Master in Big Data Hadoop Course

• The architecture of Hadoop cluster
• What is High Availability and Federation?
• How to setup a production cluster?
• Various shell commands in Hadoop
• Understanding configuration files in Hadoop
• Installing a single node cluster with Cloudera Manager
• Understanding Spark, Scala, Sqoop, Pig, and Flume

• Introducing Hadoop Hive
• Detailed architecture of Hive
• Comparing Hive with Pig and RDBMS
• Working with Hive Query Language
• Creation of a database, table, group by and other clauses
• Various types of Hive tables, HCatalog
• Storing the Hive Results, Hive partitioning, and Buckets

• 1. Database creation in Hive
• 2. Dropping a database
• 3. Hive table creation
• 4. How to change the database?
• 5. Data loading
• 6. Dropping and altering table
• 7. Pulling data by writing Hive queries with filter conditions
• 8. Table partitioning in Hive
• 9. What is a group by clause?

• Indexing in Hive
• The ap 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

• 1. How to work with Hive queries?
• 2. The process of joining the table and writing indexes
• 3. External table and sequence table deployment
• 4. Data storage in a different table

• Apache Pig introduction and its various features
• Various data types and schema in Hive
• The available functions in Pig, Hive Bags, Tuples, and Fields

• 1. Working with Pig in MapReduce and local mode
• 2. Loading of data
• 3. Limiting data to 4 rows
• 4. Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive

• Apache Sqoop introduction
• 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?

• 1. Working with Flume to generate Sequence Number and consume it
• 2. Using the Flume Agent to consume the Twitter data
• 3. Using AVRO to create Hive Table
• 4. AVRO with Pig
• 5. Creating Table in HBase
• 6. 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 programming
• Executing the Scala code
• 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 and comparing the mutable and immutable collections
• Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.

• 1. Writing Spark application using Scala
• 2. Understanding the robustness of Scala for Spark real-time analytics operation

• Detailed Apache Spark and its various features
• Comparing with Hadoop
• Various Spark components
• Combining HDFS with Spark and Scalding
• Introduction to Scala
• Importance of Scala and RDD

• 1. The Resilient Distributed Dataset (RDD) in Spark
• 2. How does it help 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 a Key/Value pair?

• 1. How to deploy RDD with HDFS?
• 2. Using the in-memory dataset
• 3. Using file for RDD
• 4. How to define the base RDD from an external file?
• 5. Deploying RDD via transformation
• 6. Using the Map and Reduce functions
• 7. 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 Hive Context
• Writing Dataframe 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?
• 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 frames provide the benefits of both Spark RDD and Spark SQL?
• Deploying Hive on Spark as the execution engine

• 1. Data querying and transformation using Data Frames
• 2. Finding out the benefits of Data Frames over Spark SQL and Spark RDD

• Introduction to Spark MLlib
• Understanding 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?
• Various ML algorithms supported by MLlib
• Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

• 1. Building a recommendation engine

• Why Kafka?
• What is Kafka?
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Basic operations
• Kafka monitoring tools
• Integrating Apache Flume and Apache Kafka

• 1. Configuring Single Node Single Broker Cluster
• 2. Configuring Single Node Multi Broker Cluster
• 3. Producing and consuming messages
• 4. Integrating Apache Flume and Apache Kafka.

• 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
• Working with advanced data sources
• Features of Spark streaming
• Spark Streaming workflow
• Initializing StreamingContext
• Discretized Streams (DStreams)
• Input DStreams and Receivers
• Transformations on DStreams
• Output Operations on DStreams
• Windowed operators and its uses
• Important Windowed operators and Stateful operators

• 1. Twitter Sentiment analysis
• 2. Streaming using Netcat server
• 3. Kafka-Spark streaming
• 4. Spark-Flume streaming

• Create a 4-node Hadoop cluster setup
• Running the MapReduce Jobs on the Hadoop cluster
• Successfully running the MapReduce code
• Working with the Cloudera Manager setup

• 1. The method to build a multi-node Hadoop cluster using an Amazon EC2 instance
• 2. Working with the Cloudera Manager

• 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

• 1. The process of performance tuning in MapReduce

• Introduction to the checkpoint procedure, name node failure
• How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes

• 1. How to go about ensuring the MapReduce File System Recovery for different scenarios
• 2. JMX monitoring of the Hadoop cluster
• 3. How to use the logs and stack traces for monitoring and troubleshooting
• 4. Using the Job Scheduler for scheduling jobs in the same cluster
• 5. Getting the MapReduce job submission flow
• 6. FIFO schedule
• 7. Getting to know the Fair Scheduler and its configuration

• How ETL tools work in the 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

• 1. Connecting to HDFS from ETL tool
• 2. Moving data from Local system to HDFS
• 3. Moving data from DBMS to HDFS,
• 4. Working with Hive with ETL Tool
• 5. Creating MapReduce 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 on scoring the highest marks
• Tips for cracking Hadoop interview questions

• 1. The project of a real-world high value Big Data Hadoop application
• 2. Getting the right solution based on the criteria set by the Intellipaat team

Importance of testing

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, and 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 and 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.), reporting defects to the development team or manager and driving them to closure

• Consolidating 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 MRUnit for testing of MapReduce programs

• Automation testing using the OOZIE
• Data validation using the query surge tool

• Test plan for HDFS upgrade
• Test automation and result

• Test, install and configure