Big Data Architect Masters Program

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Big Data
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Master Big data skills with CertZip Big Data Architect Masters Program and lead your way in professional life. In this best Big Data Architect Masters Program, you will learn about the aspects of Big Data Architect.

Course Description

Big Data Architect Masters Program drives you to be professional in tools and systems utilized by Big Data experts. This master in Big data includes training on Hadoop and Spark stack, Cassandra, Talend, and Apache Kafka messaging system.

Big data architects are responsible for providing the framework that appropriately replicates the Big Data needs of a company utilizing data, hardware, software, cloud services, developers, and other IT infrastructure to align the IT support of an association with its enterprise goals.

Candidates with a bachelor's degree in computer science, computer engineering, or a related field can pursue this Course.

Big Data permits institutions to catch trends and spot patterns that can be utilized for future advantage. It can help to see which customers are likely to buy products or help to optimize marketing campaigns by identifying which advertisement strategies have the highest return on investment.

There are no prerequisites for enrollment in the Big Data Architect Certification. Whether you are a skilled professional working in the IT industry or an aspirant planning to enter the data-driven world of analytics, Masters's Program is designed and developed to accommodate many professionals.

Big Data architects create and sustain data infrastructure to pull and organize data for accepted individuals to access. Data architects/engineers operate with database administrators and analysts to guarantee easy access to the company's big data.

One of the most promising and integral roles in data science is the data architect. From 2018–to 2028, it is expected that the demand for data architects will grow by 9%, higher than average for all other occupations.

What you'll learn

  • In this Course, you will learn: Hadoop and Spark stack, Cassandra, Talend and Apache Kafka messaging system and more.

Requirements

  • There is no particular requirement to pursue this course.

Curriculam

learn about Java architecture, advantages of Java, and develop the code with various data types, conditions, and loops.

Bytecode
Class Files
Compilation Process
Data types and Operations
If conditions
Loops - for, while and do-while
Data Types and Operations
if Condition
for..loop
while..loop
do..while loop

learn how to code with arrays, parts, and strings using examples and Programs.

Arrays - Single Dimensional and Multidimensional arrays
Functions
Function with Arguments
Function Overloading
Concept of Static Polymorphism
String Handling -String
String buffer Classes
Declaring the arrays
Accepting data for the arrays
Calling the functions which take arguments, perform a search in the array, and display the record by calling the function which takes arguments

comprehend object-oriented programming through Java using Classes, Objects, and different Java ideas like Abstract, Final, etc.

OOPS in Java: Concept of Object Orientation, Attributes and Methods, Classes and Objects
Methods and Constructors : Default Constructors, Constructors with Arguments, Inheritance, Abstract, Final and Static
Inheritance
Overloading
Overriding

know about packages in Java and scope specifiers of Java. You will also learn exception handling and how multithreading works in Java.

Packages and Interfaces
Access Specifiers
Package
Exception Handling
Multithreading
Interfaces
Packages
Exception
Thread

Discover to write code with Wrapper Classes, Inner Classes, and Applet Programs. How to use io, lang, and util packages of Java and Collections.

Wrapper Classes and Inner Classes: Integer, Character, Boolean, Float, etc.
Applet Programs: Writing UI programs with Applet, Java. Lang, Java.io, Java. Util.
Collections: ArrayList, Vector, HashSet, TreeSet, HashMap, HashTable.
Wrapper class
Collection

comprehend what Big Data is, the constraints of the traditional solutions for Big Data problems, how Hadoop decodes those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works

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

learn Hadoop Cluster Architecture, essential configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and set up Single Node and Multi-Node Hadoop Cluster.

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
one Node Cluster & Multi-Node Cluster set up
Basic Hadoop Administration

understand the Hadoop MapReduce framework fully, the working of MapReduce on data stored in HDFS, and advanced MapReduce concepts like Input Splits, Combiner & Partitioner.

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

discover Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format, and XML parsing.

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

learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts.

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

learning Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts, and Hive UDF.

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

comprehend advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive, Apache HBase, HBase Architecture, HBase running modes, and its components.

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

Learn advanced Apache HBase concepts. Witness demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps monitor a cluster & why HBase uses Zookeeper.

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

learning Apache Spark, SparkContext & Spark Ecosystem, and working in Resilient Distributed Datasets (RDD) in Apache Spark.

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

comprehend how numerous Hadoop ecosystem components work together to solve Big Data problems, Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

A. Discover the frequency of books published each year. (Hint: Sample dataset will be provided) B. Find out in which year the highest number of books were published C. Find out how many books were published based on ranking in 2002.

The Book-Crossing dataset consists of 3 tables that will be given to you.

A. Find a list of Airports operating in Country India B. Find the list of Airlines holding zero stops C. List of Airlines operating with codeshare D. Which Country (or) territory has the highest Airports E. Find the list of Active Airlines in the united state

In this service case, there are 3 data sets. Final_airlines, routes.dat, airports_mod.dat

Know Big Data and how it creates problems for traditional Database Management Systems like RDBMS; Cassandra solves these problems and understands Cassandra's features.

Intro to Big Data and Problems caused by it
5V – Volume, Variety, Velocity, Veracity, and Value
Traditional Database Management System
Limitations of RDMS
NoSQL databases
Common characteristics of NoSQL databases
CAP theorem
How does Cassandra solve the Limitations?
History of Cassandra
Features of Cassandra
VM tour

Know about Database Model and similarities between RDBMS and Cassandra Data Model. You will also understand the critical Database Elements of Cassandra and learn about the concept of Primary Key.

Introduction to Database Model
Understand the analogy between RDBMS and Cassandra Data Model
Understand the following Database Elements: Cluster, Keyspace, Column Family/Table, Column
Column Family Options
Columns
Wide Rows, Skinny Rows
Static and dynamic tables
Creating Keyspace
Creating Tables

Gain knowledge of architecting and creating Cassandra Database Systems, complex inner workings of Cassandra such as Gossip Protocol, Read Repairs, and so on.

Cassandra as a Distributed Database • Key Cassandra Elements a. Memtable b. Commit log c. SSTables
Replication Factor
Data Replication in Cassandra
Gossip protocol – Detecting failures
Gossip: Uses
Snitch: Uses
Data Distribution
Staged Event-Driven Architecture (SEDA)
Managers and Services
Virtual Nodes: Write path and Read path
Consistency level
Repair
Incremental repair

learn about Keyspace and its attributes in Cassandra, Keyspace, learn how to create a table, and perform operations like Inserting, Updating, and Deleting data from a table while using CQLSH.

Replication Factor
Replication Strategy
Defining columns and data types
Defining a partition key
Recognizing a partition key
Specifying a descending clustering order
Updating data
Tombstones
Deleting data
Using TTL
Updating a TTL
Create Keyspace in Cassandra
Check Created Keyspace in System_Schema.Keyspaces
Update Replication Factor of Previously Created Keyspace
Drop Previously Created Keyspace
Create A Table Using cqlsh
Make A Table Using UUID & TIMEUUID
Form A Table Using Collection & UDT Column
Construct a Secondary Index On a Table
Insert Data Into Table
Insert Data into Table with UUID & TIMEUUID Columns
Insert Data Using COPY Command
Deleting Data from Table

Learn how to add nodes in Cassandra and configure Nodes using the "Cassandra. yaml" file. Use nodetool to remove the node and restore the node into the service. In addition, by using the node tool repair command, learn the importance of repair and how to repair operation functions.

Cassandra nodes