Allied Health Training Programs
Admin / 09 Mar 2017
Please enter keywords
POWERFUL CERTIFICATION
FOR POWERFUL PEOPLE
Certification Training is our specialty. Courses are for certificate preparation purposes only. These courses are NOT associated with WIOA/WDP or any other State or Federal sponsored employment training.
-
-
Veterans Training Programs
Admin / 09 Mar 2017
-
AVTECH Current Classes
Admin / 09 Mar 2017
Recent Posts
Course Introduction
Corporations, institutions and government agencies today are generating massive amounts of data that are too large and too unwieldy to fit in relational databases. They are turning to massively parallel computing solutions such as Apache Hadoop for help. Hadoop is a framework, with Hadoop Distributed File System (HDFS) and MapReduce (M/R) framework at its core, which allows for the distributed processing of massive data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop has established itself as an industry-leading platform for deploying cloud-based applications and services. The Hadoop eco-system is large, and it includes such popular products as HBase, Zookeeper, Oozie, Pig, and Hive. However, with such versatility comes complexity and difficulty in deciding on appropriate use cases. This course breaks down the walls of complexity of the Hadoop eco-system and distributed processing of Big Data by providing a practical approach to developing applications on top of the Hadoop platform.
Having background in Java language, such as multi-threading, serialization, JAVA I/O, and JAVA Collection API. Having Basic Knowledge of Object-Oriented concepts mandatory. Unix experience is helpful. The course consists of an approximately equal mixture of lecture and hands-on lab time. The course assumes that all students already have at least moderate previous Java experience, but not necessarily any experience with Hadoop or cloud computing. Although the course will use Java 7, previous experience with earlier Java versions is sufficient.
Software Engineers and Team Leads
Project Managers
Business Analysts
DBAs and Data Engineering teams
Business Customers
System Analysts
Project Managers
Business Analysts
DBAs and Data Engineering teams
Business Customers
System Analysts
This course will enable you to:
Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
Get an overview of Sqoop and Flume and describe how to ingest data using them
Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
Gain a working knowledge of Pig and its components
Do functional programming in Spark
Understand resilient distribution datasets (RDD) in detail
Implement and build Spark applications
Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
Understand the common use-cases of Spark and the various interactive algorithms
Learn Spark SQL, creating, transforming, and querying Data frames
Prepare for Cloudera Big Data CCA175 certification
Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
Get an overview of Sqoop and Flume and describe how to ingest data using them
Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
Gain a working knowledge of Pig and its components
Do functional programming in Spark
Understand resilient distribution datasets (RDD) in detail
Implement and build Spark applications
Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
Understand the common use-cases of Spark and the various interactive algorithms
Learn Spark SQL, creating, transforming, and querying Data frames
Prepare for Cloudera Big Data CCA175 certification
The course presents the material as small building blocks with a thorough coverage of each component in the Hadoop stack. We begin by looking at Hadoop’s architecture and its underlying parts with topdown identification of component interactions within the Hadoop eco-system. The course then provides in-depth coverage of Hadoop Distributed FileSystem (HDFS), HBase, Map/Reduce, Oozie, Pig and Hive. To re-enforce concepts, each section is followed by a set of hands-on exercises. The exercises come in various complexities to accommodate developers with various levels of expertise. By completing this course, students will gain an in-depth understanding of how MapReduce and Distributed File Systems work. In addition, they will be able to create Hadoop-based MapReduce applications in Java and also leverage Hadoop other components to build powerful data processing applications.
The course consists of six main units:
1. The Big Data Economy
2. Apache Hadoop Architecture and Ecosystem
3. Hadoop for the Enterprise - Cloudera
4. HDFS Architecture and Programming
5. MapReduce Architecture and Programming
6. Data warehousing using Hive
7. Parallel programming using Pig
8. Realtime Online NoSQL using HBase
Unit One: The Big Data Economy
• Data
• Data Economy
• Data Analytics
• Data Science
• Traditional Data Processing Technologies
Unit Two: Apache Hadoop Architecture and Ecosystem
• Hadoop Background
• Hadoop Architecture
• Hadoop and RDBMS
• Hadoop Subprojects
• Hadoop Distributions
• Hadoop Documentation
Unit Three: Hadoop for the Enterprise - Cloudera
• CDH and Cloudera Manager
• Installing and Configuring Hadoop Cluster
• Starting Hadoop Cluster
• Running Hadoop Clients
• Browsing Cloudera Manage UI Consoles
Unit Four: HDFS Architecture and Programming
HDFS Architecture
• Hadoop HDFS Architecture
• Hadoop HDFS Architectural Capabilities
Performance
Scalability
Availability
Installability
Comnfigurability
Operability
Usability
Security
HDFS Programming Basics
• Hadoop Configuration API
• HDFS API Overview
• HDFS File CRUD API
• HDFS Directory CRUD API
HDFS Programming Advanced
• File Compression Decompression
• Type Serialization Deserialization
• Sequence Files
Unit Five: MapReduce Architecture and Programming
MapReduce Architecture
• Hadoop 1.0 MapReduce Architecture
• Hadoop 1.0 MapReduce Architectural Capabilities
Performance
Scalability
Availability
Installability
Comnfigurability
Operability
Usability
Programmability
• Hadoop 2.0 MapReduce Architecture
MapReduce Programming Basics
• MapReduce Programming Concepts – Map Phase and Reduce Phase
• MapReduce API – Key Java Classes and their Hierarchy
• Steps to Write a MapReduce Program
MapReduce Programming Intermediate
• Setting Mapper Counts and Reducer Counts
• MapReduce Configuration
• Combiners
• Partitioners
• Speculative Execution
• Task JVM Reuse
• Compression
MapReduce Programming Advanced
• Output Format
• Custom data Format
• Input Format
• Built in Mappers and Reducers
• Counters
• Multithreading
• Distributed Cache
MapReduce Streaming and Pipes
• MapReduce using Hadoop Streaming
• MapReduce using Hadoop Pipes
MapReduce Development Best Practices
• Logging in Hadooop
• Exception Handling
• Running Jobs Locally
• Unit Testing with MRUnit
• Top 10 Hadoop Anti-Patterns
Unit Six: Data warehousing using Hive
• Hive Background
• Hive Architecture
• Downloading, Installing and Configuring Hive
• Simple Hive Example
• Loading Data into Hive
• Hive Query Statements
• Hive Schema Violations
• Using Built-in Hive Functions
• Partitioning Data using Hive
• Joining Data
Unit Seven: Parallel programming using Pig
• Pig Background
• Architecture
• Downloading, Installing and Configuring Pig
• Running Pig
• Pig Latin Language Basics
• Core Relational Operators – DISTINCT, FILTER, SPLIT, ORDER BY, LIMIT, GROUP, FOREACH
• Built-in Functions
• Relational Join Operators
• Debug Operators
Unit Eight: Realtime Online NoSQL using HBase
• HBase Overview
• Data Model
• Architecture
• Downloading, Installing and Configuring HBase
• HBase Shell
• HBase Java API for CRUD Operations
The course consists of six main units:
1. The Big Data Economy
2. Apache Hadoop Architecture and Ecosystem
3. Hadoop for the Enterprise - Cloudera
4. HDFS Architecture and Programming
5. MapReduce Architecture and Programming
6. Data warehousing using Hive
7. Parallel programming using Pig
8. Realtime Online NoSQL using HBase
Unit One: The Big Data Economy
• Data
• Data Economy
• Data Analytics
• Data Science
• Traditional Data Processing Technologies
Unit Two: Apache Hadoop Architecture and Ecosystem
• Hadoop Background
• Hadoop Architecture
• Hadoop and RDBMS
• Hadoop Subprojects
• Hadoop Distributions
• Hadoop Documentation
Unit Three: Hadoop for the Enterprise - Cloudera
• CDH and Cloudera Manager
• Installing and Configuring Hadoop Cluster
• Starting Hadoop Cluster
• Running Hadoop Clients
• Browsing Cloudera Manage UI Consoles
Unit Four: HDFS Architecture and Programming
HDFS Architecture
• Hadoop HDFS Architecture
• Hadoop HDFS Architectural Capabilities
Performance
Scalability
Availability
Installability
Comnfigurability
Operability
Usability
Security
HDFS Programming Basics
• Hadoop Configuration API
• HDFS API Overview
• HDFS File CRUD API
• HDFS Directory CRUD API
HDFS Programming Advanced
• File Compression Decompression
• Type Serialization Deserialization
• Sequence Files
Unit Five: MapReduce Architecture and Programming
MapReduce Architecture
• Hadoop 1.0 MapReduce Architecture
• Hadoop 1.0 MapReduce Architectural Capabilities
Performance
Scalability
Availability
Installability
Comnfigurability
Operability
Usability
Programmability
• Hadoop 2.0 MapReduce Architecture
MapReduce Programming Basics
• MapReduce Programming Concepts – Map Phase and Reduce Phase
• MapReduce API – Key Java Classes and their Hierarchy
• Steps to Write a MapReduce Program
MapReduce Programming Intermediate
• Setting Mapper Counts and Reducer Counts
• MapReduce Configuration
• Combiners
• Partitioners
• Speculative Execution
• Task JVM Reuse
• Compression
MapReduce Programming Advanced
• Output Format
• Custom data Format
• Input Format
• Built in Mappers and Reducers
• Counters
• Multithreading
• Distributed Cache
MapReduce Streaming and Pipes
• MapReduce using Hadoop Streaming
• MapReduce using Hadoop Pipes
MapReduce Development Best Practices
• Logging in Hadooop
• Exception Handling
• Running Jobs Locally
• Unit Testing with MRUnit
• Top 10 Hadoop Anti-Patterns
Unit Six: Data warehousing using Hive
• Hive Background
• Hive Architecture
• Downloading, Installing and Configuring Hive
• Simple Hive Example
• Loading Data into Hive
• Hive Query Statements
• Hive Schema Violations
• Using Built-in Hive Functions
• Partitioning Data using Hive
• Joining Data
Unit Seven: Parallel programming using Pig
• Pig Background
• Architecture
• Downloading, Installing and Configuring Pig
• Running Pig
• Pig Latin Language Basics
• Core Relational Operators – DISTINCT, FILTER, SPLIT, ORDER BY, LIMIT, GROUP, FOREACH
• Built-in Functions
• Relational Join Operators
• Debug Operators
Unit Eight: Realtime Online NoSQL using HBase
• HBase Overview
• Data Model
• Architecture
• Downloading, Installing and Configuring HBase
• HBase Shell
• HBase Java API for CRUD Operations
CCP Data Scientist, MCSE: Business Intelligence, MongoDB Certified DBA
data scientist, data analyst, software engineer, big data engineer.
.png)