Hadoop is an open-source software framework for storing and processing big data.
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Hadoop parallelizes data processing across many nodes (computers) in a compute cluster, speeding up large computations and hiding I/O latency through increased concurrency. Hadoop is especially well-suited to large data processing tasks (like searching and indexing) because it can leverage its distributed file system to cheaply and reliably replicate chunks of data to nodes in the cluster, making data available locally on the machine that is processing it.
Hadoop is written in Java. Hadoop programs can be written using a small API in Java or Python. Hadoop is a rapidly evolving ecosystem of components for implementing the Google MapReduce algorithms in a scalable fashion on commodity hardware. Hadoop enables users to store and process large volumes of data and analyze it in ways not previously possible with less scalable solutions or standard SQL-based approaches.
What you will learn
As an evolving technology solution, Hadoop design considerations are new to most users and not common knowledge. As part of the Dell | Hadoop solution, Dell has developed a series of best practices and architectural considerations to use when designing and implementing Hadoop solutions.
Hadoop can also run binaries and shell scripts on nodes in the cluster provided that they conform to a particular convention for string input/output. As with many other types of information technology (IT) solutions, change management and systems monitoring are a primary consideration within Hadoop. The IT operations team needs to ensure tools are in place to properly track and implement changes, and notify staff when unexpected events occur within the Hadoop environment.