In our previous post, we broke down the five hidden costs of running two compute engines in your lakehouse — the infrastructure duplication, the cost opacity, the metadata sync bugs, the skills fragmentation, and the governance headaches. We showed that this dual-engine tax can run $40,000+ per year for a mid-size data team.

The obvious question: why not just use Spark for everything?

The honest answer has always been: because Spark cannot deliver query results to BI tools fast enough. Not because Spark cannot execute the query — it usually can — but because the last mile of data delivery through traditional JDBC/ODBC protocols is painfully slow.

Arrow Flight SQL eliminates that bottleneck. And with it, the primary architectural reason for running a second query engine disappears.

Take an honest look at your data platform architecture. If you are running a lakehouse on AWS, there is a good chance it looks something like this: Spark clusters for ETL and data engineering, plus Trino (or Dremio, or Presto) clusters for analytics and BI queries. Two engines, two teams, two bills — all pointed at the same data.

This dual-runtime pattern has become the default architecture for most modern data platforms. And on the surface, it makes sense. Spark is great at processing data. Trino is great at querying it. Each engine solves a real problem.

But running two engines has hidden costs that most organizations never quantify — and once you add them up, the number is hard to ignore.

"Which platform is cheapest for Spark?" is one of the most common questions data teams ask — and one of the most misleading. The honest answer is: it depends entirely on your workload shape.

A platform that saves you thousands on large nightly batch jobs might quietly waste thousands on your fleet of small ETL runs. The billing model that looks transparent at first glance might hide costs in cold starts, minimum increments, or idle compute you never asked for.

In this post — Part 3 of our compute cost series — we compare Databricks, Amazon EMR, and Cazpian across three realistic workload scenarios. No hypotheticals. Real pricing. Real math.

In Part 1 of this series, we exposed the Small Job Tax — the hidden cost of cold starts, overprovisioned clusters, and per-job infrastructure overhead that silently drains data budgets. We showed that for many teams, more than half of their Spark compute spend goes to infrastructure bootstrapping, not actual data processing.

The natural follow-up question: what if you could eliminate that overhead entirely?

That is exactly what Cazpian Compute Pools are built to do.

Most data teams obsess over optimizing their biggest, most complex Spark jobs. Meanwhile, hundreds of tiny ETL jobs — each processing a few gigabytes — quietly rack up a bill that nobody questions.

We call it the Small Job Tax: the disproportionate cost of running lightweight workloads on infrastructure designed for heavy lifting. And for many organizations, it is the single largest source of wasted compute spend.

As enterprises expand across AWS, Azure, and Google Cloud, multi-cloud analytics becomes a strategic requirement. Choosing the right architecture — lakehouse or data warehouse — shapes cost, governance, and future growth. This guide explains the differences with practical guidance for multi-cloud systems.

This post was created automatically without writing any markdown files!

Is it magic?​

No, it is just code.

The biggest challenge in AI today isn't the model—it's the data. Cazpian AI Studio was built to bridge the gap between your Lakehouse and your AI applications.

We are excited to announce Cazpian, a new kind of data platform built from the ground up for AWS.

In today's world, data teams face a constant struggle: how to manage massive amounts of data without getting bogged down by infrastructure complexity. Cazpian solves this by combining the power of Apache Iceberg and Apache Spark into a seamless, managed experience.

When we started building Cazpian, we had a choice of many different storage formats and compute engines. We chose Apache Iceberg and Apache Spark for three main reasons: