Contributor: Michael Hausenblas

MapR Converge Blog author, Michael Hausenblas

Michael Hausenblas was Chief Data Engineer for EMEA at MapR, he helped people tap the potential of Big Data by bridging the technical (architecture, scalability, etc.) and the business side (RoI, TCO, etc.). His background is in large-scale data integration, the Internet of Things, and Web applications and he's experienced in advocacy and standardization (World Wide Web Consortium).

When not solving complex Big Data problems, Michael enjoys spending time with his three kids, reading books and, less frequently, practicing Shotokan Katas.

Blog Posts by Michael Hausenblas

January 19, 2015 | By Michael Hausenblas

Key Requirements for an IoT Data Platform

In this series of blog posts on the Internet of Things (IoT), we've initially established why IoT naturally lends itself to big data, reviewed the current IoT landscape and had a look at some IoT use cases (smart cities, smart phones, and smart homes...

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January 14, 2015 | By Michael Hausenblas

Home Smart Home and the Internet of Things

A recent study found that by 2019, some 69% of consumers will own an in-home IoT device and companies all over the place have identified the opportunities around the so called smart home. For example, Samsung's CEO announced at the Consumer Electronics...

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November 18, 2014 | By Michael Hausenblas

Smart Phones and the Internet of Things

In this installment of the IoT blog post series we take a closer look at a sometimes underestimated IoT device: smart phones. A modern smartphone is equipped with up to 10 sensors, able to capture anything from location to the device orientation to light...

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November 11, 2014 | By Michael Hausenblas

Smart Cities Use Cases: Interview with Mingding Han of Singapore’s A*STAR

After looking at the IoT landscape in our last blog, I thought it would be meaningful to speak with someone with real experience. In this blog we asked an expert in the Community IoT domain about use cases and emerging developments concerning smart cities...

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November 04, 2014 | By Michael Hausenblas

The Internet of Things (IoT) Landscape

In the first installment of the IoT blog series we established what the Internet of Things means for businesses and that it lends itself to big data. This time we will have a closer look at the IoT landscape, that is, IoT software and hardware provider...

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August 15, 2014 | By Michael Hausenblas

What is the Internet of Things and Why Does it Matter to Big Data?

This is the first in a series of articles around the topic of the Internet of Thingsand its relevance to big data. We will discuss challenges and opportunities, review real-world use cases and how people are benefiting from applying big data technologies...

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April 25, 2014 | By Michael Hausenblas

Serving Both Operational and Analytical Systems

The recent release of Apache Hive 0.13 prompted me to think about how far we are concerning serving both operational and analytical systems in the context of the Hadoop ecosystem. So, let's step back a bit. The MapReduce paradigm, still considered...

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March 25, 2014 | By Michael Hausenblas

Data Processing Vocabulary 101: Key Terms That You Need to Know

Big Data. Everyone from your Uncle Bill to your local mail carrier has heard the term in some form or another. However, the plethora of big data media coverage brings with it a lot of terminology that can be confusing, especially when it comes to data...

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January 06, 2014 | By Michael Hausenblas

SQL-in-Hadoop: with MapR You Can Have your Cake and Eat it Too

At the end of last year my colleague Steve Wooledge discussed options you have at your disposal for querying both schema-based or self-describing structured datasources with the MapR Big Data platform. Around that time I also reviewed Open Source SQL...

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April 30, 2013 | By Michael Hausenblas

Polyglot Persistence Shapes Big Data Solutions

In 2011 Martin Fowler coined the term Polyglot Persistence, suggesting in a nutshell: ... any decent sized enterprise will have a variety of different data storage technologies for different kinds of data. There will still be large amounts of it managed...

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