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How to use Real-Time Analytics in Microsoft Fabric to stream and query data in near real-time

Real-Time Analytics in Microsoft Fabric : Microsoft Fabric is a new all-in-one analytics solution that aims to simplify and unify data management, data engineering, data science, real-time analytics, and business intelligence. It is built on a lakehouse architecture that leverages the power of Delta Lake and OneLake, a cloud-native storage layer that integrates with Microsoft 365 apps. Microsoft Fabric also offers a seamless and integrated user experience that brings together various components from Power BI, Azure Synapse, and Azure Data Factory.

Real-Time Analytics is one of the key components of Microsoft Fabric. It is a fully managed big data analytics platform optimized for streaming and time-series data. It utilizes a query language and engine with exceptional performance for searching structured, semi-structured, and unstructured data. Real-Time Analytics is fully integrated with the entire suite of Fabric products, for both data loading, data transformation, and advanced visualization scenarios.

In this blog post, we will show you how to use Real-Time Analytics in Microsoft Fabric to stream and query data in near real-time. We will also answer some of the common questions and provide some tips and best practices on how to get the most out of Real-Time Analytics.

How to stream data into Real-Time Analytics?

Real-Time Analytics can ingest data from various sources, such as relational databases, event hubs, or OneLake. You can use different methods to stream data into Real-Time Analytics, such as:

  • Event streams: You can use event streams to capture, transform, and route real-time events from various sources to different destinations, including custom apps. You can create and manage event streams using the Fabric portal or the Azure portal. You can also process events using the processor editor, which allows you to write SQL queries or Python scripts to manipulate the events. You can monitor event streams using the Fabric portal or the Azure portal.
  • KQL database: You can use KQL database to load data from various sources into Real-Time Analytics using Kusto Query Language (KQL) commands. You can create and manage KQL databases using the Fabric portal or the Azure portal. You can also load data from Azure Synapse Analytics using tools such as Spark or SQL.
  • Data Factory: You can use Data Factory to copy data from various sources into Real-Time Analytics using Data Factory pipelines. You can create and manage Data Factory pipelines using the Fabric portal or the Azure portal. You can also orchestrate workflow dependencies within the overall processing framework.

How to query data in Real-Time Analytics?

You can query data in Real-Time Analytics using various analytical engines, such as:

  • SQL: You can use SQL to query data in Real-Time Analytics using the Fabric SQL experience or any SQL client that supports ODBC or JDBC connections. You can also use Power BI to visualize and analyze data in Real-Time Analytics using the new Direct Lake mode in the Analysis Services engine.
  • Spark: You can use Spark SQL or PySpark to query and process data in Real-Time Analytics using the Fabric Data Engineering or Data Science experiences. You can also use Databricks or other Spark applications to access data in Real-Time Analytics using the ADLS Gen2 APIs or SDKs.
  • Python: You can use Python libraries such as pandas or numpy to manipulate data in Real-Time Analytics using the Fabric Data Science experience. You can also use Azure Machine Learning or other Python applications to access data in Real-Time Analytics using the ADLS Gen2 APIs or SDKs.

You can query all data in Real-Time Analytics through data items, which are logical representations of your data that provide tailored experiences for each persona. For example, a lakehouse is a data item that gives you a Spark developer experience over your data. A warehouse is a data item that gives you a SQL developer experience over your data.

You can create data items in Microsoft Fabric using tools such as lakehouses or warehouses. You can also import existing SQL scripts or views into Microsoft Fabric as data items.

You can reference any table or file in OneLake using the OneLake syntax, which is similar to SQL syntax but with some differences. For example, you can use ONELAKE.[folder].[table] to reference a table stored in a folder in OneLake. You can also use ONELAKE.[folder].[file] to reference a file stored in a folder in OneLake.

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What are some of the benefits and advantages of using Real-Time Analytics?

Real-Time Analytics offers many benefits and advantages over traditional batch analytics, such as:

  • Simplified and unified data integration: You don’t have to worry about managing multiple storage accounts or resources for your analytics needs. You have one single place to store and access all your analytics data.
  • Improved collaboration and governance: You can easily share and reuse your data across different teams and projects without creating silos or duplicating data. You can also apply consistent security and compliance policies across your entire organization.
  • Enhanced performance and scalability: You can leverage the power of Delta Lake and ADLS Gen2 to perform fast and reliable analytics on large and complex data. You can also scale your analytical engines on demand without affecting your storage layer.
  • Seamless integration and interoperability: You can use the analytical engine of your choice to access and query data in Real-Time Analytics without any data movement or transformation. You can also integrate with other Microsoft 365 apps or Azure services to enrich and extend your analytics capabilities.

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What are some of the common questions and tips on using Real-Time Analytics?

Here are some of the common questions and tips on using Real-Time Analytics:

  • How do I get started with Real-Time Analytics? To get started with Real-Time Analytics, you need to create a Microsoft Fabric workspace and link it to your existing ADLS Gen2 account. You can then import or create data items in your workspace and access them using the Fabric experiences or other analytical engines. For more information, see Creating a lakehouse with OneLake.
  • How do I monitor and optimize my Real-Time Analytics usage and costs? You can use the Fabric portal or the Azure portal to monitor and optimize your Real-Time Analytics usage and costs. You can also use tools such as Azure Cost Management or Azure Advisor to track and reduce your spending. For more information, see Monitoring and optimizing Real-Time Analytics usage and costs.
  • How do I secure and control access to my data in Real-Time Analytics? You can use the Fabric portal or the Azure portal to secure and control access to your data in Real-Time Analytics. You can also use tools such as Azure Active Directory or Azure Role-Based Access Control to manage user identities and permissions. For more information, see Real-Time Analytics security.
  • How do I discover and manage my data in Real-Time Analytics? You can use the Fabric portal or the Azure portal to discover and manage your data in Real-Time Analytics. You can also use tools such as Azure Purview or Azure Data Catalog to catalog and classify your data assets. For more information, see Real-Time Analytics data hub.

Conclusion

Real-Time Analytics is a key component of Microsoft Fabric that enables you to stream and query data in near real-time. It simplifies and unifies data integration, collaboration, governance, performance, scalability, integration, and interoperability.

In this blog post, we have shown you how to use Real-Time Analytics in Microsoft Fabric to stream and query data in near real-time. We have also answered some of the common questions and provided some tips and best practices on how to get the most out of Real-Time Analytics.

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