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Editor's Notes

• #2: TODO: fix title Introduce self What is Stream Processing Brief intro to Kafka Kafka Streams
• #3: Exciting! Important!
• #4: Doesn’t mean you drop everything on the floor if anything slows down Streaming algorithms—online space Can compute median
• #5: About how inputs are translated into outputs (very fundamental)
• #6: HTTP/REST All databases Run all the time Each request totally independent—No real ordering Can fail individual requests if you want Very simple! About the future!
• #7: “Ed, the MapReduce job never finishes if you watch it like that” Job kicks off at a certain time Cron! Processes all the input, produces all the input Data is usually static Hadoop! DWH, JCL Archaic but powerful. Can do analytics! Compex algorithms! Also can be really efficient! Inherently high latency
• #8: Generalizes request/response and batch. Program takes some inputs and produces some outputs Could be all inputs Could be one at a time Runs continuously forever!
• #9: Companies == streams What a retail store do Streams Retail - Sales - Shipments and logistics - Pricing - Re-ordering - Analytics - Fraud and theft
• #10: Quick run-through of the features in Kafka.
• #11: Logs Distributed Fault-tolerant
• #12: Change to Logs Unify Batch and stream processing
• #15: Can’t just scale storage, need to scale processing Important: order
• #16: Streaming platform is the successor to messaging Stream processing is how you build asynchronous services. That is going to be the key to solving my pipeline sprawl problem. Instead of having N^2 different pipelines, one for each pair of systems I am going to have a central place that hosts all these event streams—the streaming platform. This is a central way that all these systems and applications can plug in to get the streams they need. So I can capture streams from databases, and feed them into DWH, Hadoop, monitoring and analytics systems. They key advantage is that there is a single integration point for each thing that wants data. Now obviously to make this work I’m going to need to ensure I have met the reliability, scalability, and latency guarantees for each of these systems.
• #17: Current state
• #23: OpenGL Triangle
• #29: Add screenshot example
• #30: Add screenshot example
• #32: TODO: Summarize
• #33: Change to “Logs make reprocessing easy”
• #34: Time is hard Need a model of time Request/Response ignores the issue, you just set an aggressive timeout Batch solves the issue usually by just freezing all data for the day Stream processing needs to actually address the issue
• #39: Kafka Streams: Manage the set of live processors and route data to them Uses Kafka’s group management facility External framework Start and restart processes Package processes Deploy code
• #40: DBs handle tables Stream Processors handle streams
• #41: Companies == streams What a retail store do Streams Retail - Sales - Shipments and logistics - Pricing - Re-ordering - Analytics - Fraud and theft
• #43: But…no notion of time
• #62: Also: Other talks Kafka Summit Streaming data hackathon Stop by the Confluent booth and ask your questions about Kafka or stream processing Get a Kafka t-shirt and sticker. We’re also giving away a few books: the early release of Kafka: The Definitive Guide, Making Sense of Stream Processing, and I Heart Logs Meet the authors and get your book signed. We also want to invite you to participate in the Stream Data Hackathon in San Francisco on the evening of April 25, the day before Kafka Summit You might be interested in some of the other Confluent talks. If you missed it you’ll have access to the video recording.