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Implement Best Practices For Hadoop Development

This web-based training course on Implement Best Practices For Hadoop Development functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

Hadoop is the leading tool for big data analytics and is being used worldwide in almost every single organization which transacts large amounts of data on a daily basis. The capabilities and features of Hadoop which provide big data manipulation and processing are proven and trusted across the world. The various elements and features of Hadoop and its extensions such as HDFS, MapReduce, Pig, Hive, Yarn, etc. provide abilities which can be leveraged to improve data modeling and processing methodologies of any organization in order to make better business decisions and predictions.


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Course Details

This is a special purpose course which is structured for professionals who are already working with the Hadoop environment or planning to work in the Hadoop environment for data processing and modeling. The course provides the best practices and work methodologies which will help professionals in gaining an edge over their competitors and creating even beter data processing models than they ever could. This Best Practices for Big Data Hadoop development course is structured with in depth understanding of the practical understanding of Hadoop’s secrets which can be leveraged effectively.


Before gathering data, always gather business requirements

  • How to gather, analyze and understand the business requirements
  • Methods of aligning big data projects along specific business goals

Consider implementation of big data as a business decision and not IT

  • Approach analytics solutions from a business perspective
  • Create Solutions which fit defined business needs

Use of Agile and Iterative Approach for implementations

  • Use-case and data set for specific purposes
  • Deliver quick solutions through Agile and iterative methods
  • Identify specific and high-value opportunities
  • Do not lose site of the big picture
  • Understand the Big data framework

Methods of Evaluating data requirements

  • How to carry out a full evaluation of data
  • Business stakeholders inputs
  • Analyze the data which needs to be retained, managed and made accessible
  • Analyze which data can be discarded

Use standards and governance for skills shortage

  • Standardize big data efforts in an IT governance program

Improve transfer of knowledge using a center of excellence

  • Create a Center of Excellence (CoE) for sharing solution knowledge
  • Planning artifacts and ensuring oversight for projects
  • Sharing Soft and hard costs across the enterprise
  • Drive information architecture maturity in a structured and systematical way

How to Embrace and plan sandbox for prototyping and performance

  • Construct data experiments and prototyping in preferred languages and programming environments
  • Reprogram and/or reconfigure implementations using an IT turn-over team

Aligning with the cloud operating model

  • How to create Analytical sandboxes on-demand
  • Resource management needs while having control of the entire data flow
  • Planning private and public cloud provisioning and strategies of security
  • Advantage of a public cloud for being provisioned and scaled up instantly

Associating big data with enterprise data

  • Getting associated with enterprise application data
  • How to establish new capabilities and leveraging prior investments
  • How to invest in integration capabilities while enable knowledge workers

Embed analytics and decision-making with intelligence

  • Making “analytics” the way business is supposed to be done
  • Making analytics a part of the corporate culture
  • How to model and forecast businesses
  • Is leaving analytics to silos of teams a good idea?

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