Why Use Big Data and Hadoop?
Hadoop handles big data that conventional IT systems cannot manage, because the data is too big (volume), arrives too fast (velocity), or comes from too many different sources (variety). (Learn more about big data basics.)
Hadoop technology is efficient and cheap. Big data and Hadoop together make a powerful tool for enterprises to explore the huge amounts of data now being generated by people and machines. Big data analysis, Hadoop style, can help you generate important business insights, if you know how to use it.
Overcoming Limits of Conventional Systems
A conventional database has two big limitations. First, it only handles data with a certain structure. Data with a different structure must be transformed, before being loaded into the database. Second, it is difficult to expand beyond one physical IT system. Making the system more powerful to handle more data rapidly makes it much more expensive.
Hadoop technology solves these problems by the following approach.
- Data, whatever its structure, is stored “as is” in Hadoop nodes. A software program using Hadoop data and functions only applies the specific transformation it needs, when it needs it.
- A Hadoop node runs on an ordinary (commodity) IT system. To get more power, you simply add more nodes and more of these inexpensive systems. There is no limit to scalability and costs stay at a reasonable level. In addition, Hadoop software is Open Source software that is free to use.
“MapReduce” to Divide and Conquer
Big data and Hadoop make the most of these cheap systems, thanks to Hadoop’s “MapReduce” process.
- Hadoop distributes the big data over multiple nodes, each node doing a part of the processing or analysis required. Hadoop keeps track of which part of the data is being handled by which node. This is the “Map” part of the process.
- When nodes are ready with the results of their processing, Hadoop brings these results back into one place to give an overall result for all the big data processed. This is the “Reduce” part of the process.
User-Friendly Big Data and Hadoop
See it in action:
Until recently, using Hadoop technology needed specialist skills. Today, new developments have improved the user-friendliness of Hadoop solutions, allowing non-technical users to use intuitive business intelligence and analytics interfaces to reach bi insights leverage the power of big data and Hadoop.