Working with sensitive data or inside a highly regulated environment requires secure and secure cloud infrastructure for data processing. The cloud might seem to be an open environment on the web and lift security concerns. While you start your journey with Azure and don’t have enough experience with the resource configuration it is straightforward to make design and implementation mistakes that may impact the safety and adaptability of your recent data platform. On this post, I’ll describe an important facets of designing a cloud adaptation framework for a knowledge platform in Azure.
An Azure landing zone is the muse for deploying resources in the general public cloud. It comprises essential elements for a sturdy platform. These elements include networking, identity and access management, security, governance, and compliance. By implementing a landing zone, organizations can streamline the configuration technique of their infrastructure, ensuring the utilization of best practices and guidelines.
An Azure landing zone is an environment that follows key design principles to enable application migration, modernization, and development. In Azure, subscriptions are used to isolate and develop application and platform resources. These are categorized as follows:
- Application landing zones: Subscriptions dedicated to hosting application-specific resources.
- Platform landing zone: Subscriptions that contain shared services, akin to identity, connectivity, and management resources provided for application landing zones.
These design principles help organizations operate successfully in a cloud environment and scale out a platform.
A knowledge platform implementation in Azure involves a high-level architecture design where resources are chosen for data ingestion, transformation, serving, and exploration. Step one may require a landing zone design. When you need a secure platform that follows best practices, starting with a landing zone is crucial. It should enable you to organize the resources inside subscriptions and resource groups, define the network topology, and ensure connectivity with on-premises environments via VPN, while also adhering to naming conventions and standards.
Architecture Design
Tailoring an architecture for a knowledge platform requires a careful number of resources. Azure provides native resources for data platforms akin to Azure Synapse Analytics, Azure Databricks, Azure Data Factory, and Microsoft Fabric. The available services offer diverse ways of achieving similar objectives, allowing flexibility in your architecture selection.
For example:
- Data Ingestion: Azure Data Factory or Synapse Pipelines.
- Data Processing: Azure Databricks or Apache Spark in Synapse.
- Data Evaluation: Power BI or Databricks Dashboards.
We may use Apache Spark and Python or low-code drag-and-drop tools. Various mixtures of those tools can assist us create probably the most suitable architecture depending on our skills, use cases, and capabilities.
Azure also permits you to use other components akin to Snowflake or create your composition using open-source software, Virtual Machines(VM), or Kubernetes Service(AKS). We will leverage VMs or AKS to configure services for data processing, exploration, orchestration, AI, or ML.
Typical Data Platform Structure
A typical Data Platform in Azure should comprise several key components:
1. Tools for data ingestion from sources into an Azure Storage Account. Azure offers services like Azure Data Factory, Azure Synapse Pipelines, or Microsoft Fabric. We will use these tools to gather data from sources.
2. Data Warehouse, Data Lake, or Data Lakehouse: Depending in your architecture preferences, we are able to select different services to store data and a business model.
- For Data Lake or Data Lakehouse, we are able to use Databricks or Fabric.
- For Data Warehouse we are able to select Azure Synapse, Snowflake, or MS Fabric Warehouse.
3. To orchestrate data processing in Azure we now have Azure Data Factory, Azure Synapse Pipelines, Airflow, or Databricks Workflows.
4. Data transformation in Azure might be handled by various services.
- For Apache Spark: Databricks, Azure Synapse Spark Pool, and MS Fabric Notebooks,
- For SQL-based transformation we are able to use Spark SQL in Databricks, Azure Synapse, or MS Fabric, T-SQL in SQL Server, MS Fabric, or Synapse Dedicated Pool. Alternatively, Snowflake offers all SQL capabilities.
Subscriptions
A crucial aspect of platform design is planning the segmentation of subscriptions and resource groups based on business units and the software development lifecycle. It’s possible to make use of separate subscriptions for production and non-production environments. With this distinction, we are able to achieve a more flexible security model, separate policies for production and test environments, and avoid quota limitations.
Networking
A virtual network is analogous to a standard network that operates in your data center. Azure Virtual Networks(VNet) provides a foundational layer of security to your platform, disabling public endpoints for resources will significantly reduce the danger of information leaks within the event of lost keys or passwords. Without public endpoints, data stored in Azure Storage Accounts is simply accessible when connected to your VNet.
The connectivity with an on-premises network supports a direct connection between Azure resources and on-premises data sources. Depending on the form of connection, the communication traffic may undergo an encrypted tunnel over the web or a non-public connection.
To enhance security inside a Virtual Network, you should utilize Network Security Groups(NSGs) and Firewalls to administer inbound and outbound traffic rules. These rules let you filter traffic based on IP addresses, ports, and protocols. Furthermore, Azure enables routing traffic between subnets, virtual and on-premise networks, and the Web. Using custom Route Tables makes it possible to regulate where traffic is routed.
Naming Convention
A naming convention establishes a standardization for the names of platform resources, making them more self-descriptive and easier to administer. This standardization helps in navigating through different resources and filtering them in Azure Portal. A well-defined naming convention permits you to quickly discover a resource’s type, purpose, environment, and Azure region. This consistency might be helpful in your CI/CD processes, as predictable names are easier to parametrize.
Considering the naming convention, you must account for the data you wish to capture. The usual ought to be easy to follow, consistent, and practical. It’s price including elements just like the organization, business unit or project, resource type, environment, region, and instance number. It is best to also consider the scope of resources to make sure names are unique inside their context. For certain resources, like storage accounts, names should be unique globally.
For instance, a Databricks Workspace is perhaps named using the next format:
Example Abbreviations:
A comprehensive naming convention typically includes the next format:
- Resource Type: An abbreviation representing the form of resource.
- Project Name: A novel identifier to your project.
- Environment: The environment the resource supports (e.g., Development, QA, Production).
- Region: The geographic region or cloud provider where the resource is deployed.
- Instance: A number to distinguish between multiple instances of the identical resource.
Implementing infrastructure through the Azure Portal may appear straightforward, but it surely often involves quite a few detailed steps for every resource. The highly secured infrastructure would require resource configuration, networking, private endpoints, DNS zones, etc. Resources like Azure Synapse or Databricks require additional internal configuration, akin to organising Unity Catalog, managing secret scopes, and configuring security settings (users, groups, etc.).
When you finish with the test environment, you‘ll need to copy the identical configuration across QA, and production environments. That is where it’s easy to make mistakes. To attenuate potential errors that might impact development quality, it‘s beneficial to make use of an Infrastructure as a Code (IasC) approach for infrastructure development. IasC permits you to create cloud infrastructure as code in Terraform or Biceps, enabling you to deploy multiple environments with consistent configurations.
In my cloud projects, I take advantage of accelerators to quickly initiate recent infrastructure setups. Microsoft also provides accelerators that might be used. Storing an infrastructure as a code in a repository offers additional advantages, akin to version control, tracking changes, conducting code reviews, and integrating with DevOps pipelines to administer and promote changes across environments.
In case your data platform doesn’t handle sensitive information and also you don’t need a highly secured data platform, you may create an easier setup with public web access without Virtual Networks(VNet), VPNs, etc. Nonetheless, in a highly regulated area, a very different implementation plan is required. This plan will involve collaboration with various teams inside your organization — akin to DevOps, Platform, and Networking teams — and even external resources.
You’ll need to ascertain a secure network infrastructure, resources, and security. Only when the infrastructure is prepared you may start activities tied to data processing development.
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