Microsoft Fabric: Revolutionizing Data Analytics in AI Powered-Unified Analytics Platform

Sherlock Holmes creator, Sir Arthur Conan Doyle, warned, “It is a capital mistake to theorize before one has data.” In our information age, data is the new oil – data literally fuels every key business decision. But how do you analyze data accurately? How do you avoid bad-quality data that leads to incorrect business decisions? That’s the pain point that Microsoft’s Azure Ecosystem has solved since its inception. 

With 2.5 quintillion bytes of data being created daily, it has become important to process and clean this data to obtain meaningful insights from it. Data Analytics, a multi-billion-dollar industry now, predicted CAGR of 29.9% from 2022 to 2030. The Artificial Intelligence (AI) and Business Intelligence (BI) wave has swept through the world, driven by massive data collection in the past decade. #ChatGPT, #Bard and other AI models are now intelligent enough to automate tasks that earlier required human intervention.

However, despite this intelligence wave, organizations continue to struggle with data engineering cycles. They are either plagued with delays or miss out on objectives due to the complexity of managing a data pipeline with all its bells and whistles.

This is where Microsoft Fabric is the need of the hour to empower the process of maximizing the value of enterprise data. The presence of a unified Azure ecosystem will not just solve existing problems of scalability and data visibility for Azure users, but also lead to Smaller and Streamlined Data Engineering Cycles that will take care of the compliance and costing issues. In today’s article, we look at what’s changed with Microsoft Fabric and how it benefits companies.

 

Why Microsoft Fabric?

The current approach to data engineering for business intelligence and analytics evolved with inefficiencies. It begins with extracting data from various ERP/CRM/Budgeting/Planning systems and ends with displaying it in analytics tools such as PowerBI. Furthermore, the process often requires multiple data stores to hold the data (Multiple Copies), which adds additional complexity and cost.

Solutions include a Data Lake or a Data Warehouse, or both. However, their usage is often determined by the data’s complexity and the customer’s specific needs. As a result, the current approach to data engineering requires far more time and resources to manage.

 

A typical data pipeline looks like this:

Source -> Data Lake -> Data warehouse -> Transformation -> BI Tool -> Decision Makers

Here is an example of the flow with some of the popular industry tools:

Source (Oracle/Dynamics/SAP)-> AWS Data Lake -> Redshift -> Tableau Prep -> Tableau -> Consumers

 

As shown above, the workflow extracts data from a source database, loads it into a Data Lake, processes and transforms it using a Data Warehouse, prepares the data using Transformation tools, and visualizes it using BI Tools like PowerBI.   Building and maintaining a pipeline that spans multiple technologies and platforms can be complex and time-consuming.

 

Challenges Faced by Companies Today

1. Complexity: Building and maintaining a pipeline that spans multiple technologies and platforms can be complex and require specialized knowledge and expertise. This can result in increased development and maintenance costs.
2. Latency: Moving data between different systems and platforms can introduce latency into the pipeline. This can impact the timeliness of the insights generated by the pipeline.
3. Security: Transferring data between different systems and platforms can also introduce security risks if not done properly. It is important to ensure that all data is encrypted during transit and at rest, and that access controls, keys and credentials are in place to prevent unauthorized access.
4. Cost: Depending on the volume of data being processed and the specific technologies used, the cost of building and operating a pipeline like this can be significant.
5. Compatibility: Ensuring data is properly formatted and compatible across different systems and platforms can be challenging. Investing additional resources into data transformation and normalization may be necessary to ensure that data can be properly processed and analyzed.
6. Database-specific limitations: SAP or Oracle or SQL may have limitations on the amount of data that can be extracted or may have proprietary data structures that may require additional development effort to extract and transform the data.
7. Tool-specific limitations: The specific tools being used for transformation, such as Redshift, Snowflake, may have limitations on the types of data sources they can connect to or the complexity of transformations they can perform.

 

A lot of moving parts means something somewhere is constantly breaking down, leading to a higher probability of errors and misplaced data.  Microsoft Fabric addresses all these complex data platform issues and empowers analytics platforms with artificial intelligence while solving them.

 

Microsoft Fabric: Faster, Smarter, Unified, AI-Powered and Most Efficient Data Management Platform

After the adoption of #openAI, Microsoft now delivers a unified and comprehensive AI powered, unified data analytics solution in the form of Microsoft Fabric to help organizations of all sizes streamline their data management and analysis processes. Its advanced, AI powered end-to-end analytics solution helps businesses make better decisions with their data.

Microsoft Fabric creates data visibility for end users using BI Tools (Power BI) at every stage of data engineering. Data storage has been innovated to work seamlessly across all technologies. This means you can see the data from a Data Lake or Warehouse on the BI tool without loading the data into the tool (NO replication? Really). 

 

Here is a quick rundown on how Kanerika helps you maximize the value of your data using Microsoft’s Microsoft Fabric: 

1. AI Powered Microsoft Fabric is a Unified solution covering all data pipeline stages, from data ingestion and storage to processing, transformation, security and analysis. 
2. Microsoft Fabric is designed to be highly scalable, allowing organizations to process and analyze large volumes of data quickly and efficiently without data movement. This can help organizations keep up with growing data volumes and provide faster insights to support decision-making.
3. Unify your data estate – It helps organizations reduce costs by consolidating multiple tools and technologies into a single unified solution using an open and lake centric hub that helps data engineers connect, curate and personalized views for every data consumer in your company.
4. Empowering your business – Help businesses innovate, and make faster decisions with real time data access within Microsoft 365 apps like teams, excel or Power apps within the Microsoft Fabric interface.
5. Microsoft Fabric follows the leading industry storage format, delta Parquet. This allows for seamless cross-platform interchange of data and allows collaboration between different tools such as your Data Lake, SQL Engine, or even your Notebook. This is possible as all data are saved in the same format in the tools. Furthermore, users can choose their processing technology based on the following factors:
   1. Expected Data volume 
   2. Quality of in-house expertise 
   3. Expected Final Outcome 
6. All popular technologies such as Data Pipeline, Data flow gen2, SQL, Kusto, and Notebook are available to users within the same ecosystem to create a unified and cohesive data engineering experience.
7. Security & Governance – With unified analytics solutions, customers get control of how their data needs to be governed. Microsoft Fabric allows connecting people and data using an open and scalable solution that gives data stewards additional control with built-in security, governance, and compliance. 

 

Microsoft Fabric empowers data engineers to analyze data in Power BI at every stage of the data lifecycle, beginning from the raw data in Data Lakes to the processed data after Data Transformation.

This new unified ecosystem ensures complete data visibility to QA and Business teams from data inceptions stage itself and helps create a better collaborative environment that thrives in using similar data formats and tools.

 

Don’t Miss Out: Join the Fabric Preview and Get a Head Start with Kanerika

Kanerika is a niche consulting company to maximize the value of your data. As a preview user of Microsoft Fabric, you can explore all of the features and benefits of Microsoft Fabric’s comprehensive data pipeline solution with Kanerika. This will give you a head start on understanding the capabilities of Microsoft Fabric and give you an edge over your competition through the use of the latest data technologies.

Sign up now to gradually transition your data processes before the public release of Microsoft Fabric and ensures you experience the Azure ecosystem more succinctly upon its release date.

Don’t miss out on this exclusive opportunity to preview Microsoft Fabric – Contact us today to revolutionize your data processing capabilities, get an edge over your competition and signup for Microsoft Fabric experience with Kanerika.