Microsoft Fabric Updates Blog

Announcing General Availability: Explore the Capabilities of Real-Time Analytics in Microsoft Fabric

In the fast-paced world of data analytics, real-time insights are the driving force behind informed decisions and competitive advantage. The long-awaited moment is here: Real-Time Analytics in Microsoft Fabric has reached general availability (GA), unveiling a wide range of transformative features and capabilities to empower data-driven professionals across diverse domains. Whether you’re an experienced business analyst, a curious citizen data scientist, or a passionate data engineer, Real-Time Analytics is your gateway to endless possibilities.

Key Features of Real-Time Analytics

Real-Time Analytics offers countless features all aimed at making your data analysis more efficient and effective.

  1. Rapid Deployment: Create a database, ingest data, run queries, and generate a Power BI report all in less than 5 minutes. Real-Time Analytics prioritizes efficiency and speed, enabling you to get to the heart of data analysis without delay.
  2. Low latency data streaming and query: By-default streaming provides high performance, low latency, high freshness data analysis. Go from data to complex business insights in mere seconds.
  3. User-friendly data exploration:  KQL querysets are an easy way to run queries, view, and customize query results on data from a KQL database. Save your queries for future use or collaboration with others on your data exploration.
  4. Robust Visibility: Gain profound insights into database and table metadata, such as original ingested data size, compressed size, column data types, query URIs, recommendations, and data streams. See useful statistics presented in a live dashboard and enhance your ability to manage and organizeyour data effectively.
  5. Query Versatility: Whether you’re a fan of Kusto Query Language (KQL) or prefer traditional SQL, Real-Time Analytics has got you covered. The service allows you to generate quick KQL or SQL queries, ensuring you can work in your preferred language and get results within seconds.
  6. One Lake Integration: Your data doesn’t live in isolation. Real-Time Analytics seamlessly integrates with OneLake and Azure storage, making it easier to access and analyze your data from multiple sources.

7. Effortless Data Ingestion and Querying: Real-Time Analytics simplifies the creation of databases, data ingestion, and query execution. Whether you’re a seasoned developer or just starting your coding journey, you can begin your real-time analytics adventure within seconds and with a low-code experience visual experience.

8. Autoscaling for Peak Efficiency: Say goodbye to the hassle of infrastructure management. Real-Time Analytics offers built-in autoscalingbased on workload factors like hot cache, memory, CPU usage, and ingestion. This feature ensures the seamless operation of your analytics solution with minimal cost, allowing you to concentrate on your data analysis.

9. Sample Data Gallery: Kickstart your data analytics journey with a rich selection of sample data from diverse sources and use cases. Experiment and learn how to runsample queries without the need to create your own dataset.

10. Graph Query Semantics: Visualize a scenario where complex data relationships become clear as day. Real-Time Analytics introduces Graph Query Semantics, enabling users to perform graph analytics with a Cypher-like query language within Kusto Query Language (KQL). Dive deep into interconnected data structures and extract invaluable insights, transforming the way you interpret complex datasets.

11. In-Place Data Sharing: The silos that once hindered collaboration are now a thing of the past. Real-Time Analytics supports in-place, real-time data sharing acrosstenants, fostering seamless teamwork and knowledge exchange. Plus, the platform enables the creation of database shortcuts, further simplifying the process of sharing insights and findings across the organization.

12. Inline Python in KQL Database: Data analysis reaches new heights of convenience. Users can now enable the Python plugin within their KQL database to query every delta table in One Lake or Azure Storage, making data analysis more versatile and accessible for professionals of varying skill levels.

13. Vector Search for AI Embeddings: Harness the distributed capabilities of Kusto to optimize high-performance embedding selection. Real-Time Analytics takes vector similarity searches to a new level, enabling efficient selection of embeddings, particularly beneficial for applications relying on AI.

Seamless Integration with Fabric Experiences

Real-Time Analytics is designed to seamlessly integrate with other Fabric services and components, from Pipelines and Dataflows to One Lake, Shortcuts, Notebooks, and Eventstream. This cohesive integration ensures a unified and efficient experience as you harness the power of real-time analytics.

Industry Scenarios: Real-Time Analytics in Action 

Real-Time Analytics spans various industries and use cases, as highlighted in the following sections showcasing its application in various industry contexts.


Imagine a global e-commerce giant ensuring that customers worldwide receive their orders promptly. With a vast network spanning the globe and managing a massive volume of packages and data, Real-Time Analytics helps track shipments, optimize delivery routes, and anticipate potential delays in real-time, providing a seamless shopping experience.


For marketing specialists launching new campaigns, Real-Time Analytics offers real-time analysis of campaign impacts on sales, inventory, and logistics. Stream vast amounts of data into your KQL database from Eventstream with minimal latency, perform real-time analysis, and visualize findings in Power BI reports, with immediate adjustments and holistic performance evaluation.


As a business analyst in a global retail chain, you’re tasked with analyzing data from various sources in different formats, such as structured, semi-structured, and unstructured data. Real-Time Analytics provides a scalable solution for data capture and storage with time series analysis. Additionally, you can create geospatial analytics, detect anomalies, and collaborate with project managers to make data-driven decisions.


Real-Time Analytics helps the automotive industry optimize its supply chains by monitoring inventory levels, tracking the movement of parts and components, and predicting maintenance needs. This data-driven approach enhances vehicle performance, reduces downtime, and ultimately delivers a seamless driving experience for customers.


In the field of education, Real-Time Analytics revolutionizes learning by providing educators with insights into student performance and engagement. By using a KQL database, teachers can have instant access to data on student progress. This data can then be queried and visualized in a shareable Power BI report that can help teachers collaborate on tailoring instructional strategies, identify students who may need extra support, and improve overall educational processes.


Real-Time Analytics plays a crucial role in the energy sector. You can use OneLake to collect real-time data incoming from wind turbines and solar panels, and identify new business models through AI-driven analysis of energy distribution, consumption, and customer demand. Doing so allows for the efficient utilization of resources, better grid management, and the integration of renewable energy sources. These capabilities contribute to sustainability and the reduction of energy waste.


As a game developer, you can collect and store incoming data from player behavior and in-game actions across various games for years on end. You can gain valuable insights from your growing data that will assist in personalizing and adjusting gameplay elements, adding challenges, and creating rewards to deliver more immersive and engaging gaming experiences.


Real-Time Analytics in the healthcare industry provides healthcare workers instant access to critical patient data using built-in security and governance policies. This enables healthcare professionals togain real-time and operational insights that assist in making informed decisions backed by data that improve patient care. Monitoring patient vital signs, managing medical records, and optimizing hospital workflows are just a few ways Real-Time Analytics can enhance healthcare processes.


In the logistics sector, Real-Time Analytics helps streamline shipping and transportation operations. It enables the tracking of vehicles and shipments, monitors delivery routes, and predicts potential delays. This ensures that packages and goods reach their destinations on time, reducing costs and improving customer satisfaction.


Real-Time Analytics is instrumental in manufacturing industries, where it boosts production efficiency and product quality. Monitoring equipment performance, detecting anomalies in real-time, and optimizing production processes contribute to higher output and reduced waste.

Oil and Gas:

Real-Time Analytics is invaluable for the oil and gas industry, as it can promote efficient optimization exploration, extraction, and distribution processes. Monitoring drilling operations, managing equipment health, and analyzing seismic data in real time can improve operational efficiency and reduce downtime.

Public Transportation:

Real-Time Analytics enhances public transportation systems by providing real-time information to commuters. This enables passengers to make informed decisions about routes and schedules, leading to more efficient and convenient commutes.


As a retail planning manager, you can use Real-Time Analytics to centralize streaming data from purchase orders, inventory, and manufacturing. You can create a KQL queryset to gain insights from your data in real-time or create Power BI reports to visualize the success of marketing campaigns and make informed decisions to promote sales growth and increase customer satisfaction.


Real-Time Analytics in telecommunications optimizes network performance, ensuring seamless communication experiences for users. It involves monitoring network traffic, identifying and mitigating network issues, and optimizing resource allocation to provide uninterrupted service.

These scenarios demonstrate the versatility and impact of Real-Time Analytics across various industries, making it an indispensable tool for professionals worldwide. As technology continues to advance, the potential applications of real-time analytics will only continue to grow, shaping the future of data-driven decision-making.

The Future of Real-Time Analytics

Our journey has just begun. Real-Time Analytics is committed to ongoing improvement and innovation. Expect exciting developments, including Natural Language to KQL, Real-Time Dashboards, and Copilot integration.

Real-Time Analytics in Microsoft Fabric is your ticket to unlocking the potential of real-time data insights. Whether you’re charting new data horizons, seeking to optimize your analytics solutions, or simply looking for a more efficient and user-friendly data analysis experience, this service is your trusted partner. Stay ahead of the data game and embark on your journey with Real-Time Analytics today. For more information on Real-Time Analytics, see Real-Time Analytics – Microsoft Fabric , get started with Real-Time Analytics in Microsoft Fabric – Training, or check out the newly published Real-Time Analytics white paper.

Related blog posts

Announcing General Availability: Explore the Capabilities of Real-Time Analytics in Microsoft Fabric

June 14, 2024 by Guy Reginiano

Announcing triggers and alerts on Real-Time Analytics Dashboards.

June 12, 2024 by Estera Kot

The Native Execution Engine showcases our dedication to innovation and performance, transforming data processing in Microsoft Fabric. We are excited to announce that the Native Execution Engine for Fabric Runtime 1.2 is now available in public preview. The Native Execution Engine leverages technologies such as a columnar format and vectorized processing to boost query execution … Continue reading “Public Preview of Native Execution Engine for Apache Spark on Fabric Data Engineering and Data Science”