Hadoop 2.x Cluster Architecture, Federation and High Availability, A Typical Production Cluster setup, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster, Hive, Pig, Sqoop, Flume, Scala and Spark.
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 – Working with HDFS, replicating the data, determining block size, familiarizing with Namenode and Datanode.
Detailed understanding of the working of MapReduce, the mapping and reducing process, the working of Driver, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sor
Hands-on Exercise – The detailed methodology for writing the Word Count Program in MapReduce, writing custom partitioner, MapReduce with Combiner, Local Job Runner Mode, Unit Test, ToolRunner, MapSide Join, Reduce Side Join, Using Counters, Joining two datasets using Map-Side Join &Reduce-Side Join
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 – Creating of Hive database, how to drop database, changing the database, creating of Hive table, loading of data, dropping the table and altering it, writing hive queries to pull data using filter conditions, group by clauses, partitioning Hive tables
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 – Working with Hive queries, writing indexes, joining table, deploying external table, sequence table and storing data in another 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.
Introduction to Apache Sqoop, Sqoop overview, basic imports and exports, how to improve Sqoop performance, the limitation of Sqoop, introduction to Flume and its Architecture, introduction to HBase, 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.
The RDD operation in Spark, the Spark transformations, actions, data loading, comparing with MapReduce, 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, reading of JDBC files, the importance of Data Frames in Spark, creating Data Frames, schema manual inferring, working with CSV files, reading of JDBC tables, converting from Data Frame to JDBC, the user-defined functions in Spark SQL, 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.
Different Algorithms, the concept of iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning, various variables in Spark like shared variables, broadcast variables, learning about 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, File system image and Edit log
Hands-on Exercise – The method to do performance tuning of MapReduce program.
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 IBM 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 IBM project of a real-world high value Big Data Hadoop application and getting the right solution based on the criteria set by the IBM 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 Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science
Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case
Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions
Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram
Terminologies of Statistics ,Measures of Centers, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA
Supervised Learning – Linear Regression ,Bivariate Regression, Multiple Regression Analysis, Correlation( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories
What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix
Random Forest, What is Naive Bayes?
Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics.
The importance of Scala, the concept of REPL (Read Evaluate Print Loop), deep dive into Scala pattern matching, type interface, higher order function, currying, traits, application space and Scala for data analysis.
Learning about the Scala Interpreter, static object timer in Scala, testing String equality in Scala, Implicit classes in Scala, the concept of currying in Scala, various classes in Scala.
Learning about the Classes concept, understanding the constructor overloading, the various abstract classes, the hierarchy types in Scala, the concept of object equality, the val and var methods in Scala.
Understanding Sealed traits, wild, constructor, tuple, variable pattern, and constant pattern.
Understanding traits in Scala, the advantages of traits, linearization of traits, the Java equivalent and avoiding of boilerplate code.
Implementation of traits in Scala and Java, handling of multiple traits extending.
Introduction to Scala collections, classification of collections, the difference between Iterator, and Iterable in Scala, example of list sequence in Scala.
The two types of collections in Scala, Mutable and Immutable collections, understanding lists and arrays in Scala, the list buffer and array buffer, Queue in Scala, double-ended queue Deque, Stacks, Sets, Maps, Tuples in Scala.
Introduction to Scala packages and imports, the selective imports, the Scala test classes, introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test, packaging of Scala applications in Directory Structure, example of Spark Split and Spark Scala.
Introduction to Spark, how Spark overcomes the drawbacks of working MapReduce, understanding in-memory MapReduce, Spark Hadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better Hadoop, deploying Spark without Hadoop.
Spark installation guide, working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark.
Deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing.
Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD.
Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application, the web user interface of Spark application, a real world example of Spark and configuring of Spark.
Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations.
Understanding the RDD persistence overview, distributed persistence, RDD lineage
Understanding the Spark streaming, creating a Spark stream application, processing of Spark stream, streaming request count and DStreams.
Introduction to Spark multi-batch operations, state operations, sliding window operations and advanced data sources.
Learning about the Spark common use cases, the concept of iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning.
Introduction to various variables in Spark like shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.
Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, understanding the Data Frames in Spark, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.
Learning about the scheduling and partitioning in Spark, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling, Spark master high availability, standby Masters with Zookeeper, Single Node Recovery With Local File System, High Order Functions.
Understanding how to design capacity planning in Spark, creation of Maps, Transformations, the concept of concurrency in Java and Scala.
Understanding about log analysis with Spark, first log analyzers in Spark, working with various buffers like array, compact and protocol buffer.
Project 1 : Augmenting retail sales with Data Science
Industry : Retail
Problem Statement : How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data.
Topics : In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.
Project 2 : Increasing revenues of a retail enterprise using Data Science tools
Industry : Retail
Problem Statement : How to successfully convert visitors into buyers for retail enterprise
Topics : In this Data Science project you will learn how to reduce the churn rate, recommend the right items for the customers for the ecommerce website and place the right products in the physical store. You will learn how to optimize the limited budget for marketing using the right Data Science techniques.
Project 3 : Analyzing pre-paid model of stock broking
Industry : Finance
Problem Statement: Finding out the deciding factor for people to opt for the pre-paid model of stock broking.
Topics:In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.
Project 4 : Cold Start Problem in Data Science
Problem Statement: how to build a recommender system without the historical data available
Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.
Project 5 : Recommendation for Movie, Summary
Topics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:
Project 6 : Making sense of customer online buying pattern
Industry : ecommerce
Problem Statement : An ecommerce company wants to know how to deploy targeted selling to its customers
Description : This Data Science project involves learning from the customer buying habits and selling them the products that they need. As part of the project you will aggregate, cleanse, transform and load the data of customer buying history. Then you will deploy statistical analysis, predictive modeling and create profiles of customers to implement targeted selling to them.
Project 7 : Fraud Detection in Banking System
Industry : Banking and Finance
Problem Statement : A major bank wants to deploy data science to detect fraudulent activities and take remedial actions before it is too late
Description : This data science project will help you understand how you can look for fraudulent activities in a banking ecosystem. You will work with banking transactional data, look for outliers in the data, classify this data based on various parameters, apply statistics and come up with inferences to look for rogue transactions and mitigate the risk before it is too late.
The Market Basket Analysis (MBA) case study
This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
Intellipaat is the pioneer of Big Data, Hadoop, Data Science training. This Intellipaat all-in-one Combo course exclusively trains you in the most sought-after domains in Big Data. You will gain hands-on experience in mastering Big Data Hadoop ecosystem, Data Science concepts, programming languages, Apache Spark, Scala, data analysis, statistical computing and probability. The entire training course content is fully aligned towards clearing the following Big Data and Data Science certification exams: CCA Spark and Hadoop Developer (CCA175), Cloudera Certified Administrator for Apache Hadoop (CCAH), Cloudera Data Scientist certification (CCP:DS). This is a completely career-oriented Big Data Hadoop and Data Scientist training and it is designed by industry experts. Your training program includes real time projects, step-by-step assignments to evaluate your progress and specially designed quizzes for clearing the requisite certification exams.
Intellipaat also offers lifetime access to videos, course materials, 24/7 Support, and course material upgrades to latest version at no extra fees. For Hadoop and Spark training you get the Intellipaat Proprietary Virtual Machine for Lifetime and free cloud access for 6 months for performing training exercises. Hence it is clearly a one-time investment.
This course is designed for clearing the following certification exams:
Cloudera Spark and Hadoop Developer Certification (CCA175)
Cloudera CCA Administrator Exam (CCA131)
The entire training course content is in line with respective certification program and helps you clear the requisite certification exam with ease and get the best jobs in the top MNCs.
As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.
At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
Intellipaat Data Science Certification and Course Completion certificate will be awarded on the completion of Project work (upon expert review) and on scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
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