lakehouse Archives - Microsoft Business Intelligence

Ensuring Data Integrity in Microsoft Fabric: Validating Excel Headers in Data Pipeline Before Loading Data into the Destination

In data engineering, maintaining data integrity is crucial. One common challenge is ensuring that an incoming file contains all the required column headers before loading it into the destination table. In this blog, we will implement a solution in Azure Fabric where we: Load an Excel file into Lakehouse. Extract and validate the column headers. … Continue reading Ensuring Data Integrity in Microsoft Fabric: Validating Excel Headers in Data Pipeline Before Loading Data into the Destination

Overcoming Lakehouse Limitations: Implementing Upserts with PySpark

Lakehouses combine the best features of data lakes and data warehouses, offering scalable and cost-effective solutions for storing and processing large datasets. However, they come with a notable limitation: insert, update, and delete operations on tables are not natively supported. This poses a challenge for use cases requiring data synchronization or incremental updates. To overcome… Continue reading Overcoming Lakehouse Limitations: Implementing Upserts with PySpark