Hadoop for. NET Developers, hadoopdevelopers

Hadoop for. NET Developers, hadoopdevelopers
Hadoop for. NET Developers (1): Understanding Hadoop

Over the years, big data has become an exciting source for the analysis industry. For the purpose of this blog series, I will loose define this term to focus on the expansion of data sources from the core business systems of data-the scope of traditional business intelligence-including new (either historically ignored or new) available) data source.

This is a rough simplification of big data, but it is inherent with the challenges that have been pushed through the new data platform, these new data work. When we focus on Hadoop, the most widely recognized, such a limited definition of big data as these new data platforms seems to be enough.

Hadoop is an elastic, distributed, and non-pattern data processing platform. It is ideal for you to have a large number of datasets. A single record contains a small value, such as log files, because it provides a low investment for data access solutions. This is also a good platform for complex analysis and interpretation of complex data, such as XML or JSON documents, image files, and/or variable interpretations, for example, the customer pushes the text (in the JSON file ).

In addition, Hadoop is an excellent platform when you need large-scale scalability beyond the ability to implement relational database platforms. Even so, I did not find this final solution that is applicable to many of my clients (although it is applicable to some ). For me and most people, the flexibility and economics of Hadoop are often the most convincing reason to explore this platform.

Hadoop for. NET Developers (2): Infrastructure

Hadoop is the implementation of a group of associated project components. The Core Component is MapReduce, which is used to process job execution and a storage layer. It is usually implemented as a Hadoop Distributed File System (HDFS ). For the purpose of this article, we will assume that HDFS is in use.

Hadoop components are implemented through a series of servers called data (or computing) nodes. These nodes are where data is stored and processed.

The node server of the name is retained in the environment, and its data is stored on the track of the node, and provides the data node as a single entity. This singular representation is called a cluster. If you are familiar with RDBMS implementation term clusters, please note that there is not necessarily any shared storage or other resources between nodes. Hadoop clusters are purely logical.

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