In the previous blog of this series, we explored how modern data platforms use columnar file formats like Apache Parquet to store data efficiently and enable faster analytics.

While Parquet significantly improves performance and storage efficiency, it still has limitations when it comes to managing data changes, maintaining data consistency, and handling concurrent operations.

To solve these challenges, modern platforms introduced Delta Tables.

In this blog, we will explore what Delta Tables are, why they were created, and how they improve reliability in modern data platforms such as Microsoft Fabric and Databricks.

In today’s data-driven world, organizations generate and process massive volumes of data every day. Traditional file formats such as CSV and Excel were once sufficient for storing and analyzing business data. However, as data volumes grew and analytics became more advanced, these formats started showing limitations.

Modern data platforms like Microsoft Fabric, Apache Spark, and Databricks rely on optimized data formats designed for large-scale analytics. One of the most widely used formats today is Apache Parquet.

In this blog, we will explore what Parquet is, why it was created, and why it has become the foundation of many modern data platforms.

While working with a mature Power BI semantic model, we faced a simple yet critical question:

Where is the data for each table actually coming from?

To solve this problem, we built a simple automated approach that extracts the data source information for every table directly from the semantic model.

In this article, I will walk through the concept and implementation.

In modern data engineering, organizations deal with multiple data sources such as Excel files, SQL databases, APIs, and cloud storage systems. The challenge is not just collecting the data, but transforming and storing it efficiently for analytics.

Microsoft Fabric simplifies this process using Dataflow Gen2, a low-code data ingestion and transformation tool.

In this blog, we walk through how to ingest data from any source and store it in a Lakehouse using Dataflow Gen2.

For many years, SQL Server Reporting Services (SSRS) has been a reliable solution for creating detailed, paginated reports. Organizations have used it extensively for operational reporting, invoices, financial statements, and structured data outputs.

However, as businesses increasingly demand interactive dashboards, real-time insights, and cloud integration, Power BI has emerged as the modern analytics platform of choice.

Migrating reports from SSRS to Power BI allows organizations to preserve their existing data logic while enabling interactive visualizations, self-service analytics, and scalable cloud-based reporting.