Category: Data Engineering

The Complete Guide To Data Pipelines With Architecture, Types and Use Cases 

In the modern enterprise, data isn’t just an asset,  it’s the lifeblood of decision-making. But raw data is like crude oil – it holds immense potential but is unusable in its natural state. It must be extracted, refined and transported to where it can power the business. This is the fundamental role of a data pipeline. For any leader looking to build a truly data-driven organization, understanding and investing in robust data pipeline architecture is not an IT expense, it’s a strategic imperative. 

This guide moves beyond the technical jargon to explore why data pipelines are the bedrock of business agility, how to build them effectively and the tangible outcomes they deliver.  Continue reading “The Complete Guide To Data Pipelines With Architecture, Types and Use Cases “

Top Data Engineering Companies in India 2025

In today’s data-centric world, information is everywhere, but its true potential shines only through effective data engineering. As we move towards 2025, the ability to build, maintain, and innovate robust data infrastructures will distinguish leading companies in the industry. Indian technology companies are leading this transformation, providing excellent expertise to transform complex data into streamlined strategic resources. For businesses and professionals, adapting to this dynamic landscape is essential to achieve success in the future.  Continue reading “Top Data Engineering Companies in India 2025”

Building a Reliable Cloud Data Storage Architecture for Big Data

Introduction 

As businesses continue to generate large amounts of data every day, it has become essential to establish a reliable cloud data storage architecture. Whether you’re working with analytics workloads, IoT data, or datasets for AI training, a thoughtfully designed cloud storage setup guarantees scalability, availability, and high performance while keeping costs and security under control. 

In this guide, we will discuss designing a cloud data storage architecture suitable for big data, its components, best practices, and cutting-edge technologies that are fueling data-driven innovation.  Continue reading “Building a Reliable Cloud Data Storage Architecture for Big Data”

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”