Hadoop is the de facto standard for Big Data. Its database is HBase.
Key Advantages of Hadoop
Hadoop is a platform that provides Distributed storage & Computational capabilities both
Hadoop is the popular data storage and big data analysis platform. Large and successful companies are using it to do mission critical tasks and powerful analysis. Hadoop offers two important services: It can store any kind of data from any source at very large scale, and its extreme performance can do very sophisticated data process and analysis of that data easily and quickly.
Hadoop delivers several key advantages:
Hadoop can be expended from few nodes to a large number of nodes, thus it can provide extreme processing power to handle extremely challenging requirements.
Store anything and NO information is lost
Hadoop stores data in its native format without forcing the transformation when data arrives, therefore no information is lost. Downstream analyses run with no loss of fidelity. Hadoop allows the data analyst to choose how and when to digest, analyze and transform data.
Proven at scale
You may not have petabytes of data that you need to analyze today, nevertheless, you can deploy Hadoop with confidence because companies like Facebook, Yahoo! and others run very large Hadoop instances managing enormous amounts of data. When you adopt a platform for data management and analysis, you are making a commitment that you will have to live with for years. The success of the biggest Web companies in the world demonstrates that Hadoop can grow as your business does.
Use with confidence
The user community of Hadoop and HBase is global, active and diverse. Companies across many industries participate, including social networking, media, financial services, telecommunications, retail, health care and others (for more information, please read: Who uses HBase and Hadoop).
Hadoop 2.x offers High Availability without Single-Point-Of-Failure using Multiple-Master model, multiple and redundant namenodes are acting in congress to Hadoop Distributed File System (HDFS), it enables Hadoop to scale out over 4,000 machines per cluster. It also introduces MapReduce NextGen which transforms Hadoop into a full blown platform as a service.
Big Data Random Access and Flexible Secondary Indexes
HBase is Hadoop's Database, with Built-in load-balancing, Automatic versioning, Automatic failover and Built-in scalability. It is a Strongly consistent database and provides random access to your big data.
Extremely cost effective to handle Big Data
Hadoop runs on industrial standard hardware. It means that the cost per terabyte, for both storage and processing, is much lower than on older systems. HBase makes efficient use of disk space by support pluggable compression algorithms. Adding or removing storage capacity is simple. You can dedicate new hardware to a cluster incrementally.
Hadoop Product Family - The Complete Tool Set for Big Data Analysis
The Hadoop allows for the distributed processing of large data sets across clusters of computers using simple programming models. Its product library provides Complete Tool Sets to analyze Big Data
Use cases of and Hadoop
Simple numerical summaries – average, minimum, sum – were sufficient for the business problems of the 1980s and 1990s. Large amounts of complex data, though, require new techniques. Recognizing customer preferences requires analysis of purchase history, but also a close examination of browsing behavior and products viewed, comments and reviews logged on a web site, and even complaints and issues raised with customer support staff. Predicting behavior demands that customers be grouped by their preferences, so that behavior of one individual in the group can be used to predict the behavior of others. The algorithms involved include natural language processing, pattern recognition, machine learning and more. These techniques run very well on Hadoop.
Use case of HBase and Hadoop, as follows but not limited to:
When you need random, realtime read/write access to your Big Data, you may consider Hadoop now.
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