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Batch Date: Nov 23rd & 24th @6:00PM

Faculty: Mr. N. Vijay Sunder Sagar (17+ Yrs Of Exp,..)

Duration: 15 Weekends Batch

Venue :
DURGA SOFTWARE SOLUTIONS,
Flat No : 202, 2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038

Ph. No: +91 - 9246212143, 80 96 96 96 96

Syllabus:

BIG DATA HADOOP

I: INTRODUCTION

• What is Big Data?
• What is Hadoop?
• Need of Hadoop
• Sources and Types of Data
• Comparison with Other Technologies
• Challenges with Big Data
  • i. Storage
  • ii. Processing
• RDBMS vs Hadoop
• Advantages of Hadoop
• Hadoop Echo System components

II: HDFS (Hadoop Distributed File System)

• Features of HDFS
• Name node ,Data node ,Blocks
• Configuring Block size,
• HDFS Architecture ( 5 Daemons)
  • i. Name Node
  • ii. Data Node
  • iii. Secondary Name node
  • iv. Job Tracker
  • v. Task Tracker
• Metadata management
• Storage and processing
• Replication in Hadoop
• Configuring Custom Replication
• Fault Tolerance in Hadoop
• HDFS Commands

III: MAP REDUCE

• Map Reduce Architecture
• Processing Daemons of Hadoop
  • Job Tracker (Roles and Responsibilities)
  • Task Tracker(Roles and Responsibilities)
• Phases of Map Reduce
  • i) Mapper phase
  • ii) Reducer phase
• Input split
• Input split vs Block size
• Partitioner in Map Reduce
• Groupings and Aggregations
• Data Types in Map Reduce
• Map Reduce Programming Model
  • Driver Code
  • Mapper Code
  • Reducer Code
• Programming examples
• File input formats
• File output formats
• Merging in Map Reduce
• Speculative Execution Model
• Speculative Job

IV: SQOOP (SQL + HADOOP)

• Introduction to Sqoop
• SQOOP Import
• SQOOP Export
• Importing Data From RDBMS to HDFS
• Importing Data From RDBMS to HIVE
• Importing Data From RDBMS to HBASE
• Exporting From HASE to RDBMS
• Exporting From HBASE to RDBMS
• Exporting From HIVE to RDBMS
• Exporting From HDFS to RDBMS
• Transformations While Importing / Exporting
• Filtering data while importing
• Vertical and Horizontal merging while import
• Working with delimiters while importing
• Groupings and Aggregations while import
• Incremental import
• Examples and operations
• Defining SQOOP Jobs

V: YARN

• Introduction
• Speculative Execution ,Speculative job and
• Speculative Task.
• Comparision of Hadoop1.xx with Hadoop2.xx
• Comparision with previous versions
• YARN Architecture Componets
  • i. Resource Manager
  • ii. Application Master
  • iii. Node Manager
  • iv. Application Manager
  • v. Resource Scheduler
  • vi. Job History Server
  • vii. Container

VI: NOSQL

• What is “Not only SQL”
• NOSQL Advantages
• What is problem with RDBMS for Large
• Data Scaling Systems
• Types of NOSQL & Purposes
• Key Value Store
• Columer Store
• Document Store
• Graph Store
• Introduction to cassandra – NOSQL Database
• Introduction to MongoDB and CouchDB Database
• Intergration of NOSQL Databases with Hadoop

VII: HBASE

• Introduction to big table
• What is NOSQL and colummer store Database
• HBASE Introduction
• Hbase use cases
• Hbase basics
• Column families
• Scans
• Hbase Architecture
• Map Reduce Over Hbase
• Hbase data Modeling
• Hbase Schema design
• Hbase CRUD operators
• Hive & Hbaseinteragation
• Hbase storage handlers

VIII: HIVE

• Introduction
• Hive Architecture
• Hive Metastore
• Hive Query Launguage
• Difference between HQL and SQL
• Hive Built in Functions
• Loading Data From Local Files To Hive Tables
• Loading Data From Hdfs Files To Hive Tables
• Tables Types
• Inner Tables
• External Tables
• Hive Working with unstructured data
• Hive Working With Xml Data
• Hive Working With Json Data
• Hive Working With Urls And Weblog Data
• Hive Unions
• Hive Joins
• Multi Table / File Inserts
• Inserting Into Local Files
• Inserting Into Hdfs Files
• Hive UDF (user defined functions)
• Hive UDAF (user defined Aggregated functions)
• Hive UDTF (user defined table Generated functions
• Partitioned Tables
• Non – Partitioned Tables
• Multi-column Partitioning
• Dynamic Partitions In Hive
• Performance Tuning mechanism
• Bucketing in hive
• Indexing in Hive
• Hive Examples
• Hive & Hbase Integration

 

PYSPARK

I ) PYSPARK INTRODUCTION

• What is Apache Spark?
• Why Pyspark?
• Need for pyspark
• spark Python Vs Scala
• pyspark features
• Real-life usage of PySpark
• PySpark Web/Application
• PySpark - SparkSession
• PySpark – SparkContext
• PySpark – RDD
• PySpark – Parallelize
• PySpark – repartition() vs coalesce()
• PySpark – Broadcast Variables
• PySpark – Accumulator

II) PYSPARK - RDD COMPUTATION

• Operations on a RDD
• Direct Acyclic Graph (DAG)
• RDD Actions and Transformations
• RDD computation
• Steps in RDD computation
• RDD persistence
• Persistence features

II) PERSISTENCE Options:

• 1) MEMORY_ONLY
• 2) MEMORY_SER_ONLY
• 3) DISK_ONLY
• 4) DISK_SER_ONLY
• 5) MEMORY_AND_DISK_ONLY

III) PYSPARK - CORE COMPUTING

• Fault Tolerence model in spark
• Different ways of creating a RDD
• Word Count Example
• Creating spark objects(RDDs) from Scala Objects(lists).
• Increasing the no of partitons
• Aggregations Over Structured Data:
• reduceByKey()

IV) GROUPINGS AND AGGREGATIONS

• i) Single Grouping and Single Aggregation
• ii) Single Grouping and multiple Aggregation
• iii) multi Grouping and Single Aggregation
• iv) Multi Grouping and Multi Aggregation
• Differences b/w reduceByKey() and groupByKey()
• Process of groupByKey
• Process of reduceByKey
• Reduce() function
• Various Transformations
• Various Built-in Functions

V) Various Actions and Transformations:

• countByKey()
• countByValue()
• sortByKey()
• zip()
• Union()
• Distinct()
• Various count aggregation
• Joins
• -inner join
• -outer join
• Cartesian()
• Cogroup()
• Other actions and transformations

VI) PySpark SQL - DataFrame

• Introduction
• Making data Structured
• Case Classes
• ways to extract case class objects
• 1) using function
• 2) using map with multiple exressions
• 3) using map with single expression
• Sql Context
• Data Frames API
• DataSet API
• RDD vs DataFrame vs DataSet
• PySpark – Create a DataFrame
• PySpark – Create an empty DataFrame
• PySpark – Convert RDD to DataFrame
• PySpark – Convert DataFrame to Pandas
• PySpark – show()
• PySpark – StructType & StructField
• PySpark – Row Class
• PySpark – Column Class
• PySpark – select()
• PySpark – collect()
• PySpark – withColumn()
• PySpark – withColumnRenamed()
• PySpark – where() & filter()
• PySpark – drop() & dropDuplicates()
• PySpark – orderBy() and sort()
• PySpark – groupBy()
• PySpark – join()
• PySpark – union() & unionAll()
• PySpark – unionByName()
• PySpark – UDF (User Defined Function)
• PySpark – map()
• PySpark – flatMap()
• pyspark – foreach()
• PySpark – sample() vs sampleBy()
• PySpark – fillna() & fill()
• PySpark – pivot() (Row to Column)
• PySpark – partitionBy()
• PySpark – ArrayType Column (Array)
• PySpark – MapType (Map/Dict)

VII) PySpark SQL Functions

• PySpark – Aggregate Functions
• PySpark – Window Functions
• PySpark – Date and Timestamp Functions
• PySpark – JSON Functions
• PySpark – Read & Write JSON file

VIII) PySpark Built-In Functions

• PySpark – when()
• PySpark – expr()
• PySpark – lit()
• PySpark – split()
• PySpark – concat_ws()
• Pyspark – substring()
• PySpark – translate()
• PySpark – regexp_replace()
• PySpark – overlay()
• PySpark – to_timestamp()
• PySpark – to_date()
• PySpark – date_format()
• PySpark – datediff()
• PySpark – months_between()
• PySpark – explode()
• PySpark – array_contains()
• PySpark – array()
• PySpark – collect_list()
• PySpark – collect_set()
• PySpark – create_map()
• PySpark – map_keys()
• PySpark – map_values()
• PySpark – struct()
• PySpark – countDistinct()
• PySpark – sum(), avg()
• PySpark – row_number()
• PySpark – rank()
• PySpark – dense_rank()
• PySpark – percent_rank()
• PySpark – typedLit()
• PySpark – from_json()
• PySpark – to_json()
• PySpark – json_tuple()
• PySpark – get_json_object()
• PySpark – schema_of_json()
• Working Examples

IX) Pyspark External Sources

• Working with sql statements
• Spark and Hive Integration
• Spark and mysql Integration
• Working with CSV
• Working with JSON
• Transformations and actions on dataframes
• Narrow, wide transformations
• Addition of new columns, dropping of columns ,renaming columns
• Addition of new rows, dropping rows
• Handling nulls
• Joins
• Window function
• Writing data back to External sources
• Creation of tables fromDataframes (Internal tables, Temporary tables)

X) DEPLOYMENT MODES

• Local Mode
• Cluster Modes(Standalone , YARN

XI) PYSPARK APLLICATION

• Stages and Tasks
• Driver and Executor
• Building spark applications/pipelines
• Deploying spark apps to cluster and tuning
• Performance tuning

PySpark Streaming Concepts

Integration with Kafka

PySpark-mllib

PYTHON

1. Python Basics

• What is Python
• Why Python?
• History of python
• Applications of Python
• Features of Python
• Advantages of Python
• Versions of Python
• Installation of Python
• Flavors of Python
• Comparision b/w various programming languages C, Java and Python

2. Python Operations

• Python Modes of Execution
• Interactive mode of Execution
• Batch mode of Execution
• Python Editors and IDEs
• Python Data Types
• Python Constants
• Python Variables
• Comments in python
• Output Print(),function
• Input() Function :Accepting input
• Type Conversion
• Type(),Id() Functions
• Comments in Python
• Escape Sequences in Python
• Strings in Python
• String indices and slicing

3. Operators in Python

• Arithmetic Operators
• Comparision Operators
• Logical Operators
• Assignment Operators
• Short Hand Assignment Operators
• Bitwise Operators
• Membership Operators
• Identity Operators

4. Python IDE’s

• Pycharm IDE Installation
• Working with Pycharm
• Pycharm components
• Installing Anaconda
• What is Conda?
• Anaconda Prompt
• Anaconda Navigator
• Jupyter Notebook
• Jupyter Features
• Spyder IDE
• Spyder Featueres
• Conda and PIP

5. Flow Control statements

• Block/clause
• Indentation in Python
• Conditional Statements
  • if stmt
  • if…else statement
  • if…elif…statement

6. Looping Statements

• while loop,
• while … else,
• for loop
• Range() in for loop
• Nested for loop
• Break statememt
• Continue statement
• Pass statement

7. Strings in Python

• Creating Strings
• String indexing
• String slicing
• String Concatenation
• String Comparision
• String splitting and joining
• Finding Sub Strings
• String Case Change
• Split strings
• String methods

8. Collections in Python

• Introduction
• Lists
• Tuples
• Sets
• Dictionaries
• Operations on collections
• Functions for collections
• Methods of collection
• Nested collections
• Differences b/w list tuple and set and Dictionary

9. Python Lists

• List properties
• List Creation
• List indexing and slicing
• List Operations
• List addresses
• List functions
• Different ways of creating lists
• Nested Lists
• List modification
• List insertion and deletion
• List Methods

10. Python Tuples

• Tuple properties
• Tuple Creation
• Tuple indexing and slicing
• Different ways of creating tuples
• Tuple Operations
• Tuple Addresses
• Tuple Functions
• Nested Tuples
• Tuple Methods
• Differences b/w List and Tuple

11. Python Sets

• Set properties
• Set Creation
• Set Operations
• Set Functions
• Set Addresses
• Set Mathematical Operations
• Set Methods
• Insertion and Deletion operation

12. Python Dictionary

• Dictionary properties
• Dictionary Creation
• Dictionary Operations
• Dictionary Addresses
• Nested Dictionaries
• Dictionary Methods
• Insertion and Deletion of elements
• Differences b/w list tuple and set and Dictionary

13. Functions in Python

• Defining a function
• Calling a function
• Properties of Function
• Examples of Functions
• Categories of Functions
• Argument types
  • default arguments
  • non-default arguments
  • keyword arguments
  • non keyword arguments
• Variable Length Arguments
• Variables scope
• Call by value and Call by Reference
• Passing collections to function
• Local and Global variables
• Recursive Function
• Boolean Function
• Passing functions to function
• Anonymous or Lamda function
• Filter() and map() functions
• Reduce Function

14. Modules in Python

• What is a module?
• Different types of module
• Creating user defined module
• Setting path
• The import statement
• Normal Import
• From … Import
• Module Aliases
• Reloading a module
• Dir function
• Working with Standard modules -Math, Random, Date time and os modules,

15. Packages

• Introduction to packages
• Defining packages
• Importing from packages
• --init--.py file
• Defining sub packages
• Importing from sub packages

16. Errors and Exception Handling

• Types of errors
• Compile-Time Errors
• Run-Time Errors
• What is Exception?
• Need of Exception handling
• Predefined Exceptions
• Try,Except, finally blocks
• Nested blocks
• Handling Multiple Exceptions
• User defined Exceptions
• Raise statement

17. File Handling

• Introduction
• Types of Files in Python
• Opening a file
• Closing a file
• Writing data to files
• Tell( ) and seek( ) methods
• Reading a data from files
• Appending data to files
• With open stmt
• Various functions

18. OOPs Concepts

• OOPS Features
• Encapsulation
• Abstraction
• Class
• Object
• Static and non static variables
• Defining methods
• Diff b/w functions & methods
• Constructors
• Parameterized Constructors
• Built –in attributes
• Object Reference count
• Destructor
• Garbage Collection
• Inheritance
• Types of Inheritances
• Object class
• Polymorphism
• Over riding
• Super() statement

19. Regular Expressions

• What is regular expression?
• Special characters
• Forming regular expression
• Compiling regular expressions
• Grouping
• Findall() function
• Finditer() function
• Sub() function
• Match() function
• Search() function
• Matching vs searching
• Splitting a string
• Replacing text
• validations

20. Database Access

• Introduction
• Installing mysql database
• Creating database users,
• Installing Oracle Python modules
• Establishing connection with mysql
• Closing database connections
• Connection object
• Cursor object
• Executing SQL queries
• Retrieving data from Database.
• Using bind variables executing
• SQL queries
• Transaction Management
• Handling errors

21. Python Date and Time

• How to Use Date &DateTime Class
• Time and date Objects
• Calendar in Python
• The Time Module
• Python Calendar Module

22. Operating System Module

• Introduction
• getcwd
• listdir
• chdir
• mkdir
• rename file/dir
• remove file/dir
• rmtree()
• Os help
• Os operations

23. Advanced concepts

• Python Iterator
• Python Generator
• Python closure
• Python Decorators
• Web Scraping
• PIP
• Working with CSV files
• Working with XML files
• Working with JSON files
• Debugging

24. GUI Programming (tkinter)

• Introduction
• Components and events
• Root window
• Labels
• Fonts and colors
• Buttons, checkbox
• Label widget
• Message widget
• Text widget
• Radio button
• image

25. Excel Workbook

• Installing and working with Xlsx writer
• Creating Excel Work book
• Inserting into excel sheet
• Insetting data into multiple excel sheets
• Creating headers
• Installing and working with xlrd module
• Reading a specific cell or row or column
• Reading specific rows and columns

26. Data Analytics

• Introduction
• pandas module
• Numpy module
• Matplotlib module
• Working Examples

27. Introduction to Datascience

• Machine Learning Introduction
• Datasets
• Supervised /Unsupervised Learning
• Statistical Analysis
• Data Analysis
• Uni-variate/multi-variate analysis
• Corelation Analysis
• Algorithm types
• Applications

28. Python Pandas

• Introduction to Pandas
• Creating Pandas Series
• Creating Data Frames
• Pandas Data Frames from dictionaries
• Pandas Data Frames from list
• Pandas Data Frames from series
• Pandas Data Frames from CSV, Excel
• Pandas Data Frames from JSON
• Pandas Data Frames from Databases
• Pandas Data Functionality
• Pandas Timedelta
• Creating Data Frames from Timedelta
• Pandas Groupings and Aggregations
• Converting Data Frames from list
• Creating Functions
• Converting Different Formats
• Pandas and Matplotlib
• Pandas usecases

29. Python Numpy

• Introduction to Numpy
• Numpy Arrays
• Numpy Array Indexing
• 2-D and 3Dimensional Arrays
• Numpy Mathematical operations
• Numpy Flattening and reshaping
• Numpy Horizontal and Vertical Stack
• Numpy linespace and arrange
• Numpy asarray and Random numbers
• Numpy iterations and Transpose
• Numpy Array Manipulation
• Numpy and matplotlib
• Numpy Linear Algebra
• Numpy String Functions
• Numpy operations and usecases
• Numpy Working Examples

30. Python Matplotlib

• Introduction to matplotlib
• Installing matplotlib
• Generating graphs
• Normal plottings
• Generating Bargraphs
• Histograms
• Scatter plots
• Stack plots
• Pie plots
• Matplotlib working examples