Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects. Ted has been very active in mentoring new Apache projects and is currently serving as vice president of incubation for the Apache Software Foundation. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (later purchased by LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.
In this week’s Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, provides some pointers for building better machine learning models, including the advantages of data streams and microservices style design in the example of a credit card fraud detector, the need for metrics, and how reconstruction of data from an auto-encoder can serve as a figure of merit that helps identify good models.
In this week’s Whiteboard Walkthrough, Ted Dunning, Chief Application Architect at MapR, describes advantages of MapR Converged Data Platform and how they work in the cloud. With files, tables and streams engineered into the same technology, MapR has particular advantages for multi-tenancy in the cloud including common pathnames and common security.
In this Whiteboard Walkthrough, MapR Chief Application Architect, Ted Dunning, explains how special capabilities such as mirroring, bi-directional stream and table replication and control of data locality make MapR particularly effective in cloud computing, whether you use cloud-to-cloud clusters or a hybrid of cloud and on-premise. Ted also explains how cloud bursting is a useful strategy for elastic work loads.
In this week's Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors.
In this Whiteboard Walkthrough, MapR’s Chief Application Architect, Ted Dunning, explains the move from state to flow and shows how it works in a financial services example. Ted describes the revolution underway in moving from a traditional system with multiple programs built around a shared database to a new flow-based system that instead uses a shared state queue in the form of a message stream built with technology such as Apache Kafka or MapR Streams. This new architecture lets decisions be made locally and supports a micro-services style approach.
In this week’s Whiteboard Walkthrough Part II, Ted Dunning, Chief Application Architect at MapR, talks about the design freedom gained by adopting a micro-services architecture based on streaming data. When you move – one step at a time - from an old style architecture that suffers from too much dependence on a shared global state database to a stream-based flow architecture, the isolation between micro-services results in reduced strain on the original database, improved flexibility and often speed.
In this week’s Whiteboard Walkthrough Part I, Ted Dunning, Chief Application Architect at MapR, explains the key capabilities required of a streaming platform in the context of micro-services and the advantages they offer.
In this week's Whiteboard Walkthrough, Ted Dunning, Chief Application Architect at MapR, explains how long tail distribution distorts the appearance of data and how to detect it.
Ted Dunning, Chief Applications Architect for MapR, talks about some newer streaming algorithms such as t-digest and streaming k-means.
In this week's Whiteboard Walkthrough, Ted Dunning, Chief Application Architect at MapR, shows you how to find out what's popular tomorrow by using Zipf's Law.
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