Big Data Hadoop Training in Melbourne

2.1 Introducing Big Data and Hadoop
2.2 What is Big Data and where does Hadoop fit in?
2.3 Two important Hadoop ecosystem components, namely, MapReduce and HDFS
2.4 In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager

Hands-on Exercise:

1. HDFS working mechanism
2. Data replication process
3. How to determine the size of the block?
4. Understanding a data node and name node

3.1 Learning the working mechanism of MapReduce
3.2 Understanding the mapping and reducing stages in MR
3.3 Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort

Hands-on Exercise:

1. How to write a WordCount program in MapReduce?
2. How to write a Custom Partitioner?
3. What is a MapReduce Combiner?
4. How to run a job in a local job runner
5. Deploying a unit test
6. What is a map side join and reduce side join?
7. What is a tool runner?
8. How to use counters, dataset joining with the map side, and reduce side joins?

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

Hands-on Exercise:

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?

5.1 Indexing in Hive
5.2 The Map Side Join in Hive
5.3 Working with complex data types
5.4 The Hive user-defined functions
5.5 Introduction to Impala
5.6 Comparing Hive with Impala
5.7 The detailed architecture of Impala

Hands-on Exercise: 

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

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

Hands-on Exercise: 

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

7.1 Apache Sqoop introduction
7.2 Importing and exporting data
7.3 Performance improvement with Sqoop
7.4 Sqoop limitations
7.5 Introduction to Flume and understanding the architecture of Flume
7.6 What are HBase and the CAP theorem?

Hands-on Exercise: 

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

8.1 Using Scala for writing Apache Spark applications
8.2 Detailed study of Scala
8.3 The need for Scala
8.4 The concept of object-oriented programming
8.5 Executing the Scala code
8.6 Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
8.7 The Java and Scala interoperability
8.8 The concept of functional programming and anonymous functions
8.9 Bobsrockets package and comparing the mutable and immutable collections
8.10 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.

Hands-on Exercise:

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

9.1 Introduction to Scala packages and imports
9.2 The selective imports
9.3 The Scala test classes
9.4 Introduction to JUnit test class
9.5 JUnit interface via JUnit 3 suite for Scala test
9.6 Packaging of Scala applications in the directory structure
9.7 Examples of Spark Split and Spark Scala

10.1 Introduction to Spark
10.2 Spark overcomes the drawbacks of working on MapReduce
10.3 Understanding in-memory MapReduce
10.4 Interactive operations on MapReduce
10.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
10.6 The overview of Spark and how it is better than Hadoop
10.7 Deploying Spark without Hadoop
10.8 Spark history server and Cloudera distribution

11.1 Spark installation guide
11.2 Spark configuration
11.3 Memory management
11.4 Executor memory vs. driver memory
11.5 Working with Spark Shell
11.6 The concept of Resilient Distributed Datasets (RDD)
11.7 Learning to do functional programming in Spark
11.8 The architecture of Spark

12.1 Spark RDD
12.2 Creating RDDs
12.3 RDD partitioning
12.4 Operations and transformation in RDD
12.5 Deep dive into Spark RDDs
12.6 The RDD general operations
12.7 Read-only partitioned collection of records
12.8 Using the concept of RDD for faster and efficient data processing
12.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

13.1 Understanding the concept of key-value pair in RDDs
13.2 Learning how Spark makes MapReduce operations faster
13.3 Various operations of RDD
13.4 MapReduce interactive operations
13.5 Fine and coarse-grained update
13.6 Spark stack

14.1 Comparing the Spark applications with Spark Shell
14.2 Creating a Spark application using Scala or Java
14.3 Deploying a Spark application
14.4 Scala built application
14.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
14.6 Creating an application using SBT
14.7 Deploying an application using Maven
14.8 The web user interface of Spark application
14.9 A real-world example of Spark
14.10 Configuring of Spark

15.1 Working towards the solution of the Hadoop project solution
15.2 Its problem statements and the possible solution outcomes
15.3 Preparing for the Cloudera certifications
15.4 Points to focus on scoring the highest marks
15.5 Tips for cracking Hadoop interview questions

Hands-on Exercise:

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

16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions

17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Hands-on Exercise: 

1. Building a Recommendation Engine

19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.5 Operations
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka

Hands-on Exercise: 

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

20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators

Hands-on Exercise: 

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

21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems

22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine

23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions