lakehouse on Mooncake

Mooncake is joining Databricks!

Wed, 01 Oct 2025 00:00:00 +0000

Thirteen months ago, we started Mooncake to bring the data intelligence stack that powers giants like OpenAI to every vibe-coded app and developer. From first prompt to IPO.

Today, we’re joining forces with Databricks. The mission continues: make Lakebase and the Lakehouse feel like one.

For a decade, we thought the final form of a database was one that could do it all. Fast updates, transactions, analytics. For database people, this was HTAP. It never quite landed. Zhou and Cheng built it. Pranav tried to sell it. It was hard.

No, Iceberg won’t fix your data stack.

Mon, 21 Jul 2025 00:00:00 +0000

Life is expectations vs reality. Here is one for Apache Iceberg – a favorite amongst the data community.

Expectations: Iceberg is the solution to all my data pains

See, the core of Iceberg is pretty neat:

Keep all data in object storage in open file formats like Parquet.
Fool any execution engine (DuckDB, ClickHouse, Spark, Snowflake, etc.) into thinking these files are a table and query them with SQL directly.
[Ongoing progress] Get exceptional performance on these tables, without having to load data into a system-specific format.

And with clever design you get:

but, my Postgres (+ lakehouse) is free

Fri, 13 Jun 2025 00:00:00 +0000

Disclaimer: Your Postgres and Lakehouse are free and open too. You don’t need to be (or spend like) a giant to use them.

Why Postgres + Lakehouse?

Reason #1: If you believe in AI, this is the agent stack

Agents need memory. Both working memory and longer-term knowledge.

Working memory has to be instant, reliable, and accurate, just like app state. What’s the best place to store it? Postgres. That’s true for both apps and agents. This state is typically tied to an individual user, tenant, or agent.

HTAP is Dead

Mon, 05 May 2025 00:00:00 +0000

This blog is inspired by Jordan Tigani’s blog titled “Big Data is Dead”. Jordan and I actually spent some time building an HTAP database at SingleStore.

The good old days (’80s)

Back in the ’80s, one relational database did everything. Transactions (OLTP) during the day and reports after hours (OLAP). Databases like Oracle V2 and IBM DB2 ran OLTP and OLAP on the same system; largely because data sets still fit on a few disks and compute was costly.

wth are S3 tables

Sun, 12 Jan 2025 00:00:00 +0000

Amazon announced table buckets during re:Invent last year. An S3 bucket that’s also a table? What does that mean?

TL;DR: S3 tables allow Iceberg tables to exist without a catalog. Let me explain.

Iceberg vs Delta Lake

What? I thought Iceberg and Delta are just table formats? Why are we comparing the way they’re implemented?

A key difference between Iceberg and Delta Lake is “Delta tables can exist without a catalog, but Iceberg cannot”, due to their metadata design:

pg_mooncake + Neon: serverless analytics in Postgres

Tue, 07 Jan 2025 00:00:00 +0000

pg_mooncake launched in beta two months ago. It adds columnar storage and a vectorized engine to Postgres.

Over the last two months, with our customers, we’ve realized we accidentally built the best serverless database for analytics. Here’s the story:

1. pg_mooncake 🤝 Neon storage engine

Neon innovated significantly on its storage engine.

There’s a great thread by Nikita on this. This storage engine is how Neon can achieve true separation of storage & compute. This leads to an exceptional DX around branching, scale-to-zero, read replicas, instant provisioning… All of these are goodies of a true serverless Postgres.

Mooncake: next-gen search and analytic systems

Wed, 30 Oct 2024 00:00:00 +0000

We are Zhou, Cheng, and Pranav. And we’ve spent the last decade building databases at MemSQL (now SingleStore).

We love complex systems, deep-tech engineering, and naturally – physics 🙂 Data systems, much like physics, are built on certain principles. The principles need to be unanimous and durable.

Building systems that are simply ’technically’ better is futile. The success of a data system depends on the principles it embodies, and changing them along the way is extremely difficult. Closed-source companies can’t go to open source and expect adoption. Databases can’t just abandon their storage format and be effective stateless engines.

Reflections from the Iceberg Meetup

Tue, 08 Oct 2024 00:00:00 +0000

Last month, I spoke at the Iceberg meetup at Snowflake HQ. Public speaking has never been my favorite, and knowing Snowflake CEO Sridhar and Tabular CEO Ryan were in the audience did not help.

It was day 18 of starting Mooncake Labs, and I spoke about a general indexing framework for Iceberg. You can find the details here. Here were some of my learnings:

1. Great products are a reflection of its community.

I spent the last decade building a proprietary database. Every time a feature request came in, engineering and product leaders would spend hours prioritizing its impact. Almost every time, it boiled down to one question: How much ARR? I watched my best engineers spend months building awkward hooks and extensions that did not fit the bigger picture. I often thought, Just ask the customer to make a PR :) The end product of this type of prioritization process tends to look like: