work_mem
• effective_cache_size

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PostgreSQL is a client/server relational database management system (RDMS). PostgreSQL also supports a wide range of extensions, such as the Citus extension in the Azure Cosmos DB for PostgreSQL option. After an extension is loaded into the database, it functions like any built-in feature.

PostgreSQL also has its own query language called pgsql. In addition, PostgreSQL supports procedural languages such as Ruby on Rails.

Client/server architecture

PostgreSQL is based on a client/server architecture. The server stores, manages, and returns data to client programs. Client programs request data using pgSQL, or one of the procedural languages that PostgreSQL supports, such as PL/pgSQL.

A PostgreSQL session consists of three parts:

• The postmaster
• The client application
• The server

Postmaster

The Postmaster is the supervisory daemon process that manages a PostgreSQL server. The postmaster daemon manages communication among the various server processes including initializing the server, shutting down the server, handling connection requests, and performing other background processes. In Azure Database for PostgreSQL, you don’t have access to the file system, or to the Postmaster process.

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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.

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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).

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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.

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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.

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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.

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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.

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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).

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