Hadoop has consistently grown in popularity since its advent and today it is almost synonymous to Big Data. Demand for personnel with Hadoop skills has risen across different industries and Hadoop training academies around the globe are working to mend the skill gap. The world is waiting for a large pool of analytics professionals to be created and Hadoop training is going to be a key factor for aspirants to enter that pool.

I have spoken with people who have a generalized idea about Hadoop; it is widely known as a singular data management tool. This concept needs to be changed if you are looking to hook yourself in Big Data analytics otherwise you may be taken by surprise when you come across the facts.hadoop

  1. Hadoop is a family of systems overseen by Apache Software Foundation that are dedicated to different processes. The Hadoop library consists of Map Reduce, Pig, Hive, HBase, HCatalog, Ambari, Mahout, Flume, of course the Hadoop Distributed File System and so on. Learning Hadoop means acquiring knowledge about multiple of these open source components.
  2. Hadoop is not bound within the Apache Hadoop library, it is an ecosystem. There is a considerable amount of vendor products, like Data base management systems, analytic tools etc that assimilate with Hadoop technologies. You may consider keeping this in mind while choosing your Hadoop training program.
  3. 3. HDFS does not perform the jobs of a Data base management system. There are some basic differences between HDFS and standard DBMSs. A DBMS can perform tasks like indexing, providing random access to data, query optimization etc.

HDFS cannot do the aforementioned by itself but it can manage and process heavy amounts of file based, unstructured data; it can also obtain minimal DBMS functionality with a layering of HBase.

  1. HDFS and Mapreduce are related but not essentially complementary. Mapreduce deals with the complexities of network communication and parallel programming and it can work with HDFS but it can work with multiple other storage technologies: file systems or DBMSs. On the other hand users can deploy HDFS with HBase or Hive without Mapreduce.
  2. HDFS is capable of handling the storage and access of any type of data whether they are web logs, XML files or personal productivity documents. All you need to do is put the data in a file and copy that on the HDFS. It is how simply HDFS handles the variety of data types rather than the volume of data it can manage, that has made it so popular.
  3. Hadoop is not just for web analytics. Although it is best known for handling web data but it is as efficient in dealing with data generated from sensory devices Robotics or manufacturing. Old analytics technologies that require large data samples can use Hadoop to manage excess big data.

Being aware of these facts should help you visualize the scope and impact expected from a Hadoop training course. And it keeps you a step ahead when it comes to understanding Hadoop.