Cloud computing and Virtualization

Course Pre-requisites:

1. Linux Operating System basic knowledge
2. Networking basics
3. Storage basics
4. Web Services basics

Cloud Computing and Virtualization from Hyderabad

Course Content

1. Cloud Computing concepts
   • What is cloud computing?
   • Infrastructure as a Service (IaaS)
   • Platform as a Service (PaaS)
   • Amazon Web Services (AWS)
   • Software as a Service (PaaS)OpenStack Open source Cloud Community
   • Public, Private and Hybrid Clouds
   • HP, Rackspace, Cisco, Dell and other vendors clouds overview
2. Virtualization concepts
   • Server Virtualization
   • Types of Virtualization
   • Virtual machines
   • Citrix Xen
   • Redhat KVM
   • VMware ESX
3. Storage Virtualization
   • SAN - Storage Area Networking basics
   • ISCSI Concepts
4. Network Virtualization
   • Virtual Switch & Openvswitch concepts
   • Tunnels & Bridges
   • VLAN Networks
5. Deep dive into OpenStack Cloud
   • Keystone Authentication
   • Glance Image Store
   • Nova Compute & Nova API
   • Neutron Networking
   • Cinder Block storage
   • Swift Object Storage
   • Access Object Storage etc

Lab Sessions

• Labs with Redhat OpenStack
• Installing All-in-One and Multi-node OpenStack setups
• Configuring Flat, Flat DHCP, VLAN, VxLAN network configurations
• Create Project, Users, Tenants, Services, API Endpoints etc.
• Create VM’s with KVM hypervisor, assign Floating IP’s
• Create and Mount Cinder volumes

Takeaway from this course

• The course provides thorough understanding big picture of Cloud computing and Virtualization technologies
• With this knowledge participant should be able to comfortably adopt to any vendor cloud platform in the market
• Cloud is the emerging technology with very promising job market

HADOOP-HIVE

Course objective

You can work on Hive programming

Course Content

1. Hive Introduction
   • What is hive
   • Inside Hive
2. Hive Data types and File Format
   • Primitive Data Types
   • Collection Data Types
   • Text File encoding of Data Types
4. Hive Data Defination
5. • Database in Hive
   • Alter Database
   • Creating Tables
   • Partitend and Managed Tables
   • Dropping Tables
   • Alter Tables
6. Hive Data manipulation
   • Loading data into Managed Table
   • Inserting data into Tables and Queries
   • Creating tables and loading them in one query
   • Exporting Query output
7. Hive QL: Queries
   • Select .. From clause
   • Where
   • Group By
   • Join Statements
   • Order By and Sort By
   • Distribute By with Sort By
   • Cluster By
   • Casting
   • Union All
8. Hive QL: Views
   • Views to reducte query complexity
   • Views that restrict data based on conditions
   • View and MapType for For Dynamic Tables
   • View Odds and Ends
9. Hive QL : Indexes
   • Creating Index
   • Rebuilding Index
   • Showing an Index
   • Dropping an Index
10. Schema Design
    • Table by Day
    • Over Parturitions
    • Uniques Keys and Normalization
    • Making multiple Passes over Same Data
    • Partitioning every table
    • Buckenting Table Data storage
    • Adding columns to Tables
    • Using Column table
    • Almost always Compression
11. Tuning
    • Using EXPLAIN
    • EXPLAIN EXTENDED
    • Limit Tuning
    • Optimized Joins
    • Local Mode
    • Parallel Execution
    • Strict Mode
    • Tuning the Number of Mappers and Reducers
    • JVM Reuse
    • Indexes
    • Dynamic Partition Tuning
    • Speculative Execution
    • Single MapReduce MultiGROUP BY
    • Virtual Columns
12. Hive - Functions
    • Discovering and Describing Functions
    • Calling Functions
    • Standard Functions
    • Aggregate Functions
    • Table Generating Functions
    • A UDF for Finding a Zodiac Sign from a Day
    • UDF Versus GenericUDF
    • Permanent Functions
    • User-Defined Aggregate Functions
    • Creating a COLLECT UDAF to Emulate GROUP_CONCAT
    • User-Defined Table Generating Functions
    • UDTFs that Produce Multiple Rows
    • UDTFs that Produce a Single Row with Multiple Columns
    • UDTFs that Simulate Complex Types
    • Accessing the Distributed Cache from a UDF
    • Annotations for Use with Functions
    • Deterministic
    • Stateful
    • DistinctLike
    • Macros
13. Customizing hive file record formats
    • File Versus Record Formats
    • Demystifying CREATE TABLE Statements
    • File Formats
    • SequenceFile
    • RCFile
    • Example of a Custom Input Format: DualInputFormat
    • Record Formats: SerDes
    • CSV and TSV SerDes
    • ObjectInspector
    • Think Big Hive Reflection ObjectInspector
    • XML UDF
    • XPath-Related Functions
    • JSON SerDe
    • Avro Hive SerDe
    • Defining Avro Schema Using Table Properties
    • Defining a Schema from a URI
    • Evolving Schema
    • Binary Output

Course Takeaway

WebLogic Automation | Python | Jython | WLST

The WebLogic Automation is a special course which could make you Automation specialist as Oracle Fusion middleware suites required it. Increase your career growth and improve your hikes by doing better in your infrastructure as a code and DevOps.

Duration 30hrs (18 days aprox)
Availability : Weekend or Daily
Class mode  : in-house, online

Part - 1: Python, Jython Programming basics

1. Introduction Python, Jython, WLST
• What is WebLogic Automation?
• History of WLST
• WLST program structure
• features of WLST

Datatypes

• Number, bool, int, float, chr
• Sequances: tuple, List, string, dictionaries

Basic elements

• variables
• if-elif-else
• while loop
• for loop - filtered, nested loops

Functions & modules in WLST

• function structure
• arugument- positional, named
• calling function in assignment, expression, main
• lambda function
• import modules
• magic functions, __init__.py

Error & Exception Handling

• Error Hierarchy
• Error Handling
• Exception Handling
• User defined exception - raise

OOP in WLST

• What is Class?
• Initialization, methods: str, repr, init
• Abstraction using underscore
• Encapsulation
• Inheritance
• Polymorphism

File IO

• Reading file
• Writing to file, appending
• csv file creation

Regular Expressions

• match
• compile
• meta-char, Meta-class
• samples

Part - 2: WLST Scripting

1. WLST Basics
   • Two-Phase Configuration changes
   • Admin tools
   • Offline
   • Online
   • Properties
2. WebLogic Domain
   • New Domain
   • Extending domain
   • Reading Templates
   • Adding Template for extending domain
   • Write Template reusability
3. WL Environment
   • Managed Server
   • Machines
   • Cluster
4. JDBC
   • Generic DS
   • Multi DS
   • [GridLink DS]
5. JMS
   • JMS server
   • JMS Module
   • Connection Factory
   • Queue
   • Topic
   • [Bridges two domains
   • Foreign Server]
6. Server Life Cycle
   • Start Stop using Nodemanager
   • without Nodemanager
   • Server Status
7. Application Deployment
   • deploy
   • undeploy
   • stop
   • WLST invoke from ANT
   • start
   • status
   • side-by-side
8. Monitoring
   • JMS
   • JDBC Datasource
   • ThreadPool
   • JVM Runtime
   • WLDF
9. Best practices
   • Server Credentials store
   • NodeManager Credentials Store
   • Tricks & tips

Course takeaway is heavy and you could enjoy the automation fun.

HADOOP Development

We offer realtime project in HADOOP Development project work for a team of students. It also include the Hadoop administration, NOSQL, R, etc

HADOOP REALTIME PROJECTS

Hadoop with Cascading Course

1. Introduction
   • Motivation for Hadoop
   • Problems with Traditional
   • Large-Scale Systems
   • Requirements for a New Approach
   • Introducing Hadoop
2. Hadoop basics
   • Hadoop Components
   • HDFS
   • How Map Reduce works
   • How Hadoop cluster operates
   • Other Hadoop Eco systems"
3. Cascading programming
   • Data processing
   • Pipe Assemblies
   • Pipes
   • Platforms
   • Source & Sink Taps
   • Sink Modes
   • Fields Sets
   • Flows
   • Cascading
4. Executing process on Hadoop
   • "Building
   • Configuring
   • Executing
   • Debugging
5. Developing operations
   • Functions
   • Filters
   • Aggregator
   • Buffer
   • Operations and Base Operations
6. Advanced processing
   • "Sub Assemblies
   • Failture Taps
   • Checkpoint taps
   • Partial aggregation instead of combiners
7. Built in operations
   • Identify Function
   • Debug Function
   • Sample and Limit Functions
   • Insert Functions
   • Test Functions
8. Built in Assemblies
   • Aggregate By
   • Coerce
   • Discard
   • Rename
   • Retain
   • Unique
9. Best practices
10. Hive
    • High level understanding of Hive