Atlas Stream Processing Explained in 3 minutes
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00:00:00Introduction to MongoDB Stream Processing
00:00:14Similarities Between Documents and Events
00:00:28Challenges with Fixed Schema Systems
00:00:50Benefits of MongoDB's Document Model
00:01:00Leveraging Atlas for Stream Processing
00:01:18High-Level Architecture Overview
00:01:27Integration with Kafka and Change Streams
00:01:50Atlas Streams Processing and Data Routing
00:02:07Kafka Connectivity and Data Landing
00:02:22Focus on Developer Productivity
The main theme of the video is the comparison between document databases and event streams, and how MongoDB Atlas facilitates stream processing with a focus on simplicity and integration with existing systems like Kafka.
🔑 Key Points
- Documents and events have similar structures, with IDs and timestamps.
- Event processing systems need to handle flexible schemas.
- MongoDB Atlas stream processing is designed around the document model.
- Atlas stream processing can integrate with external sources like Kafka.
- MongoDB stream processing emphasizes simplicity and developer productivity.
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Full Video Transcript
[Music] but at its very core a document and an event in an event stream are very very similar so take a look here we've got an ID on the left a document in the database and we've got a time stamp on the right in one case we've got a mutable data store and on the other side we have an immutable data stream otherwise things look pretty much the same if your event processing system does not have the capability to handle this kind of data meaning it has a fixed schema you're going to have a tough time you're either going to have to pre-process the data and force some sort of rigid schema before you put the data into the system it's typically fraught with peril stream processing is built around the document model it handles continuous processing of data it leverages Atlas so as you would expect robust processing in the cloud multiple data centers ability to scale things like that this is a high level architecture of how it looks this ton of a mental model for you to kind of think about things so we've got on the left sources like Kafka and obviously mongodb change streams so that makes sense right these chain streamers would be a primitive that we use right out of the gate it allows you to then aggregate filter route and process that data in Atlas streams and then ultimately write it to mongodb Via that continuous merge or back out to Kafka now I want to point out that that Kafka is not hosted on Atlas that is somebody else's Kafka whether it's self-managed on your side in your organization maybe it's confluent cloud or one of the other Cloud vendors so that's connecting to where your data already is in Kafka processing it and then Landing it in [ __ ] or reading it from [ __ ] processing it and Landing it out to those Kafka topics so one of the things that we're really passionate about is making sure the developer was productive from day one so this is the entire stream processor this isn't just like pseudocode that's the entire stream processor the thing I want to point out here is it doesn't take pages and pages of code it doesn't take scaffolding and and you know including other libraries and it's not messy it's just the aggregation framework it is simple [Music] foreign [Music]
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