Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by FNU Aditya Ranjan
Why Modern Data Lakehouses Fail at Scale: The Metadata Problem Nobody Talks About
Open table formats have transformed analytical storage, but the performance of a lakehouse increasingly depends on metadata architecture, maintenance, and workload-aware data organization. The Lakehouse Solved One Problem and Exposed Another The modern data lakehouse emerged from a compelling idea: combine the flexibility and economics of object storage with the reliability and management capabilities traditionally associated with data warehouses.
The Hidden Cost of Real-Time Data: Why Faster Pipelines Don't Always Deliver Better Decisions
Latency is only one dimension of real-time data engineering. Correctness, freshness, recoverability, and operational cost determine whether a streaming architecture delivers useful business value. Real-Time Is Easy to Promise. Reliable Real-Time Is Harder to Deliver. The appeal of real-time data is obvious. Organizations want to react to transactions as they happen, detect anomalies before they become incidents, and make operational decisions using the most current information available.
Your data platform was built for queries. AI agents are changing the contract
Your data platform was built for queries. AI agents are changing the contract Enterprise data systems were designed to inform people and applications. As AI agents begin to act on that data, familiar concerns such as freshness, lineage, semantics, identity and observability become runtime controls. Oct 14, 2026 8:00 AM 6 min read Photo by Charles Forerunner / Unsplash This content is for members only Subscribe Already have an account? Sign in
Why adding executors rarely fixes the most expensive Spark problems A slow Spark job is often treated as a sizing problem: add executors, increase memory, raise shuffle partitions, and try again. That can hide the real issue. The expensive failures I see in Spark architectures usually come from unnecessary shuffles, skewed joins, poor file layout, stale or missing statistics, oversized scans, and pipelines designed without regard for the physical execution plan.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with FNU Aditya
Contact FNU Aditya, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack