Dealing With Unsanitized Data

Big data is not just a buzzword. It is indeed a very important concept with a considerable impact on business in general. Big data is a vast collection of various kinds of structured and unstructured data gathered from inner and outer resources which, after processing and analyzing, can be turned into valuable insights. Conventional database techniques can’t be applied to big data processing. In today’s information- and technology-dependent world, there is a burning need for new effective techniques to handle data and make most out of it. Real-time data collection provides us with the opportunity to know about customer preferences in real-time. Big data enables the segmentation of customers, a customized approach, and the ability to target the audience more precisely and in a more well-prepared way.

First of all, all that data needs to be analyzed correctly. The following points are important to consider when dealing with unsanitized data.

Let’s look at potential solutions for challenges involving the three Vs — data volume, variety, and velocity — as well as privacy, security, and quality.

Tools like Hadoop are great for managing massive volumes of structured, semi-structured, and unstructured data. As it is a new technology, many professionals are unfamiliar with Hadoop, and using it requires a lot of learning. This eventually diverts the attention from solving the main problem towards learning Hadoop.

Visualization is another way to perform analyses and generate reports, but sometimes, the granularity of data increases the problem of accessing the level of detail needed.

It is also a good way to handle volume problems. It enables increased memory and powerful parallel processing to chew high volumes of data swiftly.

Grid computing is represented by a number of servers that are interconnected by a high-speed network; each of the servers plays one or many roles.

Platforms like Spark use a model plus in-memory computing to create huge performance gains for high-volume and diversified data. All these approaches allow firms and organizations to explore huge data volumes and get business insights. There are two possible ways to deal with the volume problem. We can either shrink the data or invest in good infrastructure to solve the problem of data volume, and based on our budget and requirements, we can select the most appropriate technology or method. 

Let’s look at OLAP tools, Apache Hadoop, and SAP HANA.

Data processing can be done using OLAP tools to establish connections between information and assemble data logically in order to access it easily. OLAP tools specialists can quickly process high-volume data. One drawback is that OLAP tools process all the data provided to them regardless of the data‘s relevancy.

Hadoop is an open-source software whose main purpose is to manage huge amounts of data in a very short amount of time with great ease. The functionality of Hadoop is to divide data among multiple systems infrastructure for processing it. A map of the content is created in Hadoop so it can be easily accessed and found.

SAP HANA is an in-memory data platform that is deployable as an on-premise appliance or in the cloud.