Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
Why is Hadoop important?
- Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that’s a key consideration.
- Computing power. Hadoop’s distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
- Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
- Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
- Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
- Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.
How is Hadoop being used?
1) Low-cost storage and data archive
The modest cost commodity hardware makes Hadoop useful for storing and combining data such as transaction, social media, sensor, machine, scientific, click streams, etc. the low-cost storage lets you keep information that is not deemed currently critical but that you might want to analyze later.
2) Data lake
Data lakes support storing data in its original or exact format. The goal is to offer a raw or unrefined view of data scientist and analysts for discovery and analytic. It helps them ask new or difficult question without constraints. Data lakes are not a replacement for data warehouse. In fact, how to secure and govern data lakes is huge topic for IT.
They may rely on data federation techniques to create a logical data structures.
3) Complement your data warehouse
We’re now seeing Hadoop beginning to sit beside warehouse environment, as well as certain data sets being offloaded from the data warehouse into Hadoop or new types of data going to Hadoop. The end goal for every organization is to have a right platform for storing and processing data of different schema, formats, etc to support different use cases that can be integrated at different levels.
4) IoT and Hadoop
Things in the IoT need to know what to communicate and when to act. At the core of the IoT is a streaming, always on torrent of data. Hadoop is often used as the data store for millions or billions of transaction. Massive storage and processing capabilities also allow you to use Hadoop as a sandbox for discovery and definition of patterns to be monitored for prescriptive instruction. You can then continuously improve these instruction, because Hadoop
is constantly being update with new data that doesn’t match previously defined patterns.
5) Sandbox for discovery and analysis
Because Hadoop was designed to deal with volumes of data in a variety of shapes and forms, it can run analytical algorithms. Big data analytic on Hadoop can help your organization operate more efficiently, uncover new opportunities and derive next-level competitive advantage. The sandbox approach provides an opportunity to innovate with minimal investment.
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