put in Big Data into perspective of supercomputing
describe Big Data in context of technology waves and put it into the perspective by comparing to previous technology waves
list the six emerging technologies and relate them to Big Data
define big data and describe Gartner's Vectors
define structured and unstructured data in terms of Gartner's model
list the standard sizes used in Big Data to determine sizes of data sets
list the three primary key contributors to the origins of Big Data
list the primary Big Data distro companies
describe the Apache Software Foundation
list projects attributable to the Apache Software Foundation
list projects attributable to the Apache Software Foundation
describe Cascading and MongoDB
list the layers of the Big Data Stack
list the common Big Data components
describe columnar databases and Hbase
describe solutions for scaling computing
describe the design principles of Hadoop
map out the functional view of Hadoop
describe the architecture of HDFS
describe the architecture of Yarn
describe the attributes and processes of MapReduce
describe the architecture of Spark
describe Big Data in a historical context and the tools available for working with Big Data
Big Data Opportunities and Challenges
start the course
list the team members in a Big Data team, and describe their interrelationship
list the team members in a Big Data business team, and describe their interrelationship
list the team members in a Big Data analytics team, and describe their interrelationship
list the team members in a Big Data architects team, and describe their interrelationship
list the team members in a Big Data Hadoop operators team, and describe their interrelationship
describe the factors impacting Big Data teams
describe DIY supercomputing Big Data projects
describe the use of Hadoop in cloud computing
describe data warehousing for Big Data
identify the key factors in the costs of Big Data
assess relational database management systems in the context of Big Data
assess Big Data infrastructure projects
describe data mining
describe Big Data recommendation engines
describe common use cases for Big Data analytics
describe the global impact of Big Data
describe the global increase in produced data volume
list the main companies involved in Big Data
identify Big Data business opportunities
describe the Big Data physical challenges to be overcome
describe the privacy and security challenges of Big Data
describe the required planning activities for Big Data
describe Big Data industry trends
describe and identify the challenges and opportunities presented by Big Data
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. Thelab enviromentconsist of completenetworks containing for example,clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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