In this article, we point out top reasons why Hadoop is a game changer certification for IT engineers and analysts.
Hadoop-based Technology Key to Big Data Resources
Technology is shifting tracks faster than businesses can adopt. Smaller companies and open source programming community are coming together to totally transform the way Big Data industry operates in 2019. Amidst all the noise and ambiguity around how Big Data analytics and IT trends impact DevOps, we have a shiny and powerful entity that remains a central figure any IT professional can latch onto and drive straight into a solid career path.
Open Source Feature Make it readily Adaptable
If you are new to IT cloud industry but want to make quick inroads into the industry, Hadoop is your entry point. Apache Hadoop is the most popular and reliable Big Data tool, that ties well with a numerous tools such as storage, batch processing, and resource management layers.
Often regarded as the liquid data foundation of any Big Data project, Hadoop Distributed File System is the perfect blend of technology and human expertise working in sync. Together with MapReduce and Apache Spark help to shoot many challenges that modern Cloud and on-prem architectures suffer from.
If you have speed, and understand the 4Vs of Big Data, you are ready for Hadoop!
Hadoop engineers work in andem on their batch processing and anomaly detection. Back in those days when machine learning was just a concept and AIOps teams were still a piece of virtual imagination, engineers would process in parallel on a cluster of nodes, and follow it with manual detection of fault tolerance. Hadoop changed all that.
Hadoop’s fault tolerance capability is the most sought after feature. In fact, there are thousands of open source documents and A/B experiment models to help Hadoop engineers manage fault tolerance in Geographically Distributed Data Cluster.
What Fault Tolerance does in Hadoop architecture?
Hadoop engineers are capable of identifying and removing recurring faults in a system. With auto-correction and fault anomaly tolerance, Hadoop systems are 100 times more capable of data replication – something that could save from recreating the scenario all over again manually.
Trends Show Hadoop is King among All Big Data and ITOps tools
Hadoop is fairly easy to start with. It becomes easier if you have an IT background and have worked with basics of Java programming, Linux, Python, PERL, and Scala. With an average experience in Python and R, your Hadoop skills could further refine the Big Data Analytic and Data reporting models you work with.
In advanced Hadoop courses, IT professionals can lay their hands on a popular concept called Hadoop Streaming. This is a key utility within Hadoop Distribution that helps to execute complex Big Data programs using other languages I just mentioned above.