Nearly nine years ago, Hadoop came into existence for Big Data to provide cheaper storage and parallel processing for large and complex datasets.
There are many tools and technologies that resides on the top of Hadoop for processing different types of data, such as MySQL that provides a SQL environment for Hadoop, NoSQL databases that provide schema-less features and support semi and un-structured data. Hadoop was architected around batch-oriented because they felt that batch was best. Before Hadoop, there was no way to store and process large amounts of data on commodity servers.
In various ways, Hadoop file system is like a fine wine. It improves with age as harsh edges (or flavour profiles) are smoothed out, and the individuals who hold up to expand it will likely to have a superior affair.
The work being done by companies like Cloudera and Hortonworks at the distribution level is great and important. MapReduce can be used as a processing framework for certain types of batch workloads. But, not every company can afford to manage Hadoop on a day-to-day basis.
What Hadoop founder says about future of big data!
- “In the future, we’ll be able to store and process more data than we can now.”
- “The enterprises that will do best are those that will best leverage their technology.”
- “Not only can we afford to store more data in the future, but in many ways as well.”
- “Hadoop will get better.”
- “More things will get integrated … and that trend will continue.”
- “More and more data will move out of silo systems and into central systems that provide a variety of tools running on a variety of datasets … essentially an ‘enterprise data hub’.”
The founder of Hadoop thinks, the future will be one that uses and extends his baby more and more.
The scope of Hadoop never ends, demand and need will be increasing and it’s proportional to the rate of data generation. In another way, we can say that, newer frameworks like Spark, Storm that provides in-memory clustering, will fulfil the demand of streaming data.