About The Course
Big data is a term for so large or complex datasets that cannot be processed by traditional data processing applications. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.
Hadoop simply provides solution to the Big Data problems. Hadoop is an open source software framework for distributed storage and distributed processing of very large datasets on clusters of commodity hardware. Hadoop is having separate filesystem called HDFS (Hadoop distributed File System) and a distributed framework called MapReduce. After attending all the Hadoop training sessions at Great Online Training you will have complete understanding of all the components in Hadoop Ecosystem (i.e. HDFS, MapReduce, Hive, Pig, Hcatalog, Sqoop, Flume, HBase, Zookeeper, Oozie, etc.…)
How this course used by IT industry?
Techniques for analyzing data, such as A/B testing, machine learning and natural language processing Big Data technologies, like business intelligence, cloud computing and databases visualization, such as charts, graphs and other displays of the data Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet. Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS. The practitioners of big data analytics processes are generally hostile to slower shared storage, preferring direct-attached storage (DAS) in its various forms from solid state drive (Ssd) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—Storage area network (SAN) and Network-attached storage (NAS) —is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost. Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.
How to get job?
We suggest you to Surf through all the JOB portals, short list the jobs most appropriate for you. The most Important thing is DO NOT USE COMMON CV FOR APPLYING TO ALL THESE JOB. Modify your CV for applying to each JOB keeping job description in mind. In other words, as per Job description tailor-made your CV so that you get interview call for sure. Instead of applying 1000 job with common CV apply only 10 most appropriate job with Tailor-made CV. The second most Important thing is NEVER USE WORD FRESHER Keep this statement in your mind that “No employer is willing to conduct a training institute.” You are a certified consultant, be confident and go KING SIZE. Till the time you get your JOB keep sharing your knowledge and keep training your Juniors. This will help refreshing and gaining more knowledge. You can find all types of jobs for this course
Salary range for the course?
A Data Scientist in Indian IT industry earns an average salary of Rs.607,193 per year. Experience strongly influences income for this job. The highest paying skills associated with this job are Data Mining / Data Warehouse, Python, Machine Learning, Big Data Analytics, and Statistical Analysis. Most people move on to other jobs if they have more than 10 years’ experience in this career. In USA salary range for Data Analysts will be 60K $ to 100K $ per annum
Prerequisites to this course?
It is better if the student have basic knowledge of Core Java and SQL but it is not mandatory. We can give basic knowledge of Core Java which is required to learn Hadoop.
How to do certification?
To become a Certified Big Data & Hadoop Developer, you must fulfill both the following criteria: Complete any one project out of the four projects given by Great Online Training, within the maximum time allotted for the Big Data Hadoop developer course. When you have completed your project, you’ll email it to the lead trainer who will evaluate it. You can submit your project through LMS. Passing the online examination with a minimum score of 80%. If you don’t pass the exam the first time, you can re-attempt the exam one more time. When you have completed the course, you will receive an experience certificate stating that you have 3 months experience in implementing Big Data and Hadoop Projects.
Who can do this course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: Software Developers and Architects Analytics Professionals Data Management Professionals Business Intelligence Professionals Project Managers Aspiring Data Scientists Graduates looking to build a career in Big Data Analytics Anyone interested in Big Data Analytics