Recently Microsoft has introduced its new DevOps product called Azure Synapse Analytics, which brings a new security control layer over the database services, including Azure Data lakes, Data Factory Pipelines, and SQL warehouses. It also adds new components to the previous Azure Product list, such as Spark Pools and Serverless SQL. Once you integrate into the core system, it helps organisations establish a security layer for the data stored in data lakes, analytics, and warehouses. However, it also demands new methods and concepts to handle those data securely.
Azure Synapse Analytics provides information analysis services in bulk, which large enterprises primarily utilise. These companies use this solution to bring an evolution of Azure SQL Data Warehouse and business data storage systems altogether with Big Data analysis.
It provides a single service set for each job while processing, handling, and utilising data to achieve real-time BI and Predictive Analysis. In future, it is possible to integrate Power BI and Azure ML tools with Synapse. As it supports mathematical processing models with the help of the ONNX format. It enables IT engineers to perform query and management tasks on a data at scale. It uses serverless demand processing and ad hoc analysis to achieve data insights.
The New Workspace Portal:
Comparing Synapse to other services proves that it has a separate workspace. It offers code access, SQL, process pipelines, record books, visibility, and various management panels. You can access the portal using Azure AD access controls on the public internet. This grants access to any of Synapse’s instances available in any cluster that the developer needs to access. All thanks to Synapse, today, it is possible to access the workspace portal from any separate or personal internet connection rather than establishing a connection using PLH (Private Link Hubs).
Unlike PLH, which protects service and database authorisation, this new solution helps network engineers divert traffic to a web portal. After completing integration with Azure AD Conditional Access Policy, this new workspace will get protection from network and authorisation measures.
Speaking of authorisation, Synapse Analytics includes a new concept called “Synapse Roles,”. Moreover it plays an essential role in assigning analytics teams to an internal workspace.
- Azure Roles: It provides control access for the orchestration of the Azure Synapse Analytics resources inside its workspace portal. Plus, there are no such built-in platform-specific roles.
- Synapse Roles: These roles resided in the synapse portal and code, such as Spark Admin, Artifact publisher & user, compute operator etc. These are either assigned to you at the workspace level or individuals. Including spark pool, integrated runtime services or login credentials.
The precisions of role assignments enable detailed accessibility for each admin, support team, developer, and data scientist. In the latest version of Synapse roles, customisation is not supported. It can be previewed. Therefore the users have to be ready to accept changes. Furthermore, these roles aren’t efficient in giving access protection to data sets that reside in ADLs.
Data Access Control:
Dataset access management in heterogeneous environments is crucial for security policy. Also, it requires various tools to identify business data centres, logging solutions, and big data analytics environments. Synapse workspace defines the process with the same controls often used in the Azure Active Directory. It provides centralised access control across the Synapse infrastructure.
- In the case of SQL access, Azure AD groups are integrated with AAD authentications to get authorisation access. Plus, this type of database object supports Azure SQL models.
- In the case of Azure Data Lake, Azure roles and Hierarchical namespace features. You can use it for accessing authorisation to set up read and update controls in AAD groups as per the directory hierarchy. This type of access is also known as a unique feature of ADLS Gen2. Plus, this authentication uses the pass-through operation, which means the user’s identity and authority level are necessary to access data sets.
Apart from that, Azure synapse workspaces contain their own system-distributed identity to gain password-less access to external resources, significant data pipelines, and external data lakes. You should integrate managed identity with the synapse’s roles in the initial stage to gain access to confidential and external resources.
Data Encryption:
Synapse also supports a built-in encoding process with customer-managed secret keys like other Azure data solutions. Synapse provides customer-managed encryption keys to users that utilise the Azure data lake and other Azure storage systems. In the case of Azure Synapse Workspaces, it provides encryption keys with a vault that supports SQL & Spark pools and runtime data factories. You can enable or disable Transparent data encryption. Synapse uses 2048 byte and 3072-byte RSA keys.
As it uses customer-managed keys, it has to provide company policies for Key alterations and encryption-data backups. These policies are the same as the Azure Synapse Analytics, implemented to provide security across organisation infrastructure.
Wrapping Up:
Azure Synapse Analytical solution provides a common platform for various data storage systems, analytical tools, and security measures. It makes it easier to control security measures by providing a centralised management platform. Achieving better security demands logical, precise, and strategic decisions, including enabling managed networks, data transaction protection, data security for specific Azure SQL Pools, and customer-managed encryption keys utilisation.
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