Engineers are responsible for truly understand how their designs work. What do you do when the real-world doesn't match the simulation? As they say in the Army, if the map doesn't match the terrain, trust the terrain.. Therefore, you should understand the analysis, even if the arithmetic was done by a computer. If you don't know how to do at least a reasonable approximation by hand, then how can you trust the result of the program? Learn the fundamental of Digital Logic Design as follow:
To understand Hadoop, you have to understand two fundamental things about it. Imagine you had a file that was larger than your PC's capacity. You could not store that file, right? Hadoop lets you store files bigger than what can be stored on one particular node or server. So you can store very, very large files. It also lets you store many, many files.Mainstream business users don't need to know how Hadoop works.But they do need to understand that the constraints they once had on storing and processing data are removed when Hadoop is installed.the business can start thinking big again when it comes to data.There is less confusion than there was 12 months ago. Executives just know that it is a big data technology, and that is enough for them.
The second characteristic of Hadoop is its ability to process that data, or at least (provide) a framework for processing that data. That's called MapReduce. Moving data over a network can be very, very slow, especially for really large data sets. Imagine if you're opening a really, really big file on your laptop, it takes a long, long time. It takes much longer than if it's a short, tiny file.
Apache™ Hadoop® is an open source software project that enables distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with very high degree of fault tolerance. Rather than relying on high-end hardware, the resiliency of these clusters comes from the software's ability to detect and handle failures at the application layer.
Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative.
Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper.
The Hadoop framework is used by major players including Google, Yahoo and IBM, largely for applications involving search engines and advertising. The preferred operating systems are Windows and Linux but Hadoop can also work with BSD and OS X.
Hadoop Video Tutorial from http://onlinetrainingcources.blogspot.in/
ARM is eyeing on building new operating system, software components, tools and ecosystem that will make creation and deployment of commercial, standards-based Internet of Things devices is possible at scale. The ARM® mbed™ IoT Device Platform has been introduced to make this vision a reality. The design principles of this ARM based platform focus on connectivity, efficiency, security and productivity. For this reason, mbed OS, mbed Device Server and mbed Tools were built. To have insight of this platform, below are video tutorials on mbed microcontroller by Bme Builds:
