Michael Hausenblas

Chief Data Engineer MapR

My digital ID is: http://mhausenblas.info/#i

Contact

Michael's ID at DERI Michael's ID at dogfood

My name is Michael Hausenblas and I work at MapR Technologies as Chief Data Engineer, where I enable our customers to do as-it-happens business by reducing the time from data to action.

MapR Technologies is a pre-IPO start-up and the leading provider of production Hadoop. We offer an enterprise-grade data platform—including Hadoop, Spark, Drill, Storm, etc.—supporting many mission-critical & production use cases at over 700 paying customers, worldwide.

Please use m.hausenblas@acm.org for any general enquiries.

Activities

Some of my active projects:

Mentoring data processing in the Internet of Things (IoT) through the iot-a.
Maintaining The Apache Spark Stack advocacy site.
Advocating the Lambda Architecture for building fault-tolerant large-scale data processing system.
Blogging about polyglot processing and data analytics economics.
Public speaking engagements include, but are not limited to: Hadoop Summit, Strata+Hadoop World, NoSQL matters, Thingmonk, and various local Hadoop & Big Data User Groups all over Europe.

More ...

Some of my past activities:

Contributing to the Apache Drill, where I helped building an interactive, distributed query engine and maintained drill-user.org.
Research and development in the realm of Internet of Things (IoT): HTTP and CoAP, Mobile Augmented Reality (position paper, demo), Live Linked Open Sensor Database.
I was General Chair of the European Data Forum 2013 (EDF).
Co-creator and maintainer of the 5 ★ Open Data site, explaining costs & benefits of Open Data incl. examples for each stage.
Developed the 'Rapid-Appraisal Crawler for Original Open data Nuggets' (Racoon).
Initiator and co-maintainer of enable-cors.org to advocate good practices around CORS.
Inventor of and contributor to the 'Vocabulary of Interlinked Datasets' (VoID) that allows to describe RDF-based datasets.
I was co-chairing the W3C RDB2RDF Working Group that standardised mappings of relational data into RDF.

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