2 Jan 2017 Cornelius Evans
This is the age of data computation and manipulation. With astronomical amounts of data being transacted on a daily basis, it has become a necessity for organizations and enterprises to work with data in a comprehensive and innovative manner. As businesses have started realizing the importance of big data analytics, the importance of platforms to implement big data is rising. One such implementation technology is Hadoop which has risen to be at the top of the big data revolution providing immense implementation abilities and applications. Getting trained in Big Data analytics through Big Data training or Hadoop online training is now an easy task as a number of such resources are available offline and online. As organizations are embracing the Hadoop way, we bring to you some quick tips for using Hadoop as Big Data analytics tool.
Start early and practice more
Consistent practicing on this tool will take you a long way in making you well versed in this technology. Using cloudera QuickStart VM will be helpful in practicing big data analytics. Additionally, start using graphical user interfaces like ambary or cloudera manager after getting familiar with command line.
Increasing ulimits to maximum is not such a good idea
You must configure Hadoop for optimal performance which does not mean making it a data daemon. Your optimization and increase of limits must depend entirely on your own application.
Checking if your NoSQL database is working correctly
If you have installed and integrated a NoSQL database with your Hadoop, you want to use YCSB benchmark to check if it is working as per your requirements.
Understand various stepping stones to Hadoop
Data auditing is always a good idea
Whenever you audit data, you can identify what aspects of your data structure might be useful but you haven’t exploited yet.
Stay updated and stay ahead
Always keep track of new and emerging technologies in Hadoop Big Data , they move rapidly and you have to keep pace.
Combine hardware and software
Make your system work harmoniously between the hardware and software to gain the maximum advantage for you specific needs. You can also get your hardware designed to suit your specific needs.
Hardware configuration of clusters is very important
If and when your load is Input-output bound, the specifications of your disk spaces and memories is very important. As a rule of thumb; when the CPU is bound, use a faster CPU and when the memory is bound then use a faster Server.
Importance of network connectivity is paramount
At least 1 gigabit of NIC is must in Hadoop cluster in order to provide inter-communications without a bottleneck situation in the clusters which will make your application drag.
Use a good monitoring tool
Tools such as Ganglia are beneficial in monitoring and pointing out any bottlenecks in order to provide a smooth flow of operations.