Deploying a Serverless Data Analytics Stack with Azure Functions & Databricks
Today, data is everywhere, and companies need faster and smarter ways to analyze it. Serverless Data Analytics helps organizations process, analyze, and visualize data without managing servers. Using tools like Azure Functions and Azure Databricks, teams can build automated, scalable, and cost‑effective data analytics solutions for real‑world business needs. Every time someone buys something online, opens an…
Best Practices for Data Partitioning and Optimization in Big Data Systems
Best Practices for Data Partitioning and Optimization in Big Data Systems Data Partitioning and Optimization guide you through a complete PySpark workflow using simple sample data. You learn how to load data, fix column types, write partitioned output, improve Parquet performance, and compact small files in a clear, beginner-friendly way. Introduction This blog explains Best…
Architecting Robust ETL Workflows Using PySpark in Azure
Architecting Robust ETL Workflows Using PySpark in Azure Creating an ETL workflow is one of the first practical tasks you will undertake as a beginner in data engineering. The process of moving and cleaning data before it is prepared for dashboards or analysis is known as extract, transform, and load, or ETL. This article will…
What is a Data Lake and How Does It Work?
A data lake is a central repository that allows you to store all your structured and unstructured data at any scale. It’s designed to handle large volumes of data with low latency, and it enables you to store data in its raw format and process and analyze it using various tools and technologies. One of…