Smart Data Merging: How to Eliminate Duplicates, Improve Performance, and Reduce Costs

Data engineers and architects constantly battle duplicate records, slow queries, and ballooning storage costs.

If you’ve ever run a query and wondered, “Why is this taking forever?” or seen your cloud bill and thought, “This can’t be right…” – then you probably have a bad merge strategy.

Many pipelines rely on UPSERT (update + insert) operations to handle new and updated data. But this leads to unnecessary updates, file rewrites, and performance degradation – especially in big data systems like Databricks (Delta Lake).

So, what’s the solution? A smarter merge strategy that combines:

✅ Insert-Only Merge for preventing duplicates

✅ Efficient partitioning and indexing

✅ Performance optimization to reduce storage bloat

✅ Cost-saving techniques across Azure, AWS, and GCP

Let’s break it down:

Understanding the Duplicate Problem

Imagine your data lake is a warehouse. You receive shipments (data updates) daily. Now, if you:

• Don’t check inventory before restocking → duplicates pile up.

• Throw old stock into random aisles → your queries (pickers) struggle to find items.