Today’s world runs on data. Almost every rideshare application, food order app, retail or shopping site, and even all e-commerce sites require consumer data to provide an optimally satisfying customer experience. As every aspect of the web and applications are becoming experience-driven, every corporation and company are thinking about monetizing their data. Unfortunately, with the rise of mobile computing and multi-device access, gargantuan volumes of data keep flowing in from all directions. The traditional database architecture is no longer sufficient to hold enormous amounts of data or organize it appropriately.

Why is dealing with Big Data a significant challenge?

Big Data usually flow into a heterogeneous environment that data scientists typically refer to as a data lake. They are different from data warehouses. The traditional warehouses of data have a comparatively uniform architecture that is either wholly definite or rigid. Some companies define their data lakes as modern data warehouses, primarily since they use Hadoop. Hadoop makes data collection, storage, and management quite straightforward even for the small businesses that are new to the world of Big Data.

Here are the currently available technologies that deal with Big Data technologies –

• Traditional RDBMS including SQL databases
• NoSQL database systems
• Hadoop and other massively parallel computing technology

What are SQL databases?

RDBMS or relational database management system has been the standard response to all data storage and collection challenges people have faced in the near past. However, SQL databases are usually appropriate for a definite volume of data that has defined structure. Relational databases have been losing popularity in recent times as the age of Big Data dawns upon us. Big Data has massive volume, and it flows in at a tremendous velocity. It is highly variable that a traditional RDBMS database cannot tackle. It is not the primary scalable solution that meets every need for Big Data.

What are NoSQL databases?

NoSQL databases are taking over the data management landscape thanks to the rise of Big Data. Nonetheless, the much popular and time-tested structures are not enough to either store or analyze the ever-evolving nature of Big Data. Database admins now require something dynamic yet robust to tackle the management and analytical problems the new generation of data throws their way.

Unlike traditional SQL technology, NoSQL is flexible, and it is highly scalable. Most NoSQL database leaves room for the DBA to define and redefine data types and database structures. NoSQL allows the database admin to trade off rigid structures for agility and speed. It is the ideal requirement for Big Data management where the primary necessity is speed and not accuracy. Some of the most significant data warehouses including Google and Amazon now leverage the power of NoSQL to manage their unmeasurable bulk of data. Due to its incredible scalability, the users can continue to add more hardware as the data continues to explode.

What is Hadoop?

On the other hand, the state-of-the-art technological solutions that are capable of handling Big Data include the likes of Hadoop. It is not a database. It is a software ecosystem or framework of multiple software programs that support parallel computing. It does enable certain NoSQL database types to store and collect Big Data, like the HBase. It allows the expansion of data across multiple servers with little to no redundancy.

What is the role of MapReduce in the Hadoop framework?

MapReduce is a stable computational model of the Hadoop ecosystem. It plays a critical role in the determination of the intensive data processes from the ecosystem and spreads the computation throughout thousands (potentially endless) of servers. DBAs refer to this as a Hadoop cluster. Hadoop has standardized models that make data management a breeze for new companies and long-time running corporations. It comes with inherent fault tolerance. The data processing enjoys protection against hardware failure. Therefore, in case of a node malfunction, the job automatically goes to another node to ensure that the distribution computing remains continuous. In short, no matter how massive your data-load is, Hadoop has the solution.

Most companies that use Hadoop enjoy high flexibility of data types and scalable storage options at a low cost. Thanks to remote database management services the maintenance and updating of Hadoop enabled NoSQL databases has become a lot easier than it used to be. Users no longer require the presence of on-site DBAs for the optimization of database performance. Off-site database administration services can take care of updating, managing, caching and maintaining complete databases from remote locations. To know more about remote database management.

What are the most prominent uses of Hadoop right now?

Data analytics and predictive analytics — Most corporations and SMBs use Hadoop for analytics purposes. When there is a massive volume of data that require analysis, Hadoop is the primary choice for data scientists. It has the ability to store and process multiple data types simultaneously. That makes Hadoop the perfect fit for Big Data analytics and predictive analytics. Big Data environments are highly heterogeneous, and that consists of various information in structured, semi-structured and unstructured forms. Whether it is social media posts, social networking activities, clickstream records or customer emails, Hadoop has the agility and potential to store and sort it all.

Customer analytics — As a result, most companies use Hadoop for customer analytics purposes exclusively. One of its top functions is to predict customer behavior including conversion rates and track consumer emotions. Analysis like these utilizes information from social media activities of individual users and responses to corporate or promotional emails. E-commerce companies, healthcare organizations, and insurers often use Hadoop for analyzing promotional offers, treatment opportunities, and policy pricing respectively.

Predictive maintenance — Several manufacturers are now leveraging Hadoop in the maintenance of operations to determine equipment failure as they are about to happen. They are running real-time analytics applications including Apache Spark and Apache Flink along with Hadoop for improving their accuracy during prediction. The emergence of Hadoop as a robust and reliable prediction analytics tool has enabled the detection of online fraud, and cybercrime. It has also improved aspects of website and user interface (UI) design by gauging signs of customer satisfaction.

Hadoop has made its mark in the data management realm by attracting prominent IT vendors including Hortonworks, MapR, Cloudera and AWS. The Hadoop framework is attracting users and vendors from all across the globe. Its popularity is soaring along with the increasing importance of Big Data.

This article is contributed by Jack Dsouja, noted data analyst at RemoteDBA.com

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