06 Dec

|Microsoft, Technology

Unifying Microsoft Fabric and Azure Databricks – A Powerful Combination.

Nowadays, managing excessive amounts of data is a tricky problem for organisations of every sector. Not being able to manage your data properly limits the value you can get from it and that could hurt your competitive advantage. That’s where Microsoft Fabric and Azure Databricks come in. In this blog we discuss using the power of combining Microsoft Fabric and Databricks together, and why it can enhance your data analytics and deliver actionable insights.

Challenges in unlocking the full potential of your data

While Microsoft Fabric and Azure Databricks can help you unlock the full potential of your data, it is important to understand what the common problems are that you need to overcome?

Here are some of the roadblocks that maybe limiting the efficiency of your data:

Siloed Data

Siloed Data basically means isolated data that is controlled by only one department. Data silos prevent data sharing and can cause problems in analysing the data or when collaborating within your organisation.

Duplicate Data

A piece of data is called duplicate when identical pieces of information exist in the same dataset. Duplicate data can result in a waste of resources for your organisation by skewing results and complicating decision-making.

Data Movement

The ability of data to be moved around from one place to another is called Data Movement. While it allows you to have the right data in the right place it also increases the overall amount of data and delays time for insights and analysis.

Unstructured Data

Like the name suggests, unstructured data means data without a structure. Unstructured data increases the complexity of the data which makes extracting information from data a tough job.

Microsoft Fabric

Microsoft Fabric is a unified data analytics platform developed for organisations looking for a complete package. It has a wide array of services including Data Engineering, Data Warehouses, built-in databases, and real-time analytics. The key thing about Fabric is that it simplifies your data analytics requirements by integrating different services into one user-friendly platform making things easier for you.

Integrated AI capabilities in Microsoft Fabric eliminates the need for manual integration and turns raw data into actionable insight. Users can access advanced AI and Machine Learning (ML) capabilities to deploy efficient AI models through Azure AI foundry. Other capabilities of Fabric include OneLake, integration with Microsoft 365 and unified data management.

Components included in Fabric:

  • Power BI
  • Databases
  • Data Factory
  • Industry Solutions
  • Real-Time Intelligence
  • Data Engineering

Azure Databricks

Azure Databricks is a cloud-based platform for data science and Machine Learning tasks. It allows users to manage and analyse datasets using the Apache Spark processing engine. Databricks is a workspace that enables data scientists to collaborate and develop data-driven applications. The purpose behind Databricks is to provide tools to help make data preparation, real-time analysis, and Machine Learning easier.

Like Microsoft Fabric, Azure Databricks improves how we work with larger and varied datasets. Some of these features include:

  • Unified Workspaces: Databricks allows easier communication and collaboration through unified workspaces.
  • Flexibility: You can handle large amounts of data and configure them to be used across different workloads.
  • Security: Through features like single sign-on, encryption and protection against data exfiltration, Databricks offers secure access to your data.
  • Data Sources: Databricks connects with multiple data sources like SQL servers, cloud storage services and files like MongoDB and Avro.

Unifying Microsoft Fabric and Azure Databricks

Azure Databricks and Microsoft Fabric are built on the same architecture and seem to have similar capabilities on the surface. But, they can be combined and integrated to get a deeper understanding of your data. Both Databricks and Fabric are built on the Lakehouse architecture. Fabric has made creating a Lakehouse very simple and Databricks is built around the fact that Lakehouse is the best way to build a data platform for the future. This will allow you to be care-free about having excellent integration in future updates.

Databricks and Fabric having similar architecture allows you to leverage hybrid solutions that utilise optimal compute times for the specific workloads. All of this maintains the lakehouse architecture which ensures a unified and governed data layer across the organisation.

Unifying Microsoft Fabric and Azure Databricks can help you utilise the Machine Learning and Artificial Intelligence capabilities of Databricks in Fabric. This is a powerful combination that will make it easier for you to deploy and build applications efficiently.

Your organisation can unlock the full potential of AI and advanced analytics by utilising Databricks high-performance platform for data-processing and AI-driven analytics with Fabrics data management tools. This provides seamless data access by supporting direct queries from Power BI.

Improved data governance and security is another benefit of integrating Microsoft Fabric and Azure Databricks. The Unity Catalog in Databricks is a unified governance solution to manage data and AI assets at scale. This compliments Fabrics use of Purview to solidify governance across the organisation.

In this age, where achieving your goals is based on your operational efficiency, combining Fabric and Databricks can be a game changer for your organisation.

Key Takeaways – What this means for your organisation

  • Unifying Microsoft Fabric and Azure Databricks is an excellent way of increasing productivity and collaboration across all your data processes.
  • It offers improved security and data governance by combining Databricks Unity Catalog and Microsoft Purview.
  • Both are built on the lakehouse architecture, which allows you to implement hybrid solutions.
  • Having access to AI and ML capabilities makes deployment and analysis efficient.
  • Unlocks the complete potential of the lakehouse architecture to address complex challenges.

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