Data Council Blog

Data Council Blog

A Day in the Life: What's it like Being an Engineer at Stripe?

Alyssa Frazee tells us about the unicorn data skills she's honed on the job.

One thing that Alyssa Frazee loves about her work at Stripe is that, like someone with traditional data science skills, she gets to build machine learning models. "Oh, the rapture," cries Alyssa the data scientist!
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Pushing Kafka to the Limit at Heroku

How Everyone's Favorite PaaS Operates Kafka at Scale

Scale presents unique challenges for engineers, particularly those at companies who have the largest number of users throwing off the most data exhaust, resulting in the fattest data pipelines with the gnarliest problems. For example, Heroku, arguably the most popular platform as a service (PaaS), who last year decided to offer Apache Kafka to their customers as a hosted service, quickly realized they would need to support a large number of distinct users, each with varying use cases. This put them on a challenging path to attempt to minimize the operational headaches that come inherently with running this kind of infrastructure at scale.
 
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28/03/17 14:05 | by Pete Soderling | in Data Science, Speaker Spotlight

Fighting Fraud in Cryptocurrency using Machine Learning

Coinbase is on the front-lines of discovering advanced cryptocurrency and payment fraud techniques. Hear about how they use machine learning to help them fight the war.

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Building a Column-Oriented, Distributed Data Store for Analytics - The Story of Druid

 

Druid is a modern data store built for analytics use-cases. As the volume of data has exploded, and companies have sought deeper insights from their data, ad-hoc analytics have become difficult as more data is buried in distributed systems like Hadoop & Spark. The query model for these systems can result in long latencies making them sub-optimal for interactive analytics applications.

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How to Build a Data Pipeline That Handles Hundreds of Different Inputs

How many different file formats does your ETL system need to parse? For many data pipelines, several well-defined formats will suffice. Things break, and at times require manual intervention, but not so often that a couple engineers can't keep tabs on the system and keep things running relatively smoothly.

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