Tag: Data Engineering

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”

Getting Started with StreamLit: Build Interactive Data Apps in Python

  In this blog, we will explore the Streamlit library, which simplifies the creation of data-driven web applications without having prior knowledge of front-end development. 

INTRODUCTION 

Streamlit is an open-source Python library that simplifies the creation of interactive web apps for data science and machine learning projects. It is highly user-friendly, with minimal coding required to turn Python scripts into shareable web apps. It allows developers and data scientists to create interactive, visually appealing applications with minimal effort by focusing on writing Python code rather than dealing with front-end development.  Continue reading “Getting Started with StreamLit: Build Interactive Data Apps in Python”

Data Privacy Challenges in Cloud Environments

In today’s technology-centric landscape, businesses are increasingly relying on cloud computing for storing, processing, and managing their data. There are many benefits to using the cloud, such as scalability, cost savings, and flexibility. However, the transition to a cloud environment also poses serious data security issues that require serious attention. Concerns such as data breaches, unauthorized access, and data loss incidents are on the rise, underscoring the need to implement robust security measures in cloud settings. Continue reading “Data Privacy Challenges in Cloud Environments”