There is no pre-requisite to take this Big Data training and to master Hadoop. But basics of UNIX, SQL and java would be good. At Intellipaat, we provide complimentary UNIX and Java course with our Big Data certification training to brush-up the required skills so that you are good on you Hadoop learning path.
Sydney is the economic and financial hub for the world’s top-notch firms in the entire Asia-Pacific Region. Growing at a swift pace, this city is filled with the companies which are investing heavily into research and analytics. As Big Data is driving most of the industries nowadays, the application of technologies like Hadoop is rising rapidly. Therefore there is a huge scope of Hadoop professionals in this city.
Sydney is one of the most developed cities in Australia inviting investors from across the globe. As data analytics has emerged as a vital operation over past few years, the companies have started paying extra attention towards extracting meaningful insights from these data. As it is possible only through skilled Big Data professionals, the trend for job opportunities is constantly going upwards in this city.
Big data has become the definite way to success in this highly digitized technology world. Since Hadoop is a prominent name in this direction, learning this technology will help the candidates launch their careers in this space.
As part of this training the learners will be carrying out 9 real-time projects based on Hadoop and its components like MapReduce, Hive, Spark, Pig, Oozie, Flume, Weblog analytics, etc. Moreover this training course helps you prepare for CCA175 and CCAH exams through practical lab sessions, assignments and interactive sessions.
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
It is a known fact that the demand for Hadoop professionals far outstrips the supply. So if you want to learn and make a career in Hadoop then you need to enroll for the Intellipaat Hadoop course which is the most recognized name in Hadoop training and certification. Intellipaat Hadoop training includes all the major components of Big Data and Hadoop like Apache Spark, MapReduce, HBase, HDFS, Pig, Sqoop, Flume, Oozie, and more. The entire Intellipaat Hadoop training has been created by industry professionals. You will get 24/7 lifetime support, high quality course material and videos, free upgrade to latest version of course material. Thus, it is clearly a one-time investment for a lifetime of benefits.
This training course is designed to help you clear Cloudera Spark and Hadoop Developer Certification (CCA175) exam. The entire training course content is in line with these two certification programs and helps you clear these certification exams 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 Course Completion Certificate will be awarded on the completion of Project work (on expert review) and upon 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.
This training course is designed to help you clear Cloudera Spark and Hadoop Developer Certification (CCA175) exam.Intellipaat enjoys strong relationship with 80+ MNCs across the globe. We have a dedicated team who will help you with your resume building once you complete the course and your resume will be forwarded to partner MNCs. Intellipaat don’t charge any extra fees for passing the resume to our partners and clients
"PMI®", "PMP®" and "PMI-ACP®" are registered marks of the Project Management Institute, Inc.
The Open Group®, TOGAF® are trademarks of The Open Group.
The Swirl logoTM is a trade mark of AXELOS Limited.
ITIL® is a registered trade mark of AXELOS Limited.
PRINCE2® is a Registered Trade Mark of AXELOS Limited.
Certified ScrumMaster® (CSM) and Certified Scrum Trainer® (CST) are registered trademarks of SCRUM ALLIANCE®
Professional Scrum Master is a registered trademark of Scrum.org