Big Data is a collection of complex and large data sets that are difficult to process and store using traditional methods or database management. The topic is broad and encompasses various frameworks, methods, and tools. Big Data consists of data generated by various applications and devices such as black boxes, traffic, search engines, stock exchanges, power grids, social networks, etc.
Apache Hadoop is an open-source software environment used by enterprises to store and compute big data. This framework is based on Java and some native C code and shell scripting. Hadoop was developed by the Apache Software Foundation in 2006. Essentially, it is a tool for processing big data and improving its expressiveness to generate more revenue and other benefits. This means that the Hadoop ecosystem is capable of solving Big Data problems, and in case you were wondering, how they are connected.
The different parts of the Hadoop ecosystem are TEZ, Storm, Mahout, MapReduce, etc. Hadoop is low cost, but highly scalable, flexible, and provides fault tolerance in its highly valued feature set. As a result, the adoption of this technology is growing rapidly.
Tips for Learning Apache Hadoop
Learn to love data
No one ever talks about motivation when it comes to learning. Data science is a vast and little-known field, which makes it hard to learn. It’s very difficult. Without motivation, you drop everything in the middle and think you won’t make it. Then it’s not about you, it’s about learning.
Learn the science of data in practice
Learning about machine learning, neural networks, pattern recognition, and other advanced techniques is important. But in most cases, data science has nothing to do with any of that.
Work on projects to get more knowledge about the technology. Working on projects gives you skills you can apply and use immediately, because real data scientists have to manage data analysis projects from start to finish, and much of that work involves basic things like cleaning and managing data.
Learn how to make yourself understood
Data analysts must constantly communicate their analysis to others. This can make the difference between a good data analyst and an excellent one. Data analysis is usually only useful if you can convince others in your organization to act on your findings, and that means learning how to communicate with data.
Tips to learn Big Data