Big Data Certification Master’s Course

If you want to gain high-level proficiency in tools and systems of Big Data, then this Big Data Certification Master’s Course is perfect for you. You will learn to deal with the real-time advanced projects through which you can gain in-depth knowledge. Adding to this, as part of this Big Data Master’s Course online, you’ll learn to develop projects in Hadoop Administration, Hadoop Testing, Python, Splunk, Spark, Hadoop Development, Hadoop Analysis, etc.

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Big Data Training Master’s Course Online – Overview

Master your skills to reap actionable insights from big data that help in business growth. This Big Data Architect Master’s Course will allow you to get through the detailed exposure to Big Data platforms such as Spark, Hadoop, NoSQL databases, etc. Therefore, you will get a competitive edge in the advanced concepts of big data and explore better career opportunities.

Big Data Architect Master’s Course – Key Features

  • Trusted content
  • Re-learn for free anytime in a year
  • Rigorous assignments and assessments
  • Learn at your own pace
  • Mandatory feedback sessions
  • Mock-interviews
  • Hands-on real-time experience
  • Free mentorship
  • Live chat for instant solutions
  • Job-ready employees post-training
  • End-to-end training
  • Download the certificate after the course
Big Data Architect Master’s Course – Skills Covered: Hadoop. HDFS. MapReduce. Spark. Scala. Splunk. Mapping. Pivots. Python. Statistics. Data Science with Python. Machine Learning. PySpark. Lambda functions. MongoDB. Apache Kafka. Apache Storm. ETL. Hive. AWS Big Data. Hadoop Testing.

Big Data Architect Master’s Course – Benefits

The global market of big data is expected to grow tremendously in the next 5 to 8 years and with this projected growth, there will be multiple projects and wider job opportunities in the big data domain. So, master your skills in big data and upgrade your career.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Big Data Architect

UK
Hiring Companies

Big Data Architect Master’s Program – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Access to the class recordings
  • Assistance and support
  • Download certification
  • Free mentorship

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification
  • Interactive training sessions
  • Sessions on the capstone project
  • Live, online classroom training
  • Mock-interviews

Corporate Training

Customized to your team's needs

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Class recordings
  • Assistance and support
  • Certification after the course

Big Data Architect Master’s Course – Curriculum

Eligibility

Individuals or undergraduates who are aiming to develop their career as a Big Data Architect and have done a basic or intermediate level course in big data can join this big data master’s training program. Adding to this, working individuals such as data science professionals, software developers, information architects, business intelligence professionals, and big data professionals can get through this Big Data master’s course.

Pre-requisites

In order to experience a successful learning journey in this big data master’s training program, you should have done basic big data courses or should have attained foundational knowledge in big data.

Course Content

  • Hadoop Installation and Setup
  • Introduction to Big Data
  • Hadoop and Understanding HDFS and MapReduce
  • Deep Dive in MapReduce
  • Introduction to Hive Advanced Hive and Impala Introduction to Pig
  • Flume, Sqoop and HBase
  • Writing Spark Applications
  • Using Scala Use Case Bobsrockets Package
  • Introduction to Spark
  • Spark Basics
  • Working with RDDs in Spark
  • Aggregating Data with Pair RDDs
  • Writing and Deploying Spark Applications
  • Project Solution Discussion and Cloudera Certification Tips and Tricks
  • Parallel Processing
  • Spark RDD Persistence
  • Spark MLlib
  • Integrating Apache Flume and Apache Kafka S
  • Spark Streaming
  • Improving Spark Performance
  • Spark SQL and Data Frames
  • Scheduling/Partitioning
  • Following topics will be available only in self-paced mode
  • Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2
  • Hadoop Administration – Cluster Configuration
  • Hadoop Administration – Maintenance, Monitoring and Troubleshooting
  • ETL Connectivity with Hadoop Ecosystem (Self-Paced)
  • Hadoop Application Testing
  • Roles and Responsibilities of Hadoop Testing Professional
  • Framework Called MRUnit for Testing of MapReduce Programs
  • Unit Testing
  • Test Execution
  • Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
  • Scala Course Content
  • Introduction to Scala
  • Pattern Matching
  • Executing the Scala Code
  • Classes Concept in Scala
  • Case Classes and Pattern Matching
  • Concepts of Traits with Example
  • Scala–Java Interoperability
  • Scala Collections
  • Mutable Collections Vs. Immutable Collections
  • Use Case Bobsrockets Package
  • Spark Course Content
  • Introduction to Spark
  • Spark Basics
  • Working with RDDs in Spark
  • Aggregating Data with Pair RDDs
  • Writing and Deploying Spark Applications
  • Parallel Processing
  • Spark RDD Persistence
  • Spark MLlib
  • Integrating Apache Flume and Apache Kafka
  • Spark Streaming
  • Improving Spark Performance
  • Spark SQL and Data Frames
  • Scheduling/Partitioning
  • Scala Course Content
  • Splunk Development Concepts
  • Basic Searching
  • Using Fields in Searches
  • Saving and Scheduling Searches
  • Creating Alerts
  • Scheduled Reports
  • Tags and Event Types
  • Creating and Using Macros
  • Workflow
  • Splunk Search Commands
  • Transforming Commands
  • Reporting Commands
  • Mapping and Single Value Commands
  • Splunk Reports and Visualizations
  • Analyzing, Calculating and Formatting Results
  • Correlating Events
  • Enriching Data with Lookups
  • Getting Started with Parsing
  • Using Pivot
  • Common Information Model (CIM) Add-On
  • Splunk Administration Topics
  • Overview of Splunk
  • Splunk Installation
  • Splunk Installation in Linux
  • Distributed Management Console
  • Introduction to Splunk App
  • Splunk Indexes and Users
  • Splunk Configuration Files
  • Splunk Deployment Management
  • Splunk Indexes User Roles and Authentication
  • Splunk Administration Environment
  • Basic Production Environment
  • Splunk Search Engine
  • Various Splunk Input Methods
  • Splunk User and Index Management
  • Machine Data Parsing
  • Search Scaling and Monitoring
  • Splunk Cluster Implementation
  • Introduction to Data Science using Python
  • Python basic constructs
  • Maths for DS-Statistics & Probability
  • OOPs in Python (Self paced)
  • NumPy for mathematical computing
  • SciPy for scientific computing
  • Data manipulation
  • Data visualization with Matplotlib
  • Machine Learning using Python
  • Supervised learning
  • Unsupervised Learning
  • Python integration with Spark (Self paced)
  • Dimensionality Reduction
  • Time Series Forecasting
  • Introduction to the Basics of Python
  • Sequence and File Operations
  • Functions, Sorting, Errors and Exception, Regular Expressions, and Packages
  • Python: An OOP Implementation
  • Debugging and Databases
  • Introduction to Big Data and Apache Spark
  • Python for Spark
  • Python for Spark: Functional and Object-Oriented Model
  • Apache Spark Framework and RDDs
  • PySpark SQL and Data Frames
  • Apache Kafka and Flume
  • PySpark Streaming
  • Introduction to PySpark Machine Learning
  • Introduction to NoSQL and MongoDB
  • MongoDB Installation
  • Importance of NoSQL
  • CRUD Operations
  • Data Modeling and Schema Design
  • Data Management and Administration
  • Data Indexing and Aggregation
  • MongoDB Security
  • Working with Unstructured Data
  • Introduction to Big Data and Data Collection
  • Introduction to Cloud Computing & AWS
  • Elastic Compute and Storage Volumes
  • Virtual Private Cloud Storage Simple Storage Service (S3)
  • Databases and In-Memory
  • DataStores
  • Data Storage
  • Data Processing
  • Data Analysis
  • Data Visualization and Data Security
  • Introduction to Hadoop and Its Ecosystem, MapReduce and HDFS
  • MapReduce
  • Introduction to Pig and Its Features
  • Introduction to Hive
  • Hadoop Stack Integration Testing
  • Roles and Responsibilities of Hadoop Testing
  • Framework Called MRUnit for Testing of MapReduce Programs
  • Unit Testing
  • Test Execution of Hadoop: Customized
  • Test Plan Strategy Test Cases of Hadoop Testing
  • Understanding the Architecture of Storm
  • Installation of Apache Storm
  • Introduction to Apache Storm
  • Apache Kafka Installation
  • Apache Storm Advanced
  • Storm Topology
  • Overview of Trident
  • Storm Components and Classes
  • Cassandra Introduction
  • Boot Stripping
  • What is Kafka An Introduction
  • Multi Broker Kafka Implementation
  • Multi Node Cluster Setup
  • Integrate Flume with Kafka
  • Kafka API
  • Producers & Consumers
  • Advantages and Usage of Cassandra
  • CAP Theorem and No SQL DataBase
  • Cassandra fundamentals, Data model, Installation and setup
  • Cassandra Configuration
  • Summarization, node tool commands, cluster, Indexes, Cassandra & MapReduce, Installing Ops-center
  • Multi Cluster setup
  • Thrift/Avro/Json/Hector Client
  • Datastax installation part,· Secondary index
  • Advance Modelling
  • Deploying the IDE for Cassandra applications
  • Cassandra Administration
  • Cassandra API and Summarization and Thrift
  • Core Java Concepts
  • Writing Java Programs using Java Principles
  • Language Conceptuals
  • Operating with Java Statements
  • Concept of Objects and Classes
  • Introduction to Core Classes
  • Inheritance in Java
  • Exception Handling in Detail
  • Getting started with Interfaces and Abstract Classes
  • Overview of Nested Classes
  • Getting started with Java Threads
  • Overview of Java Collections
  • Understanding JDBC
  • Java Generics
  • Input/Output in Java
  • Getting started with Java Annotations
  • Reflection and its Usage
  • Introduction to Linux
  • File Management
  • Files and Processes
  • Introduction to Shell Scripting
  • Conditional, Looping statements and Functions
  • Text Processing
  • Scheduling Tasks
  • Advanced Shell Scripting
  • Database Connectivity
  • Linux Networking

Big Data Master’s Program – FAQs

It is an online training program that enables you to master the advanced concepts of big data. It is always necessary to upskill your knowledge for better career growth and the big data master’s course helps you to advance your knowledge in big data.

Undoubtedly, big data is in high demand due to its use in wider applications where businesses need to derive insights from huge collected data. Its global market growth is expected to be huge due to the increased need for analytics in a wide range of industries. Therefore, as the demand for big data is high, graduates can experience multiple job opportunities and amazing career growth.

Coding is an essential skill in the big data domain. If you are planning to enter into the world of big data, it is important to have prior coding knowledge as you need to code to perform statistical and numerical analyses of the available data sets. Therefore, before you join the big data course at Hatigen, make sure that you improve your coding knowledge in R, python, or java.

Big data refers to the collection of large and complex data while data analytics helps in extracting meaningful insights that enable to make better business decisions. However, data science is a multidisciplinary field that performs operations and produces broader insights.

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