This is a combo course that includes the complete individual courses in Hadoop Developer Training, Hadoop Analyst Training, Hadoop Administration Training, Hadoop Testing Training, Data Science, Apache Spark, and Scala Programming.
This is a fully career-oriented training course exclusively in collaboration with IBM. You will get a 360 degree overview of the Hadoop architecture, its constituent components, the high speed processing systems like Apache Scala. Master the concepts of HDFS, MapReduce, HBase Architecture, Hadoop administration and maintenance. You will get hands-on experience in Data Science, the powerful R programming language, R integration with Hadoop, Data Science overview, Data Analysis and Statistical methods.
Get the coveted Big Data IBM Certification after successfully completing the course.
There is a serious shortage of Data Scientists and this is a major concern for Top MNCs around the world. A Data Scientist is really a catch-all term that is used to signify someone who can work with huge amounts of data, convert it into nuggets of information and deploy it in business scenarios for higher growth and revenues. This course will equip you with some of the top skills needed to successfully straddle the Hadoop and Data Science domains with consummate ease.
Be proficient in Hadoop, high speed data processing, Data Analytics and Data Science. The IBM certification at the end of the training course will prepare you to take on some of the most coveted jobs in the Big Data Hadoop and Data Science worlds.
What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x
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
What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,
Understanding Apache Pig, the features, various uses and learning to interact with Pig
The syntax of Pig Latin, the various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions, functions commonly used.
Various data types including nested and complex, processing data with Pig, grouped data iteration, practical exercise
Data set joining, data set splitting, various methods for data set combining, set operations, hands-on exercise
Understanding user defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig, practical exercises
Working with real data sets involving Walmart and Electronic Arts as case study
Understanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of Hive
Understanding HiveQL, basic syntax, the various tables and databases, data types, data set joining, various built-in functions, deploying Hive queries on scripts, shell and Hue.
The various databases, creation of databases, data formats in Hive, data modeling, Hive-managed Tables, self-managed Tables, data loading, changing databases and Tables, query simplification with Views, result storing of queries, data access control, managing data with Hive, Hive Metastore and Thrift server.
Learning performance of query, data indexing, partitioning and bucketing
Deploying user defined functions for extending Hive
Deploying Hive for huge volumes of data sets and large amounts of querying
Working extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.
What is Impala?, How Impala Differs from Hive and Pig, How Impala Differs from Relational Databases, Limitations and Future Directions, Using the Impala Shell
Data Storage Overview, Creating Databases and Tables, Loading Data into Tables, HCatalog, Impala Metadata Caching
Partitioning Overview, Partitioning in Impala and Hive
Selecting a File Format, Tool Support for File Formats, Avro Schemas, Using Avro with Hive and Sqoop, Avro Schema Evolution, Compression
What is Hbase, Where does it fits, What is NOSQL
What is Spark, Comparison between Spark and Hadoop, Components of Spark
Apache Spark- Introduction, Consistency, Availability, Partition, Unified Stack Spark, Spark Components, Scalding example, mahout, storm, graph
Explain python example, Show installing a spark, Explain driver program, Explaining spark context with example, Define weakly typed variable, Combine scala and java seamlessly, Explain concurrency and distribution., Explain what is trait, Explain higher order function with example, Define OFI scheduler, Advantages of Spark, Example of Lamda using spark, Explain Mapreduce with example
Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
Putting it all together and Connecting Dots, Working with Large data sets, Steps involved in analyzing large data
How ETL tools work in Big data Industry, 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, End to End ETL PoC showing big data integration with ETL tool.
Configuration overview and important configuration file, Configuration parameters and values, HDFS parameters MapReduce parameters, Hadoop environment setup, ‘Include’ and ‘Exclude’ configuration files, Lab: MapReduce Performance Tuning
Namenode/Datanode directory structures and files, File system image and Edit log, The Checkpoint Procedure, Namenode failure and recovery procedure, Safe Mode, Metadata and Data backup, Potential problems and solutions / what to look for, Adding and removing nodes, Lab: MapReduce File system Recovery
Best practices of monitoring a cluster, Using logs and stack traces for monitoring and troubleshooting, Using open-source tools to monitor the cluster
How to schedule Jobs on the same cluster, FIFO Schedule, Fair Scheduler and its configuration
Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster
ZOOKEEPER Introduction, ZOOKEEPER use cases, ZOOKEEPER Services, ZOOKEEPER data Model, Znodes and its types, Znodes operations, Znodes watches, Znodes reads and writes, Consistency Guarantees, Cluster management, Leader Election, Distributed Exclusive Lock, Important points
Why Oozie?, Installing Oozie, Running an example, Oozie- workflow engine, Example M/R action, Word count example, Workflow application, Workflow submission, Workflow state transitions, Oozie job processing, Oozie security, Why Oozie security?, Job submission, Multi tenancy and scalability, Time line of Oozie job, Coordinator, Bundle, Layers of abstraction, Architecture, Use Case 1: time triggers, Use Case 2: data and time triggers, Use Case 3: rolling window
Overview of Apache Flume, Physically distributed Data sources, Changing structure of Data, Closer look, Anatomy of Flume, 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: Overview, User view of Impala: SQL, User view of Impala: Apache HBase, Impala architecture, Impala state store, Impala catalogue service, Query execution phases, Comparing Impala to Hive
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
Cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques
Introduction to Data Science, importance of Data Science, statistical and analytical methods, deploying Data Science for Business Intelligence, transforming data, machine learning and introduction to Recommender systems.
How Data Science solves real world problems, Data Science Project Life Cycle, principles of Data Science, introduction to various BI and Analytical tools, data collection, introduction to statistical packages, data visualization tools, R Programming, predictive modelling, machine learning, artificial intelligence and statistical analysis.
Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data
Descriptive statistics, Let us understand some terms in statistics, Variable
Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots
What is probability?, Set & rules of probability, Bayes Theorem
Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution
Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling
Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables
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 – Working with MapReduce, Hive, Sqoop
This project is involved with working on the various Hadoop components like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import data from relational database management system like MySQL data into HDFS. Deploy Hive for summarizing data, querying and analysis. Convert SQL queries using HiveQL for deploying MapReduce on the transferred data. You will gain considerable proficiency in Hive, and Sqoop after completion of this project.
Project 2 – Work on MovieLens data for finding top records
Data – MovieLens dataset
In this project you will work exclusively on data collected through MovieLens available rating data sets. The project involves the following important components:
Project 3 – Hadoop YARN Project – End to End PoC
In this project you will work on a live Hadoop YARN project. YARN is part of the Hadoop 2.0 ecosystem that lets Hadoop to decouple from MapReduce and deploy more competitive processing and wider array of applications. You will work on the YARN central Resource Manager. The salient features of this project include:
Project 4 – Partitioning Tables in Hive
This project involves working with Hive table data partitioning. Ensuring the right partitioning helps to read the data, deploy it on the HDFS, and run the MapReduce jobs at a much faster rate. Hive lets you partition data in multiple ways like:
This will give you hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, bucketing of data so as to break it into manageable chunks.
Project 5 – Connecting Pentaho with Hadoop Ecosystem
This project lets you connect Pentaho with the Hadoop ecosystem. Pentaho works well with HDFS, HBase, Oozie and Zookeeper. You will connect the Hadoop cluster with Pentaho data integration, analytics, Pentaho server and report designer. Some of the components of this project include the following:
Project 6 – Multi-node cluster setup
This is a project that gives you opportunity to work on real world Hadoop multi-node cluster setup in a distributed environment. The major components of this project involve:
You will get a complete demonstration of working with various Hadoop cluster master and slave nodes, installing Java as a prerequisite for running Hadoop, installation of Hadoop and mapping the nodes in the Hadoop cluster.
Project 7 – Hadoop Testing using MR
In this project you will gain proficiency in Hadoop MapReduce code testing using MRUnit. You will learn about real world scenarios of deploying MRUnit, Mockito, and PowerMock. Some of the important aspects of this project include:
After completion of this project you will be well-versed in test driven development and will be able to write light-weight test units that work specifically on the Hadoop architecture.
Project 8 – Hadoop Weblog Analytics
Data – Weblogs
This project is involved with making sense of all the web log data in order to derive valuable insights from it. You will work with loading the server data onto a Hadoop cluster using various techniques. The various modules of this project include:
The web log data can include various URLs visited, cookie data, user demographics, location, date and time of web service access, etc. In this project you will transport the data using Apache Flume or Kafka, workflow and data cleansing using MapReduce, Pig or Spark. The insight thus derived can be used for analyzing customer behavior and predict buying patterns.
Project 9 – Hadoop Maintenance
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 – Understanding Cold Start Problem in Data Science
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 following:
Project 2 – 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 provider 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:
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.
Project 1: Movie Recommendation
Topics – This is a project wherein you will gain hands-on experience in deploying Apache Spark for movie recommendation. You will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a machine learning library. Understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon completion of the project you will gain experience in working with streaming data, sampling, testing and statistics.
Project 2: Twitter API Integration for tweet Analysis
Topics – With this project you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.
Project 3: Data Exploration Using Spark SQL – Wikipedia data set
Topics – This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real time analysis of data, performing batch analysis, deploying machine learning, creating visualizations and processing of graphs.
Intellipaat is the pioneer of Big Data, Hadoop, Data Science training. This is an all-in-one Data Science, Hadoop, Spark, Scala training course that has been designed in exclusive collaboration with IBM.
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.
Intellipaat basically offers the self-paced training and online instructor-led training. Apart from that we also provide corporate training for enterprises. All our trainers come with over 12 years of industry experience in relevant technologies and also they are subject matter experts working as consultants. You can check about the quality of our trainers in the sample videos provided.
If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers. The best part is that you can contact Intellipaat even after completion of training to get support and assistance. There is also no limit on the number of queries you can raise when it comes to doubt clearance and query resolution.
Yes, you can learn Hadoop without being from a software background. We provide complimentary courses in Java and Linux so that you can brush up on your programming skills. This will help you in learning Hadoop technologies better and faster.
The Intellipaat self-paced training is for people who want to learn at their own leisurely pace. As part of this program we provide you with one-on-one sessions, doubt clearance over email, 24/7 Live Support, 1yr of cloud access and lifetime LMS and upgrade to the latest version at no extra cost. The prices of self-paced training can be 75% lesser than online training. While studying should you face any unexpected challenges then we shall arrange a Virtual LIVE session with the trainer.
We provide you with the opportunity to work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various Hadoop components making you perfectly industry-ready. These projects could be in exciting and challenging fields like banking, insurance, retail, social networking, high technology and so on. The Intellipaat projects are equivalent to six months of relevant experience in the corporate world.
Yes, Intellipaat does provide you with placement assistance. We have tie-ups with 80+ organizations including Ericsson, Cisco, Cognizant, TCS, among others that are looking for Hadoop professionals and we would be happy to assist you with the process of preparing yourself for the interview and the job.
Yes, if you would want to upgrade from the self-paced training to instructor-led training then you can easily do so by paying the difference of the fees amount and joining the next batch of classes which shall be separately notified to you.
Upon successful completion of training you have to take a set of quizzes, complete the projects and upon review and on scoring over 60% marks in the qualifying quiz the official Intellipaat verified certificate is awarded.The Intellipaat Certification is a seal of approval and is highly recognized in 80+ corporations around the world including many in the Fortune 500 list of companies.
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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