Using Design Patterns to Build Flexible and Extensible Software

// 07.26.2016 // Data

Software design pattern is a general repeatable solution to a commonly occurring problem in software design. It provides a description and guideline to solve a problem that can be used in multiple different situations. Because development speed is increased when using a proven prototype, developers using design pattern templates can improve coding efficiency and final product readability. MediaMath’s Engineering team used design patterns to add flexibility, extensibility and reusability to components of a greenfield real-time sizing service for Data Management Platform (DMP). Advertisers use a DMP to store millions of data entries that they have on potential users they would like to […]

Cassandra War Stories: Part 2

// 07.19.2016 // Data

In this series we have been relating some adventures MediaMath has been having getting the NoSQL database Cassandra to work for our needs as we built out of our Data Management Platform service. As mentioned in our previous post we needed to do a fair amount of tuning in order to scale Cassandra to our workload.  In this post we’ll focus on some of the techniques we developed (good and bad) in order to handle the rapid increase in our data ingest.  Using a combination of freely available automation tools, building our own custom tooling and clever utilization of AWS […]

Cassandra War Stories: Part 1

// 05.17.2016 // Data

This is part one of a multi-part series exploring the successes (and scars) that we’ve had while tuning Cassandra to perform well in MediaMath’s Data Management Platform. Fast reads on time series data We use Cassandra as the backend data store for our Data Management Platform (DMP) system here at MediaMath. DMPs are used by advertisers to store their first party data as well as third party data segments they buy so that they can deploy these to bid on ad opportunities targeted audiences. This requires hardware that can handle extremely large volumes of data and then search them very quickly. We chose to […]

Video: Fast Distributed Online Classification and Clustering

// 05.05.2016 // Data Science

Thousands of software developers, full-stack engineers, consultants and systems architects flocked to Dublin, Ireland this last April for the 2016 Hadoop Summit. Hosted by Hortonworks – a major Apache Hadoop distributors – and Yahoo, the Hadoop Summit was home to 3 full days packed with Hadoop and big data innovations – straight from the elephant’s mouth. The first day featured our own Prasad Chalasani, SVP of Data Science at MediaMath, and his talk on Fast Distributed Online Classification and Clustering. He outlines how he and Ram Sriharsha at Databricks leveraged recent machine-learning research to develop a fast, practical, scalable, online, distributed, […]

Moving Past Infrastructure Limitations

// 04.20.2016 // Infrastructure

Here at MediaMath, we’re quite fond of data. It would be surprising to hear someone say they’re not fond of data, of course, but we’ve spent the last 18 months proving to ourselves and our clients that we really mean it. Our company is built around driving concrete, measurable results, and our clients – both internal and external – have sophisticated analytics teams that want access to the data we generate for their own analysis, owned marketing, budgeting, and more. In this post we will describe the journey from data warehouse to data platform and the success of ditching our […]

How We Used Docker to Lower Test Run Times from 1 Hour to 10 Minutes

When a service grows in size and complexity, we add more tests in order to maintain test coverage. Having proper test coverage allows us to change or add new features and be reasonably confident we didn’t break any existing features. This is especially important for “bidder”, the name of our real time bidding service, where even a small unexpected downtime or bug can have major consequences. Bidder interacts with ad exchanges through http requests to place bids on advertisement opportunities (webpages, mobile apps, etc.) for our advertisers. As bidder increased in features and handled more bid opportunities (millions of bid […]

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