The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 9 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Narkhede: Central streaming platforms like Kafka replace legacy enterprise service buses
“Fundamentally, companies want to collect all sorts of data, and there isn't just a database and a warehouse anymore. There are lots and lots of distributed systems, which means that we need to move to a sort of platform-centric approach, and this will, this is…”
Narkhede: Stream processing behaves like microservices, not faster MapReduce jobs
“What we learned is that, ah, stream processing is in fact much more than a faster MapReduce layer. It, in fact, most of the applications that do stream processing look much more like a microservice of an application, and less like a faster version of a batch o…”
Neha Narkhede: All enterprise data can be represented as event streams
“My bold claim here is that all your data can be represented as event streams.”
Narkhede: Non-Kafka stream processing systems are complex and limited to niche problems
“Kafka Streams is, Extremely powerful, but very simple because it builds on top of primitives in Kafka. A lot of other systems are powerful, but, ah, not so simple. They're only applicable to a niche set of problems.”
Narkhede: Companies are shifting from batch processing to real-time data
“A lot of companies are making a fundamental shift towards leveraging data in real-time, and moving away from that style computing Ah, which is essentially once a day data processing.”
Neha Narkhede: Stream processing generalizes request-response and batch paradigms
“Stream processing is often thought about as something that is just real time in nature, but it is really a generalization of these two extremes, request, response, and batch.”
Narkhede: Banks are leading non-tech enterprise adoption of Apache Kafka
“In fact, banks is are definitely leading the way in terms of putting Kafka To you know, very sort of ambitious applications.”
Narkhede: Apache Kafka powers over 1.2 trillion written messages daily at LinkedIn
“You know, Kafka powers more than, ah, 1.2 trillion messages, ah, written per day. It is, ah, it powers more than 3.4 trillion, ah, messages, ah, read per day. All that amounts to more than one petabyte of streaming data. And that is across thousands of produce…”
Narkhede: Apache Kafka is used by thousands of companies worldwide
“Since we open sourced it, you know, roughly five years ago, Kafka is used in thousands of companies worldwide, from Uber, and LinkedIn, and Netflix, all the way to traditional enterprises like eBay, and PayPal, and Cisco, and Goldman Sachs.”