Big Data is a modern analytics trend that allows companies to make more data-driven decisions than ever before. When analyzed, the insights provided by these large amounts of data lead to real commercial opportunities, be it in marketing, product development, or pricing.
Companies of all sizes and sectors are joining the movement with data scientists and Big Data solution architects. With the Big Data market expected to nearly double by 2025 and user data generation rising, now is the best time to become a Big Data specialist.
Today, we’ll get you started on your Big Data journey and cover the fundamental concepts, uses, and tools essential for any aspiring data scientist.
Here’s what we’ll go through today:
Master Big Data with our hands-on course today.
Introduction to Big Data and HadoopThis course offers a one-of-a-kind rich and interactive experience to learn the fundamentals and basics of Big Data. Throughout this course, you will have plenty of opportunities to get your hands dirty with functioning Hadoop clusters. You will start off by learning about the rise of Big Data as well as the different types of data like structured, unstructured, and semi-structured data. You will then dive into the fundamentals of Big Data such as YARN (yet another resource manager), MapReduce, HDFS (Hadoop Distributed File System), and Spark. By the end of this course, you will have the foundations in place to start working with Big Data, which is a massively growing field.
Big data refers to large collections of data that are so complex and expansive that they cannot be interpreted by humans or by traditional data management systems. When properly analyzed using modern tools, these huge volumes of data give businesses the information they need to make informed decisions.
New software developments have recently made it possible to use and track big data sets.Much of this user information would seem meaningless and unconnected to the humans eye. However, big data analytic tools can track the relationships between hundreds of types and sources of data to produce useful business intelligence.
All big data sets have three defining properties, known as the 3 V’s:
Correlation vs. Causation
Big data analysis only finds correlations between factors, not causation. In other words, it can find if two things are related, but it cannot determine if one causes the other.
It’s up to data analysts to decide which data relationships are actionable and which are just coincidental correlations.
The concept of Big Data has been around since the 1960s and 70s, but at the time, they didn’t have the means to gather and store that much data.
Practical big data only took off around 2005, as developers at organizations like YouTube and Facebook realized the amount of data they generated in their day to day operations.
Around the same time, new advanced frameworks and storage systems like Hadoop and NoSQL databases allowed data scientists to store and analyze bigger datasets than ever before. Open-source frameworks like Apache Hadoop and Apache Spark provided the perfect platform for big data to grow.
Big data has continued to advance, and more companies recognize the advantages of predictive analytics. Modern big data approaches leverage the Internet of Things (IoT) and cloud computing strategies to record more data from across the world and machine learning to build more accurate models.
While it’s hard to predict what the next advancement in big data will be, it’s clear that big data will continue to become more scaled and effective.