Hadoop and Big Data
Hadoop is another matter of conversation during the present times. Because of the rising interest in Hadoop it is mostly responsible in bringing unique solutions to its customers. The new technologies in Hadoop are aiming to bring in something revolutionary.
Hadoop is a java based framework for programming which processes large sets of data in a distributed computing environment. Sponsored by Apache Software Foundation it is a part of the Apache project.
Hadoop is often projected as a child of Big Data. These two are working side by side in such a manner that they are often regarded as synonyms for the same term of storing and analyzing huge data and information. Before Hadoop the data storage was expensive but Hadoop lets us store a large amount of data just by adding servers to the Hadoop cluster. With each new server the storage capacity is increased and the power for each cluster also rises substantially.
More on Hadoop
Hadoop is mainly comprised of Hadoop File System and MapReduce in order to handle large volumes of data with a lot of nodes. It creates and widens the channels for data accessing instead of what is called parallel computing.
Hadoop is mainly designed to handle large files storing data on different hard disks spread across different nodes. But if the enterprise is already benefitted with the services of Big Data then switching over to Hadoop will take time and may be a bit modification of the sources.
Hadoop may leverage many users but in order to prevent it from a crash it requires sufficient amount of memory.
More on Big Data
Because the data volumes are growing on every aspect like from social networks, data resources, supply chain so managing the information related to them is an issue. The information can be of any type, structured, semi structured or even unstructured and the goal of any user is to gain meaning out of that data.
If you have Big Data then searching through the bulk of information can be done by Hadoop.
Standard database is used if most of the data is structured or unstructured but we are able to add structured Meta data which describes the unstructured portion whereas column based data storage is preferred when we have structured, unstructured with structured Meta data and we want to run complex analysis.
Benefits of Hadoop
Hadoop lets both the structured and unstructured data be stored so a lot time and money is saved which otherwise would have been wasted on configuring the tables. It has an efficient scaling property so data coming from various resources are easily used. It is widely used for storing data in a cheap manner as compared to other software which are available. Online warehouses at a low cost can be implemented easily with the help of Hadoop.
Hadoop is the present trend of the market but there are vendors who are more used to Big Data but Hadoop makes the task even simpler.
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