Running Kafka Inside Snowflake Before Snowflake Did: Part 1 of 4
The Problem Nobody Had Solved Yet
The Problem Nobody Had Solved Yet
In May 2026, my CTO dropped a challenge on my desk: get Apache Kafka running inside Snowflake's container infrastructure, and make sure an external producer can talk to it.
Simple sentence. Not a simple problem.
At that point, nobody had publicly documented how to do this. No blog post, no GitHub repo, no conference talk. Just a gap in the ecosystem sitting there, waiting for someone to stumble into it.
That someone ended up being me. And this is the story of how it went.
First Things First: What is Snowpark Container Services?
Before we get into the chaos, let me give you a quick mental model of the environment we are working in.
Snowflake is a cloud data platform. Most people know it for SQL queries and data warehousing. But over the last few years, Snowflake has been expanding what you can actually run inside it. One of those expansions is Snowpark Container Services, or SPCS for short.
Think of SPCS as Snowflake giving you a small piece of cloud infrastructure to run your own containers in, right next to your data. You bring a Docker image, Snowflake runs it, and it sits inside the Snowflake ecosystem with built-in access to your tables, your credentials, and your compute pools.
It is Snowflake saying: "You don't have to take your data out to process it. Bring your workload in here instead."
For data engineers, this is genuinely exciting. It means you can run things like machine learning models, streaming pipelines, or custom applications without leaving the Snowflake boundary. No data movement, no extra infrastructure to manage.
That is the stage. Now let's talk about the other character in this story: Apache Kafka.
And What is Apache Kafka Doing Here?
Kafka is the industry standard for real-time data streaming. If you have ever worked with systems that need to process a continuous flow of events (think: user clicks, financial transactions, sensor readings, application logs), Kafka is almost certainly involved somewhere.
Here is the core idea: producers send messages to Kafka topics, and consumers read from those topics. It is a high-throughput, fault-tolerant message bus sitting between the systems that generate data and the systems that process it.
Kafka is incredibly common in modern data architectures. And Snowflake, being a data platform, naturally needs to play well with it.
So the question my CTO was asking was: can we run Kafka inside Snowflake's container environment, and can an external system reach it?
Sounds reasonable. But there is a catch. Actually, there are a few.
Why This Had Not Been Solved Before
Let me be straight with you: this was not unsolved because engineers are lazy or because nobody thought of it. It was unsolved because the combination of constraints made it genuinely tricky.
Constraint 1: SPCS is relatively new territory.
Snowpark Container Services was not a mature, battle-tested product by May 2026. The documentation was sparse in places, community examples were limited, and the edge cases around networking were largely unexplored. Developers were still figuring out the basics.
Constraint 2: Kafka does not speak HTTPS.
This is the core technical wall. When SPCS exposes a service to the outside world, it does so through a public HTTPS endpoint. That is the only option. HTTPS is fine for most applications: REST APIs, web services, dashboards, all good.
Kafka, however, does not use HTTP at all. It has its own wire protocol, which runs over raw TCP. These are different things at a fundamental level. HTTPS is a layer built on top of TCP with encryption and a specific request/response structure. Kafka's protocol is binary, stateful, and expects to speak directly over a TCP connection.
Put SPCS's HTTPS-only door next to Kafka's TCP-only language, and you have a mismatch that is not immediately obvious how to resolve.
Constraint 3: There is a hidden second problem inside the first one.
Even if you somehow figure out the network path, Kafka has another trick up its sleeve. When a client connects to a Kafka broker for the first time, the broker responds with metadata: essentially, "here is the address you should use for all future communication."
That address is called the advertised listener. And if that address is not reachable from wherever the client is sitting, every connection after the first one fails. Silently. Without a particularly helpful error message.
So you have two problems stacked on top of each other. Solve only one and the whole thing still breaks. You need to solve both, in the right order, at the same time.
This is exactly why the problem stayed unsolved. It was not one obstacle. It was two, and they looked like one from the outside.
Then Snowflake Summit 2026 Happened
Let's fast-forward from May to June 1st, 2026: the opening of Snowflake Summit.
Snowflake announced a new product called Data Stream. In plain terms, it is a managed Apache Kafka offering built natively into the Snowflake platform. You get Kafka topics, producers, consumers, and all the streaming infrastructure you expect, without having to manage any of it yourself. Snowflake handles the brokers, the networking, the scaling, all of it.
The audience reacted like this was a big deal. Because it is.
But here is the thing that made me smile a little when I saw the announcement.
My CTO had identified this exact gap in May. He looked at the Snowflake ecosystem, saw that real-time streaming infrastructure was missing, and pushed his team to figure it out before anyone else had. Not because we needed to race Snowflake, but because solving hard, uncharted problems is how you actually learn, and how you build the kind of expertise that is hard to fake.
When Snowflake announced Data Stream, it was not a surprise to us. It was a confirmation. The problem was real. The demand was real. And we had already been living inside it for weeks.
That is what good technical leadership looks like: being curious about the right problems before the market has already moved on.
A Quick Note Before We Go Further
I did put together proper architecture diagrams for this series. They make the concepts in Parts 2 and 3 a lot easier to follow visually, and I am genuinely proud of how they turned out.
Unfortunately, I am running into a subscription issue right now that is blocking me from adding images to these articles. I did not want that to hold up publishing, because waiting means being late to a conversation that is already starting to happen in the community. So I am getting these articles out now, and I will update them with the diagrams as soon as the issue is sorted.
Think of this as version 1.0. The writing stands on its own, and the diagrams will make it even better when they arrive.
What This Series Covers
This is Part 1 of 4. Here is the road map for what comes next.
Part 2 is the story of the first demo I built. And I will be honest with you upfront: I built it wrong. Not completely wrong, but wrong in a specific way that JP was right to point out. The producer ended up inside SPCS instead of outside it, which technically worked but did not solve the real problem. That demo still taught me a lot, including a very important lesson about building Docker images on Apple Silicon Macs that I will not let you leave that article without knowing.
Part 3 is where the real solution lives. This is the technical deep dive: two stacked problems, three architectural options I considered, the one I chose and why, and the moment the whole thing actually worked end to end. If you are an engineer and you came for the substance, Part 3 is your part.
Part 4 is the reflection. How does what I built compare to Snowflake's Data Stream? What does this mean for engineers building streaming pipelines on Snowflake going forward? And a genuine thank-you to the person who pushed me into this problem in the first place.
See you in Part 2.
About Authors
Author: Shrinivas Vishnupurikar [ Snowflake Data Engineer at ArisData ]
Co-author: JayaPrakash (JP) Nellore [ Data and Analytics Principal & Co-Founder at ArisData ]
Co-author: Swami Addagalla [ Co-Founder at ArisData ]
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