Full Stack Big Data

Hadoop is an open source Java-based framework that supports businesses in the accumulation and processing of humongous data sets. Today, many business organizations across the world are struggling to interpret Hadoop software and are uncertain whether they should rely on it for delivering projects to their clients. Even so, it is important to understand how efficient Hadoop is for businesses. It’s no surprise that monitoring, analyzing and evaluating large and disparate data sets is an uphill task. Before the advent of Hadoop, many businesses relied on costly servers for their data analysis and evaluation. Now the process has become a lot more efficient and organized. Hadoop functions by allocating a large amount of data sets across hundreds of affordable servers that run parallel to one another. It is also one of the most effective storage solutions for businesses making the most of data sets. You might know that the Hadoop storage method is based on a distributed file ...

If you’re reading this post, chances are that you are well-versed with general terms such as “Data Analytics”, “Data Analysis”, and “Business Intelligence”, as they are frequently used in the context of Big Data.  Although ‘Big Data’ has been a buzzword for more than two decades, it is still one of the most misunderstood (and misused) terms. That’s why in this blog, we are going to disambiguate the term “big data”.  What Do You Mean By “Big Data”? In layman’s language, Big Data is a large set of structured, semi-structured or unstructured data that is so humongous and complex in nature that conventional data processing application software is not robust enough to deal with it. In other words, you can say Big Data is so voluminous in size that traditional data management software and tools are not able to collect, store and process it efficiently.   Big data can be easily characterized by 3Vs:  velocity (the speed of data moving in and out makes it hard...