Скачать или смотреть microsoft fabric data warehouse vs lakehouse vs kql database

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certainly! microsoft fabric is a unified analytics platform that provides various data management solutions, including data warehouses, lakehouses, and kql (kusto query language) databases. each of these components has unique characteristics, use cases, and advantages. below, we will explore each of these concepts in detail, along with code examples to illustrate how they can be used.

1. microsoft fabric data warehouse

overview
a data warehouse in microsoft fabric is designed for structured data storage and analytical processing. it is optimized for high-performance querying and reporting, making it suitable for business intelligence (bi) applications.

key features
**schema-based**: data is organized in a structured format, using tables and relationships.
**optimized for olap**: it supports online analytical processing (olap) for complex queries and aggregations.
**data transformation**: supports etl (extract, transform, load) processes to prepare data for analysis.

code example
here's an example of how you might create a table and insert data into a data warehouse using sql:



2. microsoft fabric lakehouse

overview
a lakehouse combines the features of data lakes and data warehouses, allowing for both structured and unstructured data storage. it provides flexibility in data management and is suitable for big data analytics.

key features
**unified storage**: it can handle both structured data (like a data warehouse) and unstructured data (like a data lake).
**schema on read**: data is stored in its raw format, and the schema is applied when the data is read.
**supports ai/ml workloads**: ideal for data science and machine learning applications.

code example
you can use spark sql to interact with a lakehouse. below is an example of how to create a delta table in a lakehouse:



3. kql (kusto query language) database

overview
kql databases are designed for real-time analytics and log data querying. they are optimized for large-scale data ...

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Microsoft Fabric
Data Warehouse
Lakehouse
KQL Database
Data Architecture
Cloud Data Solutions
Analytics
Data Management
Big Data
Real-time Analytics
ETL Processes
Data Storage
Scalability
Data Integration
Business Intelligence