With Codec Networks Big Data & Hadoop trainings, gain skills in data-driven business strategy and learn tools / techniques to Big Data Hadoop technology falls into four major roles: analysts, scientists, developer and administrator, its anticipated to grow by five-fold in next few years and will sense an increased temptation of great job prospects with big data sector.
Big Data or Hadoop is often characterized by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. Big Data has grown in significance over the last few years because of the evasiveness of its application, across areas ranging from weather forecasting to analyzing business trends, fighting crime and preventing epidemics etc. Big data sets are so large that traditional data management tools are incapable of analyzing all the data effectively and processing valuable information out of it. Hadoop is an open source java framework that enables distributed parallel processing of large volume of data across servers which has emerged as the solution to extract potential value from all this data.
The need for big data velocity imposes unique demands on the underlying compute infrastructure. The computing power required to quickly process huge volumes and varieties of data can overwhelm a single server or server cluster. Organizations must apply adequate compute power to big data tasks to achieve the desired velocity. This can potentially demand hundreds or thousands of servers that can distribute the work and operate collaboratively.
Administrator Training course for Apache Hadoop provides participants with a comprehensive understanding of all the steps necessary to operate and maintain a Hadoop cluster. The course topics include Introduction to Hadoop and its Architecture, MapReduce and HDFS and MapReduce Abstraction. From installation and configuration through load balancing and tuning, this training course is the best preparation for the real-world challenges faced by Hadoop administrators. It further covers best practices to configure, deploy, administer, maintain, monitor and troubleshoot a Hadoop Cluster.
After completing this course, student will be able to:
This course is best suited to systems administrators and IT managers who have basic Linux experience. Fundamental knowledge of any programming language and Linux environment. Participants should know how to navigate and modify files within a Linux environment. Prior knowledge of Apache Hadoop is not required.
Data scientists build information platforms to provide deep insight and answer previously unimaginable questions. Spark and Hadoop are transforming how data scientists work by allowing interactive and iterative data analysis at scale. Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities.
This Big-Data and Hadoop Science using Spark course helps participants understand what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, and develop concrete skills such as:
This course is suitable for developers, data analysts, and statisticians with basic knowledge of Apache Hadoop: HDFS, MapReduce, Hadoop Streaming, and Apache Hive as well as experience working in Linux environments.
Students should have proficiency in a scripting language; Python is strongly preferred, but familiarity with Perl or Ruby is sufficient.
Apache Hive makes multi-structured data accessible to analysts, database administrators, and others without Java programming expertise. Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Impala enables real-time, interactive analysis of the data stored in Hadoop via a native SQL environment.
This data analyst training course focusing on Apache Pig, Hive and Impala will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:
This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command-line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential.
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework. Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System.
The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.[11] Other projects in the Hadoop ecosystem expose richer user interfaces.
This Developer training course for Hadoop Trainings delivers the key concepts and expertise necessary to create robust data processing applications using Apache Hadoop.
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:
This course is intended and appropriate for developers who will be writing, maintaining, or optimizing Hadoop jobs Participants should have programming experience, preferably with Java. Understanding of common computer science concepts is a plus.
