Tag: Data Engineering

The Ultimate Guide to Cloud Data Engineering with Azure, ADF, and Databricks

Introduction

In today’s data-driven world, organisations are constantly seeking better ways to collect, process, transform, and analyse vast volumes of data. The combination of Databricks, Azure Data Factory (ADF), and Microsoft Azure provides a powerful ecosystem to address modern data engineering challenges. This blog explores the core components and capabilities of these technologies while diving deeper into key technical considerations, including schema evolution using Delta Lake in Databricks, integration with Synapse Analytics, and schema drift handling in ADF. Continue reading “The Ultimate Guide to Cloud Data Engineering with Azure, ADF, and Databricks”

Complete Guide to Fixing PostgreSQL Performance with PgBouncer Connection Pooling

Several factors affect database performance, and one of the most critical is how efficiently your application manages database connections. When multiple clients connect to PostgreSQL simultaneously, creating a new
connection for each request can be resource-intensive and slow. This is where connection pooling comes into play. Connection pooling allows connections to be reused instead of creating a new one every time, reducing overhead and improving performance. In this blog, we’ll explore PgBouncer, a lightweight PostgreSQL connection pooler, and how to set it up for your environment. Continue reading “Complete Guide to Fixing PostgreSQL Performance with PgBouncer Connection Pooling”

Building a Scalable And Cost-Efficient BigQuery Platform: Architecture, Practices & Lessons

As data platforms evolve from proof-of-concept pipelines to business-critical systems, scaling BigQuery requires more than writing efficient SQL. Without the right architectural choices, governance, and monitoring, organizations often face unpredictable costs, query slowdowns, and operational instability.

This blog outlines a set of platform-level engineering decisions and best practices adopted to run BigQuery at scale—focused on performance, cost optimization, security and observability. Each practice is backed by real-world implementation examples. Continue reading “Building a Scalable And Cost-Efficient BigQuery Platform: Architecture, Practices & Lessons”

LLM-Powered ETL: How GenAI is Automating Data Transformations

We’ve made huge strides in collecting data. Businesses today generate terabytes from apps, sensors, transactions, and user behavior. But the moment you want to do something with that data (feed it into dashboards, power models, trigger business logic), you run straight into the mess of transformation. 

You’ve probably seen this first-hand. Engineers spend weeks writing brittle transformation code. Every schema update breaks pipelines. Documentation is missing. Business logic is locked away in obscure ETL scripts no one wants to touch. This is the silent tax on your data operations: not gathering data, but shaping it.  Continue reading “LLM-Powered ETL: How GenAI is Automating Data Transformations”

How OpsZilla Achieved Zero-Downtime MySQL Migration with Scalable Data Engineering Practices

Running a growing e-commerce platform like Opszilla is thrilling. You’re processing thousands of orders daily across the US and Canada, scaling infrastructure, and expanding into new markets. But amidst all that momentum, something  starts to break: your data infrastructure and database performance.

At first, it’s subtle—slower queries, lagging reports, a few scaling hiccups. Then the real issue surfaces: you’re still running on MySQL 5.7, a version nearing its end-of-life in October 2023.

Continue reading “How OpsZilla Achieved Zero-Downtime MySQL Migration with Scalable Data Engineering Practices”