Yeedu Team

About Yeedu Team

The Yeedu Team is a collective of cloud architects, systems engineers, and product builders dedicated to solving the high-cost, high-complexity bottlenecks of modern data platforms. With deep hands-on expertise spanning Spark performance optimization, multi-cloud Kubernetes deployments, and automated data infrastructure, the team writes about real-world benchmarking, cloud economics, and the technical realities of scaling enterprise data workloads.

About Yeedu Team

Articles by Yeedu Team

How to Reduce Databricks DBU Costs in 2026

Are TPC Benchmarks Still Relevant in 2026?

Why Spark autoscaling won't fix your cloud bill

Apache Iceberg compaction: the small-files tax that can cost more than the disease it treats

"Git for data" solved branching. It never solved merging.

Moving Logs Into Iceberg Won't Give You the Datadog Escape You're Imagining

Your CDC-to-Iceberg pipeline probably does not need to be real-time

Choosing the Right EC2 Instance for Spark Workloads: Read the Signals First

Spark Performance Optimization: 10 Techniques That Actually Move the Needle

Top AWS EMR Alternatives in 2026: Cost-Effective Big Data Tools for Modern Data Platforms

How to Scale Spark Workloads Without Scaling Your Cloud Bill

What Is Data Skew in Spark and How Do You Detect It Before It Kills Your Job?

Databricks Serverless vs Yeedu Warm Start: Databricks Serverless cost comparison on AWS

Top 5 Databricks Alternatives That Cut Costs in 2026

Why Your Data Platform Billing Model Is Your Biggest Hidden Cost

Running Databricks Without the Bill Shock: Practical Cost Optimization Strategies

The Challenges of Spark Cost Optimization and Diagnosing Expensive Spark Jobs