Mukesh Vijayan

A lot of the attention in data lake projects goes into the build – storage, ingestion, table formats, governance. All of that

Adding a new data source to your lake feels like straightforward progress – more data available, more questions answerable. But each new

A data lake architecture that works beautifully at a few hundred gigabytes doesn’t automatically work the same way at fifty terabytes, and

We’ve covered the business-level comparison between a data lake and a lakehouse elsewhere – what changes, when each fits, how to decide.

Most lakehouse performance problems don’t announce themselves. There’s rarely a single dramatic failure – instead, queries get a little slower each month,

Ask someone to sketch a lakehouse architecture on a whiteboard, and you’ll usually get two boxes: storage and compute. That’s not wrong,

Choosing an open table format is one of the first real architecture decisions in any lakehouse project, and it’s also one of

Azure’s storage naming history is genuinely confusing. There was Azure Data Lake Store (now commonly called Gen1), plain Azure Blob Storage, and

Most articles about AWS data lakes jump straight into advanced architecture – multi-zone medallion structures, Lake Formation governance models, cross-account access patterns.

If your organization is drowning in data but starving for insights, you’re not alone. Most enterprises today collect data from a dozen