Hadoop Architect and Advanced Hadoop Administration Training: Combo Course

Learn to analyze, extract and compute large volumes of structured and unstructured data using Hadoop Architect and Advanced Administration Key Features: This is a combo course including: Hadoop Developer Training Hadoop Analyst Training Hadoop Administration Training Hadoop Testing Training Advanced Hadoop Administration 77 hours of high-quality in-depth video e-learning sessions 154 hours of lab exercises Intellipaat proprietary VM for lifetime and free cloud access for 6 months for performing exercises 70% of extensive learning through hands-on exercises, project works, assignments and quizzes The training will prepare you for Cloudera Spark and Hadoop Developer Certification (CCA175) and Cloudera CCA Administrator Exam (CCA131) You will also learn how to work with Hortonworks and MapReduce distributions 24/7 lifetime support with guaranteed rapid problem resolution Lifetime access to videos, tutorials and course material Guidance to resume preparation and job assistance Learning the step-by-step installation of the software Course Completion Certificate from Intellipaat About Hadoop Architect + Advance Hadoop Administration Training Course It is an all-in-one course designed to give a 360-degree overview of Hadoop architecture and its implementation on real-time projects, along with the advanced concepts of Hadoop administration. Major topics include Hadoop and its ecosystem, core concepts of MapReduce and HDFS, introduction to HBase architecture, Hadoop cluster setup and Hadoop administration and maintenance. The course further covers the main components of Hadoop and its Manager, Web Server, Hive, Pig, Oozie, Flume, Hue, Impala, Hadoop Security and Kerberos and ZooKeeper. Learning Objectives: After the completion of this Hadoop all-in-one course, you will be able to: Excel in the Concepts of Hadoop Distributed File System (HDFS) Implement HBase and MapReduce Integration Understand Data Science Project Lifecycle, Data Acquisition and Data Collection Execute Various Machine Learning Algorithms Understand Apache Hadoop 2.7 Framework and Architecture Write complex MapReduce Programs in Both MRv1 and MRv2 Design and Develop Applications Involving Large Data Using Hadoop Ecosystem Understand Prediction and Analysis Segmentation through Clustering Learn the Basics of Big Data and Ways to Integrate R with Hadoop Learn Various Advanced Modules like Yarn, Flume, Hive, Oozie, Impala, ZooKeeper and Hue Set Up Hadoop Infrastructure with Single- and Multi-node Clusters Using Amazon EC2 (CDH4) Monitor a Hadoop Cluster and Execute Routine Administration Procedures Understand Hadoop Architecture and Various Hadoop Managers Including Node Manager, Container Manager and Resource Manager Learn Web Application Server and Log Handler Gain an In-depth Understanding of Hadoop Security: Identify Security Threats and Solving Them Work with Secure Clusters and Know How to Set Up Kerberos Understand Hadoop HDFS over HTTPS and How Does HTTPS Work Project Works: Hadoop Projects 1. Project: Working with Map Reduce, Hive and Sqoop Problem Statement: It describes how to import MySQL data using Sqoop and querying it using Hive and also describes how to run the word count MapReduce job. 2. Project: Work on MovieLens data for finding top records Data: MovieLens dataset Problem Statement: It includes: Writing a MapReduce program to find the top 10 movies from the u.data file Creating the same top 10 movies using Pig by loading u.data into Pig Creating the same top 10 movies using Hive by loading u.data into Hive 3. Project: Hadoop Yarn Project – End-to-End PoC Problem Statement: It includes: Import Movie data Append the data How to use Sqoop commands to bring the data into the HDFS End-to-end flow of transaction data How to process the real word data or a huge amount of data using MapReduce program in terms of the movie etc. 4. Project: Partitioning Tables Problem Statement: It describes the parting and how to perform portioning, which includes: Manual Partitioning Dynamic Partitioning Bucketing 5. Project: Sales Commission Data: Sales Problem Statement: In this project, you will calculate the commission according to the sales. 6. Project: Connecting Pentaho with Hadoop Ecosystem Problem Statement: It includes: Quick Overview of ETL and BI Configuring Pentaho to work with Hadoop Distribution Loading data into Hadoop cluster Transforming data into Hadoop cluster Extracting data from Hadoop Cluster 7. Project: Multi-node Cluster Setup Problem Statement: It includes following actions: Hadoop multi-node cluster setup using Amazon EC2 and creating four-node cluster setup Running MapReduce jobs on a cluster 8. Project: Hadoop Testing Using MR Problem Statement: It describes how to test MapReduce codes with MR unit. 9. Project: Hadoop Weblog Analytics Data: Weblogs Problem Statement: The goal is to enable participants to have a feel of the actual data sets in a production environment and to load the data into a Hadoop cluster using various techniques. Once data is loaded, the next goal is to perform basic analytics on this data. 2. Advanced Hadoop Admin Project: Hadoop Maintenance Problem Statement: It includes: Hadoop maintenance Name node directory structure Secondary name node Secondary name node directory structure Data node directory structure Safe mode Safe mode properties Entering and leaving safe mode Audit logging DFS Admin File system check Data node blocks scanner Balancer HDFS federation HDFS high availability Failover Fencing DISTCP File formats in Hadoop Recommended Audience: Programming Developers, System Administrators and ETL Developers Project Managers eager to learn new techniques of maintaining large data Experienced working professionals aiming to become Big Data Analysts Professionals aiming to build a career in real-time Data Analytics with Apache Storm techniques and Hadoop computing Professionals aspiring to be a Data Scientist Information Architects aspiring to gain expertise in Predictive Analytics domain Mainframe Professionals, Architects and Testing Professionals Graduates eager to learn the latest Big Data technologies Prerequisites: Prior knowledge and experience in any programming knowledge will be beneficial Basic knowledge of UNIX and SQL Scripting Why to take Hadoop Architect and Advanced Admin combo course? Hadoop is a combination of online running applications on a large-scale built of commodity hardware. It is handled by Apache Software Foundation and is helpful in handling and storing huge amounts of data in a cost-effective manner. Big multinational companies like Google, Yahoo, Apple, eBay, Facebook and many others are hiring skilled professionals capable of handling Big Data. Experts in Hadoop can manage complete operations in an organization. This course provides hands-on exercises on end-to-end POC using Yarn or Hadoop 2.7 You will be equipped with advanced MapReduce exercises including examples of Facebook, Sentiment Analysis, LinkedIn shortest path algorithm and Inverted indexing. Read More