Full Stack Big Data

Machine learning has got off the ground and it’s taking every industry by storm, bringing new insights to businesses that aid them in taking result oriented actions. If you want to be in demand, you need to learn this skill in order to separate yourself from mere data analysts. Getting started with machine learning can be intimidating for many of us, but it’s surprisingly easy to learn machine learning if you approach it in a right manner. Believe it or not, Machine learning (ML) is a compelling field of study that’s why it is quite easy for a novice to get fascinated. It’s what that has given birth to handwriting recognition, face recognition systems, self-driven cars, navigation systems of drones of all kinds, robots that can clean your house, or the recommendation system behind YouTube and Netflix. Before Starting, a Word of Caution for beginners Machine learning (ML) is a vast field of study and is something of an advanced practice, and you’ll need to have not ...

The world is going digital, and this simply indicates that big data is here to stay. In fact, big data is present all around us, and there is an urgent need to gather, sort, and analyze whatever data is being generated, since data is the new oil. The importance of data analytics and big data is only going to increase in the coming years. You might know that big data is a technology-driven approach that is widely used to evaluate complex data sets to arrive at key decisions. Thus, we can say that big data is a smart career choice and it could be just the right type of career you have been looking for. Those who are working in the field of data science can expect a handsome salary, with the median salary range for big data scientists around $116,000. Even those who are working at the entry level as a data scientist can expect average earnings of $92,000. As more and more organizations realize the need for data scientists and big data analysts, the number of jobs will continue to gr...

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...