Learn to analyze, extract and compute large volumes of structured and unstructured data using Hadoop Architect and Advanced Administration
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 advanced concepts of Hadoop Administration. The major topics include Hadoop and its Ecosystem, core concepts of MapReduce and HDFS, Introduction to HBase Architecture, Hadoop Cluster Setup, Hadoop Administration and Maintenance. The course further covers main components of Hadoop and its Manager, Web Server, Hive, Pig, Oozie, Flume, Hue, Impala, Hadoop Security and Kerberos, Zookeeper and Hadoop Maintenance.
After completion of this Hadoop all-in-one course, you will be able to:
1. Hadoop Projects
1. Project – Working with Map Reduce, Hive, Sqoop
Problem Statement – It describes that how to import MySQL data using sqoop and querying it using hive and also describes that how to run the word count MapReduce job.
2. Project – Work on Movie lens data for finding top records
Data – Movie Lens dataset
Problem Statement – It includes:
3. Project – Hadoop Yarn Project – End to End PoC
Problem Statement – It includes:
4. Project – Partitioning Tables
Problem Statement – It describes the parting and how to perform portioning. It includes:
Data – Sales
Problem Statement – In this we calculate the commission according to the sales.6. Project – Connecting Pentaho with Hadoop Ecosystem
Problem Statement – It includes:
7. Project – Multi-node Cluster Setup
Problem Statement – It includes following actions:
8. Project – Hadoop Testing using MR
Problem Statement – It describes that how to test map reduce codes with MR unit.
9. Project – Hadoop Weblog Analytics
Data – Weblogs
Problem Statement – The goal is to enable the participants to have a feel of the actual data sets in a production environment and how 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. Advance Hadoop Admin Project – Hadoop Maintenance
Problem Statement – It includes:
The architecture of Hadoop 2.0 cluster, what is High Availability and Federation, how to setup a production cluster, the various shell commands in Hadoop, understanding configuration files in Hadoop 2.0, installing single node cluster with Cloudera Manager, understanding Spark, Scala, Sqoop, Pig and Flume.
Introducing Big Data & Hadoop, what is Big Data and where does Hadoop fits in, two important Hadoop ecosystem componentsnamely Map Reduce and HDFS, in-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability, in-depth YARN – Resource Manager, Node Manager.
Hands-on Exercise – HDFS working mechanism, data replication process, how to determine the size of the block, understanding a DataNode and NameNode.
Learning the working mechanism of MapReduce, understanding the mapping and reducing stages in MR, the various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle and Sort
Hands-on Exercise – How to write a Word Count program in MapReduce, how to write a custom Partitioner, what is a MapReduce Combiner, how to run a job in a local job runner, deploying unit test, what is a map side join and reduce side join, what is a tool runner, how to use counters, dataset joining with map side and reduce side joins.
Introducing Hadoop Hive, detailed architecture of Hive, comparing Hive with Pig and RDBMS, working with Hive Query Language, creation of database, table, Group by and other clauses, the various types of Hive tables, Hcatalog, storing the Hive Results, Hive partitioning and Buckets.
Hands-on Exercise – Database creation in Hive, dropping a database, Hive table creation, how to change the database, data loading, Hive table creation, dropping and altering table, pulling data by writing Hive queries with filter conditions, table partitioning in Hive, what is a group by clause
The indexing in Hive, the Map side Join in Hive, working with complex data types, the Hive User-defined Functions, Introduction to Impala, comparing Hive with Impala, the detailed architecture of Impala
Hands-on Exercise – How to work with Hive queries, the process of joining table and writing indexes, external table and sequence table deployment, data storage in a different table.
Apache Pig introduction, its various features, the various data types and schema in Hive, the available functions in Pig, Hive Bags, Tuples and Fields.
Hands-on Exercise – Working with Pig in MapReduce and local mode, loading of data, limiting data to 4 rows, storing the data into file, working with Group By,Filter By,Distinct,Cross,Split in Hive.
Apache Sqoop introduction, overview, importing and exporting data, performance improvement with Sqoop, Sqoop limitations, introduction to Flume and understanding the architecture of Flume, what is HBase and the CAP theorem.
Hands-on Exercise – Working with Flume to generating of Sequence Number and consuming it, using the Flume Agent to consume the Twitter data, using AVRO to create Hive Table, AVRO with Pig, creating Table in HBase, deploying Disable, Scan and Enable Table.
Using Scala for writing Apache Spark applications, detailed study of Scala, the need for Scala, the concept of object oriented programing, executing the Scala code, the various classes in Scala like Getters,Setters, Constructors, Abstract ,Extending Objects, Overriding Methods, the Java and Scala interoperability, the concept of functional programming and anonymous functions, Bobsrockets package, comparing the mutable and immutable collections.
Hands-on Exercise – Writing Spark application using Scala, understanding the robustness of Scala for Spark real-time analytics operation.
Detailed Apache Spark, its various features, comparing with Hadoop, the various Spark components, combining HDFS with Spark, Scalding, introduction to Scala, importance of Scala and RDD.
Hands-on Exercise – The Resilient Distributed Dataset in Spark and how it helps to speed up big data processing.
Understanding the Spark RDD operations, comparison of Spark with MapReduce, what is a Spark transformation, loading data in Spark, types of RDD operations viz. transformation and action, what is Key Value pair.
Hands-on Exercise – How to deploy RDD with HDFS, using the in-memory dataset, using file for RDD, how to define the base RDD from external file, deploying RDD via transformation, using the Map and Reduce functions, working on word count and count log severity.
The detailed Spark SQL, the significance of SQL in Spark for working with structured data processing, Spark SQL JSON support, working with XML data, and parquet files, creating HiveContext, writing Data Frame to Hive, How to read a JDBC file, significance of a Spark Data Frame, how to create a Data Frame, what is schema manual inferring, how to work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC, Spark SQL user-defined functions. shared variable and accumulators, how to query and transform data in Data Frames, how Data Frame provides the benefits of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.
Hands-on Exercise – Data querying and transformation using Data Frames, finding out the benefits of Data Frames over Spark SQL and Spark RDD.
Introduction to Spark MLlib, understanding the various algorithms, what is Spark iterative algorithm, Spark graph processing analysis, introducing machine learning, K-Means clustering, Spark variables like shared and broadcast variables, what are accumulators.
Hands-on Exercise – Writing spark code using Mlib.
Introduction to Spark streaming, the architecture of Spark Streaming, working with the Spark streaming program, processing data using Spark streaming, requesting count and Dstream, multi-batch and sliding window operations and working with advanced data sources.
Hands-on Exercise – Deploying Spark streaming for data in motion and checking the output is as per the requirement.
Create a four node Hadoop cluster setup, running the MapReduce Jobs on the Hadoop cluster, successfully running the MapReduce code, working with the Cloudera Manager setup.
Hands-on Exercise – The method to build a multi-node Hadoop cluster using an Amazon EC2 instance, working with the Cloudera Manager.
The overview of Hadoop configuration, the importance of Hadoop configuration file, the various parameters and values of configuration, the HDFS parameters and MapReduce parameters, setting up the Hadoop environment, the Include’ and Exclude configuration files, the administration and maintenance of Name node, Data node directory structures and files, What is a File system image, understanding Edit log.
Hands-on Exercise – The process of performance tuning in MapReduce.
Introduction to the Checkpoint Procedure, Name node failure and how to ensure the recovery procedure, Safe Mode, Metadata and Data backup, the various potential problems and solutions, what to look for, how to add and remove nodes.
Hands-on Exercise – How to go about ensuring the MapReduce File system Recovery for various different scenarios, JMX monitoring of the Hadoop cluster, how to use the logs and stack traces for monitoring and troubleshooting, using the Job Scheduler for scheduling jobs in the same cluster, getting the MapReduce job submission flow, FIFO schedule, getting to know the Fair Scheduler and its configuration.
How ETL tools work in Big data Industry, Introduction to ETL and Data warehousing. Working with prominent use cases of Big data in ETL industry, End to End ETL PoC showing big data integration with ETL tool.
Hands-on Exercise – Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool
Working towards the solution of the Hadoop project solution, its problem statements and the possible solution outcomes, preparing for the Cloudera Certifications, points to focus for scoring the highest marks, tips for cracking Hadoop interview questions.
Hands-on Exercise – The project of a real-world high value Big Data Hadoop application and getting the right solution based on the criteria set by the Intellipaat team.
Why testing is important, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing
Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges etc), Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Validating new feature and issues in Core Hadoop.
Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.
Automation testing using the OOZIE, Data validation using the query surge tool.
Test plan for HDFS upgrade, Test automation and result
How to test install and configure
Introduction to advance Hadoop admin concepts, learning about the concepts of Applications, Node, Resource Manager components, connecting of RM to nodes, introduction to container manager in advanced Hadoop, monitoring of Containers, executing Containers, node status updater and node manager, log handler, Token Secret Managers, per application interacting components, learning about the Web Server security, administrating the clusters, the web application proxy server.
Learning about the Apache Hive and Pig, the various Hive services, clients, understanding the Managed Tables and External Table, the functions of Apache Pig, the concepts of partitioning and buckets.
Introduction to Hadoop security with Kerberos authentication, the various security threats in Hadoop and its solutions, securing the HDFS on huge clusters, understanding the three step Kerberos ticketing protocol, Kerberos setup steps, securing a Hadoop cluster, key distribution center installation, setting Kerberos client on Hadoop nodes, creating and distributing Key tab files in Hadoop services, setting up Hadoop service principles, configuration files of Hadoop, deploying Hoop for HDFS over HTTP, learning how HTTPFS works and how HDFS proxy differs, understanding the Cloudera Sentry, its salient features, the Apache Knox and the Knox gateway server.
Introduction to Apache Zookeeper, a distributed coordination service for distributed applications, the various applications of Zookeeper, the services offered, its data model, understanding the Znodes and its varieties, the various features of Zookeeper like Znodes watches, reads, writes, managing of cluster, maintaining consistency, electing a leader in Zookeeper ensemble, mutually exclusive distributed lock.
The importance of Oozie workflow scheduler, Oozie installation, understanding the workflow engine, deep dive into Oozie workflow, the workflow application, submissions, state transitions, processing of job with Oozie, learning of Oozie security on Hadoop, submitting jobs to Hadoop, the concept of multi-tenancy and scalability, Oozie job timelines, the various layers of abstraction, its architecture and coordinator, data and time triggers.
Introduction to Apache Flume, Big data ecosystem, Physically distributed Data sources, Changing structure of Data, the Anatomy of Flume, its Core concepts, Event, Clients, Agents, Source, Channels, Sinks, Interceptors, Channel selector, Sink processor, Data ingest, Agent pipeline, Transactional data exchange, Routing and replicating, Why channels?, Use case- Log aggregation, Adding flume agent, Handling a server farm, Data volume per agent, Example describing a single node Flume deployment.
HUE introduction, HUE ecosystem, What is HUE?, HUE real world view, Advantages of HUE, How to upload data in File Browser?, View the content, Integrating users, Integrating HDFS, Fundamentals of HUE FRONTEND.
IMPALA Overview, Goals, User view of Impala: SQL, Apache HBase, Impala architecture, Impala state store, Impala catalogue service, Query execution phases, Comparing Impala to Hive.
This project is involved with working on the Hadoop cluster for maintaining and managing it. You will work on a number of important tasks like:
Project 1. Working with Map Reduce, Hive, Sqoop
Problem Statement – It describes that how to import mysql data using sqoop and querying it using hive and also describes that how to run the word count mapreduce job.
Project 2. Multinode Cluster Setup
Problem Statement –It includes following actions:
This course is designed for clearing Cloudera Spark and Hadoop Developer Certification (CCA175). At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.
This course is designed for clearing Cloudera CCA Administrator Exam (CCA131). At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.
You will get Lifetime access to high quality interactive tutorials along with life time access to complete Course Material .There will be 24/7 access to video tutorials with email support. If you stuck in any unexpected problem we will provide online interactive sessions with trainer for issue resolving.
We provide 24X7 support by email for issues or doubts clearance for Self-paced training.
In online Instructor led training, trainer will be available to help you out with your queries regarding the course. If required, the support team can also provide you live support by accessing your machine remotely. This ensures that all your doubts and problems faced during labs and project work are clarified round the clock.
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