Tag Archives: kafka

Analytics 2.0 goes live at Percolate

Today sees the launch of Analytics 2.0 on the Percolate platform. After 12 months of hard work by my team, I am very proud of the new platform.

1 year ago the San Francisco team was tasked with rebuilding the Analytics system at Percolate. In place of our legacy MySQL-based system, we now have a brand new architecture, based on Apache Kafka and Elasticsearch. It’s more responsive, more flexible, and offers much richer functionality.

You can learn all about the new system on the Percolate blog.

Revisiting syslog-gollector

It’s been 18 months since the first commit to my first significant Go project — syslog-gollector. After an initial burst of activity to create a functional Syslog Collector that streamed to Apache Kafka, the source code hadn’t been updated much since. But today I received a report that it no longer built, so I spent some time porting the code to the latest Shopify Sarama framework.

It was amusing to see how naive much of my early Go code was.

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Drop, Throttle, or Buffer

Real-time — or near real-time — data pipelines are all the rage these days.  I’ve built one myself, and they are becoming key components of many SaaS platforms. SaaS Analytics, Operations, and Business Intelligence systems often involve moving large amounts of data, received over the public Internet, into complex backend systems. And managing the incoming flow of data to these pipelines is key.

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Call me Definitely

The creator of the network monitoring system Riemann, Kyle Kingsbury, has put together a comprehensive series of blog posts, on the fault-tolerance, high-availability, and general correctness of number of database and storage technologies. Of the technologies discussed I am most familiar with — elasticsearch and Apache Kafka — I found the posts to be a great read.

If you haven’t read them yet, you should check them out on his site.

Infrastructure at Scale: Apache Kafka, Twitter Storm and elasticsearch

storm_logoAWS have posted the video online of Jim Nisbet’s and my talk at AWS:reinvent 2013. In it, Jim and I describe the system we built at Loggly, which uses Apache Kafka, Twitter Storm, and elasticseach, to build a high-performance log aggregation and analytics SaaS solution, running on AWS EC2.

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Speaking at AWS re:Invent 2013

amazon.com_web_servicesThis past week I had the opportunity to speak, with my colleague Jim Nisbet, at AWS re:Invent 2013. Titled “Unmeltable Infrastructure at Scale: Using Apache Kafka, Twitter Storm, and Elastic Search on AWS“, Jim and I described the architecture of Loggly’s next-generation log aggregation and analytics Infrastructure, which went live 3 months ago, and runs on AWS EC2.

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Monitoring Storm Kafka Spouts using Python

kafka-logo-tallWhen running a large real-time processing system, monitoring is critical. But it does more than allow you to keep an eye on your system. During development it allows you test hypotheses about how it works, how it performs when certain parameters are changed, and takes the guessing out of working with dynamic systems.

Storm, a real-time computational framework open-sourced by Twitter, is such a system and comes with a Spout, allowing messages to be streamed from a Kafka Broker.

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