Machine Learning | Avvacado Tech Info

Big Data Hadoop Certification Training

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About Course

Machine Learning Engineer Masters Program covers a broad array of topics which includes: Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models, Reinforcement Learning and many more.

Curriculum

Understanding Big Data and Hadoop

Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.

Topics:

Introduction to Big Data & Big Data Challenges
Hadoop & its Features
Hadoop 2.x Core Components
Hadoop Processing: MapReduce Framework
Limitations & Solutions of Big Data Architecture
Hadoop Ecosystem
Hadoop Storage: HDFS (Hadoop Distributed File System)
Different Hadoop Distributions

Hadoop Architecture and HDFS

Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.

Topics:

Hadoop 2.x Cluster Architecture
Federation and High Availability Architecture
Typical Production Hadoop Cluster
Hadoop Cluster Modes
Common Hadoop Shell Commands
Hadoop 2.x Configuration Files
Single Node Cluster & Multi-Node Cluster set up
Basic Hadoop Administration

Hadoop MapReduce Framework

Learning Objectives: In this module, you will understand Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.

Topics:

Traditional way vs MapReduce way
Why MapReduce
YARN Components
YARN Architecture
YARN MapReduce Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
Input Splits, Relation between Input Splits and HDFS Blocks
MapReduce: Combiner & Partitioner
Demo of Health Care Dataset
Demo of Weather Dataset

Advanced Hadoop MapReduce

Learning Objectives: In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.

Topics:

Counters
Distributed Cache
MRunit
Reduce Join
Custom Input Format
Sequence Input Format
XML file Parsing using MapReduce

Apache Pig

Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.

Topics:

Introduction to Apache Pig
MapReduce vs Pig
Pig Components & Pig Execution
Pig Data Types & Data Models in Pig
Pig Latin Programs
Shell and Utility Commands
Pig UDF & Pig Streaming
Testing Pig scripts with Punit
Aviation use-case in PIG
Pig Demo of Healthcare Dataset

Apache Hive

Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.

Topics:

Introduction to Apache Hive
Hive vs Pig
Hive Architecture and Components
Hive Metastore
Limitations of Hive
Comparison with Traditional Database
Hive Data Types and Data Models
Hive Partition
Hive Bucketing
Hive Tables (Managed Tables and External Tables)
Importing Data
Querying Data & Managing Outputs
Hive Script & Hive UDF
Retail use case in Hive
Hive Demo on Healthcare Dataset

Advanced Apache Hive and HBase

Learning Objectives:In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire indepth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.

Topics:

Hive QL: Joining Tables, Dynamic Partitioning
Custom MapReduce Scripts
Hive Indexes and views
Hive Query Optimizers
Hive Thrift Server
Hive UDF
Apache HBase: Introduction to NoSQL Databases and HBase
HBase v/s RDBMS
HBase Components
HBase Architecture
HBase Run Modes
HBase Configuration
HBase Cluster Deployment

Advanced Apache HBase

Learning Objectives:This module will cover advance Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.

Topics:

HBase Data Model
HBase Shell
HBase Client API
Hive Data Loading Techniques
Apache Zookeeper Introduction
ZooKeeper Data Model
Zookeeper Service
HBase Bulk Loading
Getting and Inserting Data
HBase Filters

Processing Distributed Data with Apache Spark

Learning Objectives:In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running application on Spark Cluster & comparing the performance of MapReduce and Spark.

Topics:

What is Spark
Spark Ecosystem
Spark Components
What is Scala
Why Scala
SparkContext
Spark RDD

Oozie and Hadoop Project

Learning Objectives:In this module, you will understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Topics:

Oozie
Oozie Components
Oozie Workflow
Scheduling Jobs with Oozie Scheduler
Demo of Oozie Workflow
Oozie Coordinator
Oozie Commands
Oozie Web Console
Oozie for MapReduce
Combining flow of MapReduce Jobs
Hive in Oozie
Hadoop Project Demo
Hadoop Talend Integration