What is Lakehouse in Microsoft Fabric?

Lakehouse in Microsoft Fabric


Microsoft Fabric is a comprehensive data and analytics Software-as-a-Service (SaaS) cloud offering by Microsoft. One of its essential components is the Lakehouse.

What is Microsoft Fabric?

Microsoft Fabric is an end-to-end data and analytics SaaS cloud suite recently introduced by Microsoft. It provides a structured approach to data storage and management. For a detailed understanding, you can refer to my previous article on the topic.

Introduction to Lakehouse


The term "Lakehouse" merges the concepts of Data Lake and Data Warehouse. It serves as a unified storage location for both structured data (like in a Data Warehouse) and unstructured data (as in a Data Lake). The Lakehouse can scale to handle extensive data volumes and integrates seamlessly with other tools and services for data manipulation and retrieval.

In simpler terms, a Lakehouse functions like a database where you can store both tables and files.

Creating a Sample Lakehouse

A Lakehouse is an element within the Data Engineering workload of Microsoft Fabric. To create a Lakehouse:

  • Navigate to the Microsoft Fabric portal.
  • Go to the Data Engineering home page.
  • Assign a name to your Lakehouse and create it.


Once created, your Lakehouse will initially be empty, without any tables or data. We will add data to it in the next steps.

Loading Data into Lakehouse

There are several methods to load data into a Lakehouse:

  • Dataflows Gen2
  • Data pipelines
  • New Shortcut
  • New Eventstream


The Lakehouse Explorer is the default interface you see upon creating a Lakehouse. Within this environment, you can upload files directly or use other methods to load data.

SQL Endpoint for Lakehouse

A key feature of the Lakehouse is the SQL Endpoint, which allows you to query the data stored in its tables using SQL. However, this endpoint only supports data querying, not data manipulation (insertion, updating, or deletion).

To access the SQL Endpoint:

  1. Navigate to your Workspace and click on the SQL Endpoint.
  2. This opens a SQL editor where you can write and execute SQL queries.
  3. You can also use a visual query editor, like Power Query editor.
  4. The connection string for the SQL Endpoint can be found under settings, allowing you to connect to it from other tools like SQL Server Management Studio (SSMS).

Summary

The Microsoft Fabric Lakehouse is a versatile storage solution that combines the best of Data Warehouses and Data Lakes. You can load data using various methods like Dataflows Gen2 and interact with the data through SQL or visualization tools like Power BI. This article serves as an introductory guide, offering a starting point for leveraging the full capabilities of a Lakehouse in Microsoft Fabric.



Comments

Post a Comment

Popular posts from this blog

Embedding Power BI Reports in Websites and Applications: A Complete Guide

Embedding Power BI Reports in Websites and Applications: A Complete Guide Introduction Embedding Power BI reports in websites and applications is a powerful way to share interactive data visualizations with a broader audience. Whether you want to embed a report for internal users, customers, or the general public, Power BI provides several options to make this process seamless. In this guide, we will walk you through the various embedding options and the steps required to embed Power BI reports in your web pages or custom apps. 1. Why Embed Power BI Reports? Embedding Power BI reports offers several benefits: Increased Accessibility : Allow users to view and interact with reports without needing to access Power BI Service directly. Enhanced User Experience : Integrate data insights directly within your application or website for a seamless user experience. Improved Data Sharing : Share valuable insights with users outside your organization, such as clients or partners, in a secure and ...
Read more

Import Microsoft Planner Data into Power BI Using Power Automate and SharePoint

Image
  In this guide, we’ll cover how to bring task data from Microsoft Planner into Power BI for analysis, using Power Automate and SharePoint as intermediaries. Since Power BI currently lacks a direct Planner connector, Power Automate lets us fetch the data from Planner and save it to SharePoint as a JSON file, enabling Power BI to access it on a scheduled basis. Prerequisites Before you begin, ensure you have: Access to Microsoft Planner  – to view and retrieve Planner task data. Power Automate License  – access to create and run Power Automate flows. SharePoint Access  – a SharePoint site to store JSON files. Using SharePoint simplifies the setup, as it doesn’t require a premium Power BI or Power Automate license. Power BI Desktop  – to set up and configure the JSON data connection and publish the report. Microsoft 365 Account  – for authentication, as we’ll be connecting Power BI to SharePoint through your organization’s Microsoft 365 credentials. Step 1: S...
Read more

Difference between Append and Merge in Power BI

Image
Append vs. Merge in Power BI: Key Differences and Use Cases Introduction When working with data in Power BI, combining datasets is often essential for creating comprehensive reports. Power BI offers two primary methods for combining tables: Append and Merge . While both are used to consolidate data from multiple sources, they work in different ways and are suited for different scenarios. In this blog, we will explore the key differences between Append and Merge in Power BI, along with practical examples to help you choose the right approach for your data needs. 1. What is Append in Power BI? Append in Power BI is used to combine rows from two or more tables into a single table. The structure of the tables being appended should be similar (i.e., they should have the same columns), as Append simply stacks the rows on top of each other. Key Features of Append: Row Combination : Append combines rows from multiple tables, one after the other. No Column Matching : Columns are not matched b...
Read more