Apache Kafka, every mention
65 scenes · ← back to Apache Kafka
tap a year for its mentions
every year anyone Matt Turck 35Neha Narkhede 32Jon Hyman 10Ben Johnson 5Ajay Kulkarni 5Aaron Katz 5DeVaris Brown 4Zach Sherman 3Sridhar Ramaswamy 3Nick Rockwell 3
Verbatim, from the transcripts: the passages where Apache Kafka comes up
Snowflake CEO on Winning the AI Arms Race
- ▶ 20:39 Sridhar Ramaswamy The way people typically use Snowflake is they would do, they would get data from source systems, whether it's a database or one of the apps you have on your phone that talks to a server that then writes a Kafka queue, a set of messages,
- ▶ 1:10:52 Sridhar Ramaswamy The AI backbone, we are continuing to explore, uh, the area and, um, we are in a pretty good place with respect to what do we support with respect to real time and ingestion and scale and stuff like that, but it, it is through streaming… 2 times in the scene
Trino, Iceberg and the Battle for the Lakehouse | Justin Borgman, CEO, Starburst
- ▶ 34:24 Matt Turck So, meaning that, uh, you just have, what, uh, Kafka endpoint, uh, ingesture? 3 times in the scene
Understanding Data Engineering in 2025 | Ben Rogojan, Seattle Data Guy
- ▶ 20:58 Matt Turck Spark or Kafka or like any of those frameworks, what would you recommend next once I have my language, I have my SQL, uh, what do I do next? 3 times in the scene
AI at Datadog: Monitoring machines in the age of LLMs | Olivier Pomel, CEO of Datadog
- ▶ 55:59 Olivier Pomel Like, we use a lot of Kafka, for example, that works well, and we still use a lot of Kafka.
AI at ZoomInfo: Superpowering GTM teams | Ali Dasdan, CTO, ZoomInfo
- ▶ 22:36 Ali Dasdan I mean, in the past, when we were doing data platform, there was no Kafka, for example, we created a Kafka-like system ourselves, but 2 times in the scene
How ClickHouse powers Netflix, Uber and Spotify’s Analytics | Aaron Katz, CEO of ClickHouse
- ▶ 5:41 Matt Turck Uh, so, you know, real time is really this area, um, that, um, for the last, you know, 1015 years, everybody has been saying, well, the world is moving into, uh, real time, and, you know, bit by bit, it's gotten there, but it's felt much…
- ▶ 20:47 Aaron Katz So the integrations that we do with Kafka or Confluent or Red Panda. 2 times in the scene
- ▶ 23:02 Aaron Katz We talked about the ingestion layer, things like Kafka open source or Confluent Cloud or Confluent Enterprise, uh, their on-prem, uh, product, Red Panda, we're seeing emerge, um, as a data ingestion format that people are excited about.
Build Fast APIs Faster Over Data at Scale | Tinybird Founder & CEO Jorge Gomez Sancha
- ▶ 5:42 Jorge Gomez Sancha So of pretty much a lot of the teams that we see, you have maybe Kafka capturing data, data going into one data, one of those data warehouses or other, then some tooling to, you know, model your data, uh, according to your business needs.
- ▶ 8:34 Jorge Gomez Sancha Earlier, our, my teammates are sending events to Tiny Bird, so the first thing we are going to do is, uh, in just those events, they're coming via Kafka.
- ▶ 19:22 Jorge Gomez Sancha This is the data source and the connection is with Kafka.
Fundamentals of Data Engineering | Joe Reis and Matt Housley
- ▶ 2:40 Matt Housley Yeah, exactly, and, and I'll kind of skip a bullet point and then go back, but like this one right here, what we kept hearing a lot is that, you know, data engineering is Spark, or data engineering is Kafka.
Fireside Chat: Aaron Katz (Co-Founder & CEO, ClickHouse) with Matt Turck (Partner, FirstMark)
- ▶ 3:15 Aaron Katz So obviously things like Kafka and DBT and Kinesis,
- ▶ 20:51 Aaron Katz We talked a little bit about, you know, ingestion and we're going to be investing heavily to make getting data from Kafka to ClickHouse or Kinesis or an integration on
Top 10 Trends in AI, Machine Learning and Data for 2022
- ▶ 16:43 Matt Turck Um, so Confluent, uh, which is, uh, the company behind Kafka,
Fireside Chat: DeVaris Brown (Founder & CEO, Meroxa) with Matt Turck (Partner, FirstMark)
- ▶ 9:25 DeVaris Brown Like Kafka's existed forever. 2 times in the scene
- ▶ 23:54 DeVaris Brown Uh, I had to go look it up, but, uh, it, it essentially gives you the ability to introspect streams, and it's super, super granular, super detailed, and it gives you, you know, better, better information than what you would get natively… 2 times in the scene
Fireside Chat: Dave Burgess (Head of Data Engineering, Pinterest) w/ Matt Turck (Partner, FirstMark)
- ▶ 2:38 Dave Burgess Things like Kafka eventually came out of LinkedIn that was based on work that we had been doing at Yahoo and many other things had come.
- ▶ 8:35 Dave Burgess And the way that we get all this data is using Kafka. 2 times in the scene
- ▶ 17:34 Matt Turck So you mentioned Kafka. 4 times in the scene
- ▶ 29:06 Matt Turck Can you please speak more to use of Kafka and airflow? 3 times in the scene
Fireside Chat: Bindu Reddy (Founder & CEO, Abacus.AI) with Matt Turck (Partner, FirstMark)
- ▶ 11:56 Bindu Reddy Uh, we, uh, we, um, uh, use, uh, some version of, uh, Kafka internally.
Fireside Chat: Arjun Narayan (Founder & CEO, Materialize) with Matt Turck (Partner, FirstMark)
- ▶ 2:26 Arjun Narayan Kafka being, Apache Kafka being the most, uh, 2 times in the scene
- ▶ 2:37 Matt Turck Jumping into this, do you want to talk about Kafka and, like, explain for, like, the non-technical part of this group? 8 times in the scene
- ▶ 15:34 Arjun Narayan The integrations to tools like Kafka, uh, the integrations to, to, to, to pull batch data from S three.
Fireside Chat: Jack Hanlon (VP Data, Reddit) with Matt Turck (Partner, FirstMark)
- ▶ 10:15 Jack Hanlon Pretty heavy, pretty heavy Kafka shop, pretty heavy Kubernetes shop, um, and, uh, and we're,
- ▶ 17:00 Jack Hanlon So, you know, I think we're ending up leaning heavily into being a Kafka shop, building more of the stuff into base plate, into more of these core services, um, and thinking about then, yeah,
Data Observability and Pipelines: OpenLineage and Marquez
- ▶ 18:12 Julien Le Dem Your, um, data infrastructure, and you have an injection, and then you add a storage layer for streaming and for batch processing using things like Kafka or Sree or HDFS, and then you would have stream and batch processing, and usually you…
Fireside Chat: Mike Volpi, General Partner, Index Ventures (FirstMark's Data Driven NYC)
- ▶ 27:39 Mike Volpi Uh, um, uh, Jay Kreps, who started Confluent and was the original author of Kafka, he wrote Kafka to produce the LinkedIn feed. 2 times in the scene
Apache Druid & An Introduction to Data Rivers // FJ Yang, Imply (FirstMark's Data Driven NYC)
- ▶ 1:12 FJ Yang And, ah, how many people have worked with what kind of different types of big data technologies like Hadoop, Spark, and Kafka, and others?
- ▶ 9:36 FJ Yang Uh, so if you're familiar with different types of data technologies, uh, an example of a stream hub might be something like Apache Kafka.
- ▶ 11:27 FJ Yang Uh, oftentimes it's in a stream, so it might be coming from Kafka, it might be coming from a stream processor.
Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
- ▶ 28:05 Solmaz Shahalizadeh We run them in shadow mode, which means, like, they do all the evaluation, but the results are logged to Kafka, and we see if the time to response, if the sort of distribution of predictions are different or not.
Optionality in Data Architecture // Justin Borgman, Starburst Data (FirstMark's Data Driven NYC)
- ▶ 2:55 Justin Borgman Um, this is, uh, maybe a little hard for some of you guys to see, uh, but it shows at the top, uh, a variety of popular BI tools that you can connect to using ODBC or JDBC drivers, and on the bottom, a variety of data sources, and you'll…
- ▶ 11:02 Justin Borgman Um, you might be looking at, you know, a whole host of other data sources, like Kafka, or MongoDB, or some new technology X.
Fireside Chat: Nick Rockwell, CTO of The New York Times (FirstMark's Data Driven NYC)
- ▶ 16:55 Nick Rockwell Um, we did a big Kafka implementation as well, which just runs our main publishing pipeline, so all content flows through Kafka on its way to the front end. 3 times in the scene
A New Kind of Logging System // Zach Sherman & Ben Johnson, Timber (FirstMark's Data Driven NYC)
- ▶ 2:38 Ben Johnson So we had a Kafka stream set up and then we just pulled batches of data off that Kafka 2 times in the scene
- ▶ 5:30 Ben Johnson Uh, you know, things like Kafka have really kind of, uh, inverted the industry.
- ▶ 8:55 Zach Sherman and then you have the data plane, and, and this is really like Kafka.
- ▶ 9:32 Zach Sherman So just like the old diagram, you see data still comes in from all of our clients, gets put on a, put on a Kafka or Kinesis stream.
- ▶ 10:25 Zach Sherman So, um, your team doesn't have to basically build this entire Kafka pipeline purpose, you know, like purposed around log data and metric data.
- ▶ 16:48 Ben Johnson So for example, um, we fully expect the, the utility we built to be used with the Kafka stream, which does offer certain guarantees around durability and making sure that data is processed, uh, efficiently, uh, that you don't lose the… 2 times in the scene
Ultra-Resilient SQL for Global Business // Spencer Kimball, Cockroach (FirstMark's Data Driven NYC)
- ▶ 3:25 Spencer Kimball So, part of the problem is there's more and more data, part of the problem is these systems are more complex and intertwined, it's not just your system of records, it's your Kafka queues feeding other systems, and, ah, lots of different…
3 Heretical Ideas on the Future of Data // Ajay Kulkarni, TimescaleDB (FirstMark's Data Driven NYC)
- ▶ 20:36 Ajay Kulkarni I think we're totally complementary to, say, message buses like Kafka. 5 times in the scene
Data Pipelines at Braze // Jon Hyman, Braze (FirstMark's Data Driven)
- ▶ 6:28 Jon Hyman They're the company that essentially now builds, maintains, and supports Kafka. 7 times in the scene
- ▶ 12:00 Jon Hyman We have our application that is logging data at different decision points over to Kafka, and then we use Kafka Connect in order to just zip that right over to Elasticsearch. 2 times in the scene
- ▶ 14:00 Jon Hyman It's tens of billions of messages into Kafka.
- ▶ 15:11 Matt Turck So using Kafka and Elastic, that's awesome if you have a really strong tech team. 3 times in the scene
Where Should Machines Go to Learn? // Auren Hoffman, SafeGraph (FirstMark's Data Driven)
- ▶ 3:41 Auren Hoffman You've got some sort of, like, Spark infrastructure streaming the data in with Kafka.
The Power of GPU Analytics // Todd Mostak, MapD (FirstMark's Data Driven)
- ▶ 3:57 Todd Mostak One of our clients is one of the largest social media companies in the world, and we actually pull in streaming data via Kafka.
The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)
- ▶ 10:21 Praveen Murugesan Kafka was, in general, like, a nice way where people used to pipe the results of, like, their Spark jobs. 2 times in the scene
A Kafka-Powered Real-Time Streaming Platform // Neha Narkhede, Confluent [FirstMark's Data Driven]
- ▶ 0:11 Neha Narkhede Um, two years ago, um, I, along with other creators of Apache Kafka, we created Confluent. 3 times in the scene
- ▶ 1:26 Neha Narkhede I'm going to start by talking about the motivation behind streaming data, talk about Apache Kafka for the minority of you who haven't heard about it, and then go about explaining how Kafka can become a streaming platform which operates at…
- ▶ 7:38 Neha Narkhede The first step is really Apache Kafka. 14 times in the scene
- ▶ 10:38 Neha Narkhede Uh, any infrastructure is only as useful as data that it has, and so, uh, the first step of adopting Kafka is really thinking about how to make it useful. 5 times in the scene
- ▶ 14:53 Neha Narkhede Um, Kafka Streams is really the, the layer in Kafka that allows you to do stream processing. 3 times in the scene
- ▶ 15:37 Neha Narkhede So, you know, this is the reality of, um, Kafka as a streaming platform at LinkedIn. 6 times in the scene
- ▶ 17:46 Matt Turck Are you the services version for Kafka? 8 times in the scene
A Fireside Chat with MapR CTO M.C. Srivas (Data Driven NYC / FirstMark)
- ▶ 19:25 Matt Turck And Stream is an alternative to Kafka, which is an open source, um, uh, paradigm.
- ▶ 20:45 M.C. Srivas So Kafka is an example there.
Machine Learning in Production with Josh Bloom, Co-founder Wise.io
- ▶ 3:54 Josh Bloom So it would be very easy, of course, if you've got a good group of people to stand up your own Kafka cluster or manage some multi-zone, uh, Postgres server.
Joseph Essas, OpenTable // Mining Diner Talk (Hosted by FirstMark Capital)
- ▶ 2:32 Joseph Essas Uh, all of our events flowing through Kafka, they've been populated into Cassandra, which then we run Spark instances that kind of model on top of the data.
Mike Olson, Cloudera // The Cloudera Story (Hosted by FirstMark Capital)
- ▶ 9:39 Mike Olson You need some ingest tools, so you need Scoop and Flume, and you might even be looking at Kafka right now.
Tobi Knaup, Mesosphere // Data Driven #29 // Sep 2014 (Hosted by FirstMark Capital)
- ▶ 14:35 Tobi Knaup Um, so they're running Kafka, and Cassandra, and Presto, which is, um, you know, a SQL query engine out of Facebook.