Lakehouse in Microsoft Fabric : A lakehouse is a new data architecture that combines the best elements of data lakes and data warehouses. It enables you to store and analyze data of various types and formats, such as structured, semi-structured, and unstructured data, in a single platform. A lakehouse also provides features such as transactions, data quality, consistency, isolation, indexing, caching, and query optimization that are typically found in data warehouses.
Microsoft Fabric is an all-in-one analytics solution that offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place. With Fabric, you can create a lakehouse that is based on low-cost and directly-accessible cloud storage in open formats. You can also leverage powerful AI models and tools to accelerate your analysis and empower everyone in your organization to act on insights.
In this blog post, we will show you how to create a lakehouse in Microsoft Fabric using the following steps:
- Create a OneLake account and connect your data sources
- Use Data Engineering to transform and curate your data
- Use Data Science to build and deploy AI models
- Use Power BI to visualize and share your insights
Step 1: Create a OneLake account and connect your data sources
OneLake is the unified data foundation of Microsoft Fabric. It allows you to establish an open and lake-centric hub that helps you connect and curate data from different sources, such as relational databases, NoSQL databases, files, streams, and APIs. You can also access your existing Azure Data Lake Storage (ADLS) accounts or create new ones within OneLake.
To create a OneLake account, you need to sign up for a Microsoft Fabric trial. Once you have an account, you can access OneLake from the Microsoft Fabric homepage. You can then use the Data Sources tab to browse and connect your data sources. You can also use the Data Factory tab to create and manage data pipelines that move and transform your data.
Step 2: Use Data Engineering to transform and curate your data
Data Engineering is the experience that provides a world-class Spark platform with great authoring experiences, enabling you to perform large-scale data transformation and democratize data through the lakehouse. You can use Data Engineering to write SQL queries or Python notebooks that run on Spark clusters. You can also use Data Factory to schedule and orchestrate your notebooks and Spark jobs.
To use Data Engineering, you need to switch to the Data Engineering tab from the Microsoft Fabric homepage. You can then use the Data Explorer to browse and query your data sources. You can also use the Notebooks tab to create and run notebooks that use PySpark or Spark SQL. You can also use the Jobs tab to submit Spark jobs or monitor their status.
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Step 3: Use Data Science to build and deploy AI models
Data Science is the experience that enables you to build and deploy AI models on a single foundation without data movement. You can use Data Science to write Python or R code that leverages popular frameworks such as TensorFlow, PyTorch, scikit-learn, or ML.NET. You can also use AutoML to automatically generate high-quality models for your data.
To use Data Science, you need to switch to the Data Science tab from the Microsoft Fabric homepage. You can then use the Notebooks tab to create and run notebooks that use Python or R kernels. You can also use the Models tab to register, manage, or deploy your models. You can also use the AutoML tab to create or run automated machine learning experiments.
Step 4: Use Power BI to visualize and share your insights
Power BI is the experience that allows you to create stunning reports and dashboards that showcase your insights. You can use Power BI to connect to your data sources, design interactive visuals, apply filters and slicers, add custom calculations, and share your reports with others.
To use Power BI, you need to switch to the Power BI tab from the Microsoft Fabric homepage. You can then use the Get Data button to connect to your data sources. You can also use the Report view or the Dashboard view to create or edit your reports or dashboards. You can also use the Publish button to save or share your reports with others.
Conclusion
In this blog post, we have shown you how to create a lakehouse in Microsoft Fabric using four simple steps. By using Microsoft Fabric, you can enjoy a highly integrated, end-to-end, and easy-to-use product that is designed to simplify your analytics needs. You can also benefit from the features and capabilities of OneLake, Data Engineering, Data Science, and Power BI that work together seamlessly on a single platform.
If you want to learn more about Microsoft Fabric or try it for yourself, you can visit the official website or sign up for a free trial. You can also check out the following resources for more information:
- What is Microsoft Fabric?
- Microsoft Fabric webinar series
- [Microsoft Fabric documentation]
- [Microsoft Fabric blog]
FAQs
- What is the difference between a data lake and a lakehouse?
- A data lake is a repository for raw data in a variety of formats, such as structured, semi-structured, or unstructured data. A lakehouse is a data platform that builds on top of a data lake and adds features such as transactions, data quality, consistency, isolation, indexing, caching, and query optimization that are typically found in data warehouses.
- What are the benefits of using Microsoft Fabric to create a lakehouse?
- Microsoft Fabric offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place. With Fabric, you can create a lakehouse that is based on low-cost and directly-accessible cloud storage in open formats. You can also leverage powerful AI models and tools to accelerate your analysis and empower everyone in your organization to act on insights.
- How much does Microsoft Fabric cost?
- Microsoft Fabric is currently in preview and offers a free trial for up to 25 users. You can sign up for a trial1 and get a fixed Fabric trial capacity for each business user, which may be used for any feature or capability. The pricing details for Microsoft Fabric will be announced when it becomes generally available.