The Evolving Scope of Hadoop in Big Data Database Management

Big Data and Hadoop cross paths at numerous points and the future of this association seem to be bright from the data management and business application point of view. Hadoop is providing out to be an essential open-source platform for distributed computing, which outfits thousands of different server hubs to handle an immense volume of data. The Big Data, on its rise, procures a huge buzz as being a balanced quantity-quality procedure to gather information and insights through the big data stores.

Considering these both in a fine combination, one can assume Hadoop as the racehorse and Big Data a skilled jockey. It may also be the other way around. Some other experts put forth the comparison like Hadoop as the tool to construct the house of Big Data. Whatever relationships you are trying to establish between them, there is no doubt that these two technologies are exponentially growing and intensively connected.

Moreover, from a problem point of view also Big Data and Hadoop share the same issue as both being comparatively new and being tested by fast beat which is the standard for quickly developing and juvenile technology innovations.

Hadoop on Big Data

Big Data is here for quite some time now, primarily known as “business intelligence” before the latest buzz, despite. The business organizations which were using it were incompetent to unleash its fullest capacity with limited technological resources. The level of publicity for Big Data also backfired by not meeting the overarching expectations of the industry.

There was perplexity on the term Big Data itself, for which you get a unique explanation if you ask different individuals about it. Combining the essence of all those, for most parts, Big Data was characterized as an innovative way to mine out actionable insights from a huge store of information. This is not a standalone process but incorporates artificial intelligence, machine learning, geospatial data analytics, and a wide range of other use cases too.

Giants of Big Data Hadoop

The two giants in the times of big data Hadoop are:

  • Cloudera, and
  • Hortonworks

There are also reports that these may merge in the near future, which could be a merger of equals. These two now empowers the business organizations of all size and kind to take up the projects which were not possible at one time. They were working to the core of settling the IT issues which lead to the business issues. The pioneers in businesses now rapidly comprehend the potential of these technologies to establish new data-driven services. They also encouraged the businesses to settle on data-centric choices than going on with the officials’ instincts.

All these developments now point to the fact that big data is proving out to be far more than just data. Any enterprise, big or small, has to open doors to a gigantic volume of data in variable structured and unstructured format than ever before. There are many innovative alternatives to consider for the services using these data loads, and it is crucial in light of the distinct use cases or using diverse types of data, which means it’s conceivable to adopt the right technology based on your specific business needs.

Handling real-time data

Handling real-time data, you have to consider intelligent data management services and tools, which can create useful apps to ensure business esteem and critical customer value. As points out, on utilizing the data effectively, latest machine learning algorithms can empower the enterprises to offer unprecedented services like a hyper-customized experience in retail management for the banks to predict when a customer will be keen to avail a home loan.

Irrespective of the whirling changes in the data management spectrum, Hadoop had remained a center of the attractor for many endeavors. Sticking together, both Cloudera and Hortonworks have the capability to offer a thorough and solid arrangement of business products and services. For example, end-to-end big data cloud application is capable of offering for many complex and critical business organizations on a wide range of operational tasks.

As it had always been, data technology will keep its rapid pace, and a huge number of organizations may be looking for Hadoop innovations. Altogether, it will be a very interesting space to watch for in the coming years. Considering the changing market scenario, those businesses which can harvest insights from the big data solutions can hold a favorable position in the intense competition. No doubt that the organizations which are unfit or fail to incorporate these innovations may fall far behind.

Hadoop vs. Cassandra

Another question technology administrators have in mind is between the right choices to make among Hadoop vs. Cassandra. Apache Cassandra is a unique NoSQL DBMS which enables high-speed transactional data management online. However, Hadoop largely focuses on the aspects of data warehousing and handling use cases of data lakes.

Hadoop is designed to manage the parallel processing of data. We can effectively use it as a warehouse of huge volume data. In other terms, Hadoop is a unique framework which will let storing and processing the big data in an efficient distributed environment over a cluster of computers. The primary objective of this model is to scale up storage and processing from a single server to a large of parallel machines

Cassandra is a NoSQL DB, which is aiming at high-speed data transaction. It can work without even a single failure point. It also helps to keep the status updated from the surrounding nodes in the network cluster by using gossip protocol. There may be times when a node may go down whereas another one automatically takes its responsibility until the one gone down is fixed. Also, while anode exchanges the gossip, it overwrites the previous info with the newer version.

For many Cassandra can be an ideal choice when it comes to availability, low latency, scalability without compromising on performance, etc. But when the need is for better data storage, information searching, analytical requirements, and reporting abilities, Hadoop will be a great choice to make. So, it is a matter of comparison based on the priorities of the businesses opting for big data solutions.