Big Data Hadoop Certification Training – Sydney, Australia

1. Basic fundamentals of Hadoop
2. Writing applications on YARN
3. Learning to work on Hadoop components like HDFS, Hive, pig, MapReduce, Spark, Oozie, Flume, etc.
4. Configuring Pseudo-node and Multi-node clusters
5. Managing, monitoring, administering and troubleshooting the Hadoop cluster
6. Working with Avro data formats
7. Learning to work on Spark, Spark RDD, MLlib and GraphX
8. Understanding the concepts of Big Data Analytics
9. In-depth study of testing hadoop applications

• Programming Developers and System Administrators
• Experienced working professionals , Project managers
• Big Data Hadoop Developers eager to learn other verticals like Testing, Analytics, Administration
• Mainframe Professionals, Architects & Testing Professionals
• Business Intelligence, Data warehousing and Analytics Professionals
• Graduates, undergraduates eager to learn the latest Big Data technology can take this Big Data Hadoop Certification online training

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.  

Bangalore, Melbourne, Chicago, Hyderabad, San Francisco, London, Toronto, New York, India, Los Angeles, Dubai, Pune, Houston, Singapore, Delhi, Mumbai, Chennai, Jersey City, San Jose

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 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