There’s a line in Alice’s Adventures in Wonderland that says, “It's no use going back to yesterday, because I was a different person then.” Some days, I feel that way about my storage infrastructures as well. I fell down a particular rabbit-hole into storage management accidentally almost 20 years ago. The typical application I supported was a database platform. With a monolithic app running on one very large server, we obviously used one very large storage array that, while slightly more modular, still had its configuration limitations.
According to Gartner, by 2020, a quarter of a billion connected cars will form a major element of the Internet of Things. Connected vehicles are projected to generate 25GB of data per hour, which can be analyzed to provide real-time monitoring and apps, and will lead to new concepts of mobility and vehicle usage.
In this blog, we describe how to use the Kafka REST Proxy to publish and consume messages to/from MapR Streams. The REST Proxy is a great addition to the MapR Converged Data Platform, allowing any programming language to use MapR Streams. The Kafka REST Proxy, provided with the MapR Streams tools, can be used with MapR Streams (default) as well as Apache Kafka (in a hybrid mode). In this article, we will focus on MapR Streams.
In this week's Whiteboard Walkthrough, Jack Norris, Senior Vice President of Data and Applications at MapR, explains how the MapR Converged Data Platform opens up the use of containers to the big data environment such that you can access data directly, thus taking advantage of otherwise under utilized assets.
Debugging a real-life distributed application can be a pretty daunting task. Most common Google searches don't turn out to be very useful, at least at first. In this blog post, I will give a fairly detailed account of how we managed to accelerate by almost 10x an Apache Kafka/Spark Streaming/Apache Ignite application and turn a development prototype into a useful, stable streaming application that eventually exceeded the performance goals set for the application.
If you want to try out the MapR Converged Data Platform to see its unique big data capabilities but don’t have a cluster of hardware immediately available, you still have a few other options. For example, you can spin up a MapR cluster in the cloud using multiple node instances on one of our IaaS partners (Amazon, Azure, etc.).
In the past year, the big data pendulum for financial services has officially swung from passing fad or experiment to large deployments. That puts a somewhat different slant on big data trends when compared to 2016 trends. The question of big data hype versus reality has finally been put to rest for banks.
A lot of people choose MapR as their core platform for processing and storing big data because of its advantages for speed and performance. MapR consistently performs faster than any other big data platform for all kinds of applications, including Hadoop, distributed file I/O, NoSQL data storage, and data streaming. In this post, I’m focusing on the latter to provide some perspective on how much better/faster/cheaper MapR Streams can be compared to Apache Kafka as a data streaming technology.
This series of blog posts details my findings as I bring to production a fully modern take on Complex Event Processing, or CEP for short. In many applications, ranging from financials to retail and IoT applications, there is tremendous value in automating tasks that require to take action in real time. Putting aside the IT system and frameworks that would support this capability, this is clearly a useful capability.
This post is intended as a detailed account of a project I have made to integrate an OSS business rules engine with a modern stream messaging system in the Kafka style. The goal of the project, better known as Complex Event Processing (CEP), is to enable real-time decisions on streaming data, such as in IoT use cases.
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