Author: Ramneek Kaur

I am a Data Engineer at OpsTree Global, currently contributing to major data initiatives for enterprise customers.I have deep expertise in designing and implementing scalable ETL pipelines, building robust cloud-native data platforms, and enabling real-time analytics using tools like BigQuery, GCP, Airflow, Kafka, and Mesh.My work focuses heavily on query performance tuning, pipeline reliability, and cost optimization, ensuring that data systems remain both efficient and resilient under scale. I hold a B.Tech degree in Computer Science and am passionate about building future-safe, maintainable data ecosystems that empower data-driven decision-making in organizations.

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”

How to Optimize Amazon Redshift for Faster and Seamless Data Migration

When it comes to handling massive datasets, choosing the right approach can make or break your system’s performance. In this blog, I’ll take you through the first half of my Proof of Concept (PoC) journey—preparing data in Amazon Redshift for migration to Google BigQuery. From setting up Redshift to crafting an efficient data ingestion pipeline, this was a hands-on experience that taught me a lot about Redshift’s power (and quirks). Let’s dive into the details, and I promise it won’t be boring!

Continue reading “How to Optimize Amazon Redshift for Faster and Seamless Data Migration”

Exploring Time Travel Queries in Apache Hudi

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an advanced data management framework designed to efficiently handle large-scale datasets. One of its standout features is time travel, which allows users to query historical versions of their data. This feature is essential for scenarios where you need to audit changes, recover from data issues, or simply analyze how data has evolved over time. In this blog post, we’ll walk through the process of setting up Hudi for time travel queries, using AWS Glue and PySpark for a hands-on example. Continue reading “Exploring Time Travel Queries in Apache Hudi”