Vineet Grover

Spark’s Coalesce vs Repartition vs Repartition-by-Range – My Experience with them   If you’ve spent any time tuning Spark jobs, you’ve run into the classic question: do I call `coalesce()`, `repartition()`, or `repartitionByRange()`? All three change how your data is partitioned across the cluster, but they behave very differently under the hood — and choosing […]

Modern data platforms live or die by how quickly you can ship code to production.For one of our recent projects, that speed was determined by something deceptively simple: adding a custom Python module to our distributed jobs. We started on AWS EMR Serverless and later moved to Azure Databricks for compute jobs.Both platforms can absolutely

You’ve seen what Databricks can do. Here’s why running it on Azure unlocks a completely different experience. We’ve been backing Databricks for a while now. Our customers have used it on AWS to build lakehouses, run ML pipelines, and unify analytics at serious scale — and the platform has delivered. The technology isn’t in question.

In the age of big data, organizations are swimming in vast oceans of information. While this data holds immense potential, its true value can only be unlocked when it’s accurate, consistent, and free from redundancy. This is where data deduplication, a critical application of artificial intelligence, comes into play. More than just identifying simple matching

1. Introduction Synthetic data generation is increasingly important when working with sensitive or regulated datasets. While generating synthetic data for single tables is straightforward using GANs or statistical models, generating relational multi-table synthetic data is significantly more complex. Relational databases do not exist in isolation. They contain relationships that define how information flows across the

In the realm of data management, integrating information from diverse sources poses significant challenges due to variations in terminology, structure, and content. Traditional matching methods, which depend on exact or approximate string comparisons, often fail to capture underlying meanings, leading to incomplete or inaccurate alignments.  To overcome this, fuzzy logic and phonetic matching became prominent