Workloads on Microsoft Fabric

3 min. read

A modern data architecture must be able to support different types of data workloads. Microsoft Fabric empowers users from Ph.D. level data scientists to junior marketers and all business functionaries in between to gain performance-enhancing intelligence and insights.

In our previous blog posts, we’ve discussed what must comprise a modern AI-led data architecture or fabric, how Microsoft Fabric meets the needs of a modern data architecture, and how its OneLake solution allows for data ingestion and non-destructive manipulation and storage.

As business insights and the use of data are multifarious, so must the architecture and reporting platform. It should be simple to use for data management, data analysis, engineering, and creating solutions supported by types of workloads.

Microsoft Fabric contains a comprehensive suite of features to establish and maintain a data architecture of this nature. Embedded into its platform is artificial intelligence and machine learning applied to not only speeding up analysis, but to streamlining how is  categorised, secured, accessed by technical and non-technical personnel, and reporting in simple to understand terms, figures, or visuals.

Data Science and Engineering

The upside to Microsoft Fabric is that data scientists and engineers work inside the same platform as analysts and business users, making it a Lake-centric approach. As such, all users can formulate a problem to work toward a solution through insight. This is achieved though AI-powered data discovery and pre-processing to conduct experiments and modelling, achieved through code generated and interpreted by SynapseML. It parses and analyses data for use elsewhere in the lakehouse and for business intelligence.

Data Analysis – In Real Time

SynapseML unifies several existing machine learning frameworks and new Microsoft algorithms into a single, scalable API to accelerate repetitive tasks. It also uses semantic link – using natural language instead of code to generate desired experiments and models. Semantic link discovers dependencies and links between data in real time to generate models and reports, which can be visualised. Machine learning can also identify data quality issues for users, which means cleaning and avoiding “garbage in” is embedded into the system.

Monitoring, Security, and Compliance

Synapse as a machine learning platform also incorporates multi-layered security to protect the integrity of a business architecture. Through machine learning, it enables thorough data protection, access control, threat protection, authentication, and network security. Data can be configured to be automatically classified by AI, categorised, and properly governed. Workspaces can also be compartmentalised and virtualised to isolate certain user groups and minimise potential risks.

Business Intelligence

The cycle of business intelligence gained from the data lakehouse and data science aspects of Microsoft Fabric’s data architecture all seamlessly integrates into one another, generating insights that are fed back into experiments and models, and visualised again, etc. Using Copilot AI, as we’ve discussed here, all users across all departments can call up data and generate reports using natural language and little, if any formal training in data engineering or science. This allows for faster insights, helping your business rise above the competition.

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