Will Ochandarena is Director of Product Management at MapR, responsible for user experience and cloud. Prior to MapR, Will spent some time in the SeaMicro group at AMD, responsible for networking and cloud strategy, and before that was a product manager for the Nexus family of data center switches at Cisco. Will has an engineering degree from Rensselaer Polytechnic Institute, and and MBA from Santa Clara University.
The first Kafka Summit was recently held in San Francisco. While the size of the conference was relatively small at 600 attendees, it was encouraging to see the variety of companies that are embracing real-time data pipelines.
Two blogs came out recently that share some very interesting perspectives on the blurring lines between architectures and implementation of different data services, ranging from file systems to databases to publish/subscribe streaming services.
In this week's Whiteboard Walkthrough, Will Ochandarena, Director of Product Management at MapR, explains how we are able to build the MapR Streams capabilities that differentiate us from similar products in the market.
Over the last 5 years of shipping product we’ve watched our customers get enormous value out of storing and processing big data. The use cases are far and wide, from performing predictive maintenance on oil rigs to building fraud and risk models on financial transactions.
In this blog post, I’ll share how we see Myriad delivering value to customers, and how it fits in with the MapR platform.
Lately I’ve talked to lots of people who are just getting their heads wrapped around the value of big data software such as Apache Hadoop, but are getting stuck figuring out the details. What kind of servers do I need to buy? What services do I need to install to make a “data lake”? How do I make sure I install the services in a way that makes them highly available, while being optimized for performance?
I often get asked, “What is the easiest way to get hands-on experience with MapR?” The best way is to try the MapR Sandbox, a single-node MapR cluster that you can run on your laptop. However, Hadoop clusters are never built with just one server, and some MapR features require multiple nodes, or even multiple clusters. To get hands-on with a MapR installation that more closely resembles what you might deploy on hardware, I suggest you deploy a MapR cluster in the Amazon cloud, using the MapR Installer. This blog post will walk you through that process.
Last time you bought a smartphone, what factors did you consider? You probably first evaluated the phone itself, like how well the camera could capture your kid’s special moments, or if there is enough storage to hold the full Rolling Stones collection in lossless format. You then looked at whether the services you already use and trust are supported, like the Netflix app for binging on House of Cards, or your bank’s app for catching up on bills at the end of the month. In the end, you chose the phone that had both the features and compatibility you needed.
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