Organizations today face a challenge of data disparity greater than ever before. Driven by company size and type, the data is huge, complex and is generated in different forms and varieties like -
- Structured data
- Internal unstructured information – documents, images, files
- Web generated text data – Tweets, Facebook posts, LinkedIn updates
- Machine data – information from sensors or other electronic devices
- Location data – Mobile GPS data
Hadoop is a solution to the problem of Big Data. It has an ability to turn the challenge of big data into opportunity. Hadoop provides benefits of BI tools along with advanced visualization and predictive analytics to explore data in new ways and discover patterns.
Why implement Hadoop?
While Hadoop can essentially house any type of data, structured or not, often times other specific technologies can help position the data for better analysis and understanding. Hadoop creates a data environment that provides the flexibility and the scalability to support innovation. It is offered for free as an open-source product, optimized to run on commodity server hardware, and its data environment can be customized as per organizations specific needs.
Features and benefits offered by Hadoop:
- Hadoop provides storage of Big data at reasonable cost
With Hadoop data-environment organizations are able to lower the transactional cost of managing and moving the data, as well as lower the cost of maintaining the hardware structure, through the addition of servers at typically lower and commoditized price.
- With Hadoop, you can store data longer
To manage a high volume of data, companies usually purge old data periodically. But with Hadoop it is possible to store the large volume of historical data longer.
- Hadoop allows to capture new and more data
The architectural flexibility allows the organizations to utilize any kind of data - structured, unstructured or semi-structured, from numerous devices or, sensors to generate business insights enabling them to stay competitive and drive revenue.
- Hadoop allows sharing customer data quickly
Organizations can use big data to dramatically improve every function of the business such as product enquiry, design and development, advertising and marketing, sales, and the user experience. Data combined from multiple silos can help your organization find answers to complex questions that no one has previously dared ask or known how to ask. Hadoop can be used to create a “data lake” — an integrated repository of data from internal and external data sources.
- Hadoop provides scalable analytics
Along with distributed storage, Hadoop provides distributed processing. The large volume of data can be crunched in parallel. The computing framework of Hadoop is called Map Reduce, proven to the scale of petabytes.
- Hadoop provides rich analytics
Native Map Reduce supports Java as primary programming language and other languages like Ruby, Python, and R. Several Apache projects have used advanced programming models for parallel processing (Map Reduce), resource managers (YARN) and other related projects (Ambari, Cassandra, HBase, Hive, Pig, Spark and the like) to truly exploit the power of Hadoop.
By connecting the right tools for data capture and delivery companies have started implementing a Hadoop-based data environment to protect their investment of time and money. To ensure that their critical data is acquired, integrated and normalized for more efficient use they are more likely to automate the process of data capture. Hadoop users are integrating their visualization capabilities and web-based collaboration tools with their Hadoop environment. This allows for the exploration of a wider variety of data and ability to share and socialize new insights derived from its analysis.
Click Here for Big Data Course