NoSQL is the New Hadoop
The key challenge businesses world over face today is managing data explosion. The traditional business concepts that were used to manage data have become obsolete now. The changing dynamics in the technological landscape has led to newer and more sophisticated tools that work on data at jet speed these days. I’ve found that emerging technologies like the New Hadoop framework aim at better solution for big data systems. Why relational database is not relevant any more? Relational database management system or RDBMS in the traditional setup have been the only option used by organizations to manage their databases effectively. The relational database helps to organize data in a structured manner based on relational model. Though I think keeping data in a structure form is good for enterprises, in case of huge volumes this can become a big burden, leading to progressive decline in performance. The scene will be more frequent, once the data becomes too big to manage. This makes RDBMS an inappropriate scalable solution for big data. Generic Data Processing Framework Since relational database could not satisfy the demands of data, an alternative solution was required. This resulted in the introduction of data processing software. I’ve had many queries from those new to database management about what is Hadoop? It is nothing but software framework that enables parallel processing of huge amounts of data in a large commodity hardware cluster. The entire processing is error free and unswerving. The software can execute queries and also read operations on huge data sets, which have the capability of scaling to as
big as petabyte sizes. The software framework has an unrivalled price performance ratio that is brought about by the flexible analytics feature it exhibits. Structured, semi-structured, and unstructured data can be analyzed with the same fixed framework. Parallelism and its Uses The main advantage of Hadoop is its ability to route parallel queries in the form of huge background batches within the same server farm. This reduces the expenses of using an additional hardware as was the case in traditional database systems. And in my opinion, the time and effort needed is greatly reduced. The concept for this type of framework originated from search engines like Yahoo! and Google, which use massive inexpensive servers to read parallel queries, so search indices and related data structures can be formed. But when the data to be analyzed became alarmingly huge in size, the system could not keep up as the scaling needed lots of coordinating and caching methods to reduce the alignment required. New Heights in Scalability The introduction of new Hadoop technology like YARN (Yet Another Resource Negotiator) has brought new heights to the scalability factor of the file system. This new addition has enhanced the distribution processing of the system with the successful management of big data. The highlight of this new technology is clear assigning of responsibilities to different components, thus making it a highly desirable system that I’d readily recommend. Database for Dealing with High Data Volume I’d suggest the emergence of new databases that are appropriate for unstructured data is vital for data management. What is NoSQL? It is a new generation database management system that enables easy access and utilization of poly structured data in large volumes. Some of the key points it addresses are:
Cost effective scalable solutions
Flexible assessment of data structures, which do not conform to the relational system like graphs and key- value information
The database performs a horizontal type of scaling called sharding in which each server has a separate database that is partitioned physically, so each has the data stored in the local disks in it. The drawback I’ve experienced here is you cannot do joins, schema changes or transactions and you may also need to compromise the ACID (Atomicity, consistency, isolation, and durability) which results in relaxing of the consistency factor. Prudent Use of Databases
When compared to the relational database model, I’d suggest that the schema free file system has more advantages. Though the relational database will still be in use, organizations will prefer to work with applications that run on NoSQL. This is because it can bring about far reaching success in all types of environs including content management, help in offloading the query volume and providing a high performing data store and ad targeting areas. The similarity between Hadoop and NoSQL databases is the scalability factor. For users who don’t need high performance as a priority, but want the flexibility that a file system can bring, I’d recommend a document database as a good solution. Relational database is for those who perform more than one transaction across various data objects. Since NoSQL is more about scalability and high performance, its drawbacks will not matter much for those who are in exclusive need of its features. The ability to alter an application without going via a DBA, gives it a definite advantage. You are welcome to share your thoughts on Tata BSS page.