Big Data Hadoop

This Big Data Course in collaboration with IBM helps you master Big Data and Hadoop skills like MapReduce, Hive, Sqoop, etc. through real-time projects and case studies.

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Hadoop is an open-source framework that is used to store and process large amounts of data across clusters of commodity hardware. It was originally developed by Apache Software Foundation and is now widely used in industry for big data processing.

Hadoop is based on a distributed file system called Hadoop Distributed File System (HDFS), which allows data to be stored across many machines in a cluster. Hadoop also includes a distributed processing system called MapReduce, which allows for parallel processing of data across multiple nodes in the cluster.

The objectives of using Hadoop for big data processing can vary depending on the specific use case and organization, but some common objectives include:

  • Scalability: One of the primary objectives of using Hadoop is to be able to process and store large amounts of data in a scalable and cost-effective manner. Hadoop’s distributed file system and processing framework allow for adding more nodes to the cluster to handle increasing amounts of data.
  • Data processing: Hadoop’s MapReduce framework allows for processing large amounts of data in parallel, which can significantly reduce the time it takes to process data compared to traditional processing methods. This objective is especially important for organizations that need to analyze large amounts of data in real-time or near real-time.
  • Fault tolerance: Hadoop’s distributed nature makes it highly fault-tolerant. Data is replicated across multiple nodes in the cluster, so if one node fails, the data is still available on other nodes. This objective is critical for organizations that need to ensure high availability and reliability of their data processing.
  • Cost savings: Hadoop is an open-source framework, which means that it is free to use and can be run on commodity hardware. This objective is especially important for organizations that need to process large amounts of data without breaking the bank.
  • Flexibility: Hadoop is a highly flexible framework that can be used to process various types of data, including structured and unstructured data. This objective is critical for organizations that need to be able to handle a variety of data types and formats.

Overall, the main objectives of using Hadoop for big data processing are to be able to handle large amounts of data in a scalable, fault-tolerant, cost-effective, and flexible manner.

  • More than 55% of IT industries have been shifted to the cloud space and the numbers are expected to increase by over 25% in the future. And it has been predicted by Forbes that more than 80% of Enterprise Workloads will efficiently be handled through cloud technologies.
    • Unlike different disciplines, the salary of a Big Data Hadoop professional is usually 30 percent higher.

      At the beginner level, you can earn up to $113K per annum.

      Even with less than one year of experience, you will make around Rs.4, 80,000 in a year.

  • Big Data Hadoop certified are high in demand and gaining significant traction in the US and Canada. People with years of experience can earn maximum of Rs. 1,800,000 per annum.

  • No matter whether you are a fresher or an experienced professional, our Big Data Hadoop course will help you gain the relevant skills you required. If you are looking to start a career in the field of cloud, we will help you gain all essential skills and let you clear the global certification examination, too.
    • Establish yourself as a certified Big Data Hadoop professional and get hired by leading IT industries.

      Clear the interview and gain all essential technical and communication skills.

      Get a more than 30 percent hike in salary as a certified professional.

  • On the course completion, attempt the Big Data Hadoop exam and clear it in the first attempt as we assure 100% clearance rate for our students.

  • There’s no denying that Big Data Hadoop now become the future in today’s tech-dominating landscape. As most of the industries are aggressively adopting the best Big Data Hadoop solutions, the demand for skilled Big Data Hadoop professionals has been increased worldwide.
  • The reasons behind the popularity of Big Data Hadoop are as follows:
    • A multitude of career benefits as an Big Data Hadoop professional

      In-demand Big Data Hadoop skills

      Placement Assistance

      Beyond expected salaries

      Best-in-class course content curated by leading industry practitioners.

There are various job roles associated with big data Hadoop, and each role comes with different responsibilities. Some of the common job roles in the big data Hadoop ecosystem are:

  • Hadoop Developer: The primary responsibility of a Hadoop developer is to design, develop and implement Hadoop applications. This role involves writing MapReduce jobs, Pig scripts, Hive queries, and other Hadoop ecosystem tools to process and analyze large data sets. Hadoop developers are also responsible for testing, debugging, and optimizing Hadoop applications.
  • Hadoop Administrator: The primary responsibility of a Hadoop administrator is to install, configure, and maintain the Hadoop ecosystem. This role involves managing Hadoop clusters, monitoring cluster performance, and troubleshooting issues. Hadoop administrators also ensure data security, manage user access, and maintain the overall health of the Hadoop infrastructure.
  • Big Data Architect: The primary responsibility of a big data architect is to design and implement big data solutions using Hadoop and other big data technologies. This role involves designing scalable and fault-tolerant Hadoop architectures, selecting appropriate Hadoop components, and integrating Hadoop with other systems.
  • Data Analyst: The primary responsibility of a data analyst is to analyze data using Hadoop tools and technologies. This role involves developing data models, analyzing data using Hadoop MapReduce jobs, and generating reports using data visualization tools.
  • Data Scientist: The primary responsibility of a data scientist is to use Hadoop tools and technologies to analyze and derive insights from large data sets. This role involves designing and implementing machine learning algorithms, developing predictive models, and generating insights from data.

Overall, the responsibilities of a big data Hadoop professional vary depending on the specific job role, but most roles involve working with large data sets and using Hadoop tools and technologies to process and analyze data.

  • Top industry verticals:
    • Travel Industry, IT Sector, Healthcare and Medical Sector, Media & Entertainment, Banking & Finance, Transportation, Non-Profit Industries, eCommerce, Insurance Sector, etc.

  • Top hiring companies:
    • IBM, NETFLIX, FEDEX, Infosys, American Express, etc.

  • Benefits of getting trained with us:
    • Find yourself a more competent, confident, and knowledgeable resource.

      Crack any interview with leading industries without any difficulty.

      Start working in your dream company like a PRO.

      See a drastic change in your communication skills.

  • You will get a completion certification to validate your skills after the completion of Big Data Hadoop certification training. Also, you are likely to get recognized among top corporate giants. When it comes to enhancing your overall career, we endeavor to give the maximum support.
    • Our training certificate is globally accepted

      Helps the learner to climb the professional ladder

      It showcases credibility

      Increases your earning potential

      Makes you stand tall above the rest

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Plenary for Big Data Hadoop Program

Track Week Days Course Duration Fast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 2 Hrs. Per Day 3 Hrs. Per Day 6+ Hrs. Per Day
Training Mode Online Online Online
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There are no specific eligibility criteria to enroll in our Big Data Hadoop course. Anyone interested in starting a career in Big Data Hadoop can register for this Big Data Hadoop training program and become job-ready.

Yes, our dedicated crew of professional regularly updated the content of our course; ensuring students must get the latest knowledge.

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There are several benefits to taking a Big Data Hadoop course, including:

  • In-demand skillset: With the explosion of big data in recent years, the demand for professionals with Hadoop skills has increased significantly. Taking a Big Data Hadoop course can provide you with the knowledge and skills needed to enter this high-demand field.
  • Career growth: As more organizations adopt Hadoop for big data processing, the career opportunities for professionals with Hadoop skills continue to grow. By taking a Big Data Hadoop course, you can position yourself for career growth and advancement.
  • Comprehensive knowledge: A Big Data Hadoop course can provide you with a comprehensive understanding of Hadoop and related technologies, including HDFS, MapReduce, Hive, Pig, and Spark. This knowledge can be invaluable for working on big data projects and solving complex data-related problems.
  • Hands-on experience: Many Big Data Hadoop courses offer hands-on experience, which allows you to apply what you learn to real-world scenarios. This can help you develop practical skills that can be applied in your work.
  • Networking opportunities: Big Data Hadoop courses often provide opportunities to connect with other professionals in the field, such as instructors and fellow students. This can help you expand your network and potentially lead to new career opportunities.

Overall, taking a Big Data Hadoop course can provide you with the skills, knowledge, and experience needed to enter and succeed in the field of big data processing and analysis.

There are no prerequisites associated with our Big Data Hadoop courses. It can be taken up by beginners as well as experienced professionals who want to gain in-demand Big Data Hadoop skills.

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