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Our Big Data Architect master's course lets you gain proficiency in Big Data. You will work on real-world projects in Hadoop Development, Hadoop Administration, Hadoop Analysis, Hadoop Testing, Spark, Python, Splunk Developer and Admin, Apache Storm, NoSQL databases and more. In this program, you will cover 13 courses and 33 industry-based projects. As a part of this online classroom training, you will receive four additional self-paced courses namely, Spark MLlib, and Python for Data Science.
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Course PreviewIntellipaat’s Big Data Architect master’s course offers in-depth knowledge of Hadoop, Spark, NoSQL, analytics, and ETL tools. Designed by industry experts, it includes 13 courses and 33 industry projects.
Self-paced Courses:
There are no prerequisites for taking up this training program.
Big Data is the fastest growing and the most promising technology that aids profiles like Big Data Engineer and Big Data Solutions Architect that are in huge demand. This Big Data Architect master’s course will help you grab the best jobs in this domain.
This Intellipaat training program has been specifically created to let you master the Hadoop architecture, along with helping you gain proficiency in Business Intelligence domain. Upon the completion of the training, you will be well-versed in extracting valuable business insights from raw data. This way, you can apply for top jobs in the Big Data ecosystem.
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Help clients make changes and transformations in their organization by solving complex problems.
Implement large-scale data warehouses or data lakes on the cloud with the help of AWS Native and other similar services.
Create solutions using the data warehouse systems, like IICS, Airflow, and Snowflake.
Design and create data ingestion pipelines and also perform conversion activities and data migration.
Adopt leading technologies in the industry to get high-performance computation in the organization, along with the required data storage solutions.
Simplify the flow of data and eliminate unrelated costs of fragmented and complicated data systems of the organization
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
$527
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60 Hours 33 Module
PreviewModule 01 – Hadoop Installation and Setup
Module 02 – Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
Module 03 – Deep Dive in MapReduce
Module 04 – Introduction to Hive
Module 05 – Advanced Hive and Impala
Module 06 – Introduction to Pig
Module 07 – Flume, Sqoop and HBase
Module 08 – Writing Spark Applications Using Scala
Module 09 – Use Case Bobsrockets Package
Module 10 – Introduction to Spark
Module 11 – Spark Basics
Module 12 – Working with RDDs in Spark
Module 13 – Aggregating Data with Pair RDDs
Module 14 – Writing and Deploying Spark Applications
Module 15 – Project Solution Discussion and Cloudera Certification Tips and Tricks
Module 16 – Parallel Processing
Module 17 – Spark RDD Persistence
Module 18 – Spark MLlib
Module 19 – Integrating Apache Flume and Apache Kafka
Module 20 – Spark Streaming
Module 21 – Improving Spark Performance
Module 22 – Spark SQL and Data Frames
Module 23 – Scheduling/Partitioning
Module 24 – Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2
Module 25 – Hadoop Administration – Cluster Configuration
Module 26 – Hadoop Administration – Maintenance, Monitoring and Troubleshooting
Module 27 – ETL Connectivity with Hadoop Ecosystem (Self-Paced)
Module 28 – Hadoop Application Testing
Module 29 – Roles and Responsibilities of Hadoop Testing Professional
Module 30 – Framework Called MRUnit for Testing of MapReduce Programs
Module 31 – Unit Testing
Module 32 – Test Execution
Module 33 – Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
24 Hours 23 Module
PreviewModule 01 – Introduction to Scala
Module 02 – Pattern Matching
Module 03 – Executing the Scala Code
Module 04 – Classes Concept in Scala
Module 05 – Case Classes and Pattern Matching
Module 06 – Concepts of Traits with Example
Module 07 – Scala–Java Interoperability
Module 08 – Scala Collections
Module 09 – Mutable Collections Vs. Immutable Collections
Module 10 – Use Case Bobsrockets Package
Module 11 – Introduction to Spark
Module 12 – Spark Basics
Module 13 – Working with RDDs in Spark
Module 14 – Aggregating Data with Pair RDDs
Module 15 – Writing and Deploying Spark Applications
Module 16 – Parallel Processing
Module 17 – Spark RDD Persistence
Module 18 – Spark MLlib
Module 19 – Integrating Apache Flume and Apache Kafka
Module 20 – Spark Streaming
Module 21 – Improving Spark Performance
Module 22 – Spark SQL and Data Frames
Module 23 – Scheduling/Partitioning
26 Hours 39 Module
PreviewModule 1 – Splunk Development Concepts
Module 2 – Basic Searching
Module 3 – Using Fields in Searches
Module 4 – Saving and Scheduling Searches
Module 5 – Creating Alerts
Module 6 – Scheduled Reports
Module 7 – Tags and Event Types
Module 8 – Creating and Using Macros
Module 9 – Workflow
Module 10 – Splunk Search Commands
Module 11 – Transforming Commands
Module 12 – Reporting Commands
Module 13 – Mapping and Single Value Commands
Module 14 – Splunk Reports and Visualizations
Module 15 – Analyzing, Calculating and Formatting Results
Module 16 – Correlating Events
Module 17 – Enriching Data with Lookups
Module 18 – Creating Reports and Dashboards
Module 19 – Getting Started with Parsing
Module 20 – Using Pivot
Module 21 – Common Information Model (CIM) Add-On
Module 22 – Overview of Splunk
Module 23 – Splunk Installation
Module 24 – Splunk Installation in Linux
Module 25 – Distributed Management Console
Module 26 – Introduction to Splunk App
Module 27 – Splunk Indexes and Users
Module 28 – Splunk Configuration Files
Module 29 – Splunk Deployment Management
Module 30 – Splunk Indexes
Module 31 – User Roles and Authentication
Module 32 – Splunk Administration Environment
Module 33 – Basic Production Environment
Module 34 – Splunk Search Engine
Module 35 – Various Splunk Input Methods
Module 36 – Splunk User and Index Management
Module 37 – Machine Data Parsing
Module 38 – Search Scaling and Monitoring
Module 39 – Splunk Cluster Implementation
39 Hours 14 Module
PreviewModule 01 – Introduction to Data Science using Python
Module 02 – Python basic constructs
Module 03 – Maths for DS-Statistics & Probability
Module 04 – OOPs in Python (Self paced)
Module 05 – NumPy for mathematical computing
Module 06 – SciPy for scientific computing
Module 07 – Data manipulation
Module 08 – Data visualization with Matplotlib
Module 09 – Machine Learning using Python
Module 10 – Supervised learning
Module 11 – Unsupervised Learning
Module 12 – Python integration with Spark (Self paced)
Module 13 – Dimensionality Reduction
Module 14 – Time Series Forecasting
24 Hours 9 Module
PreviewModule 01 – Introduction to NoSQL and MongoDB
Module 02 – MongoDB Installation
Module 03 – Importance of NoSQL
Module 04 – CRUD Operations
Module 05 – Data Modeling and Schema Design
Module 06 – Data Management and Administration
Module 07 – Data Indexing and Aggregation
Module 08 – MongoDB Security
Module 09 – Working with Unstructured Data
32 Hours 10 Module
PreviewModule 01 – Introduction to Big Data and Data Collection
Module 02 – Introduction to Cloud Computing & AWS
Module 03 – Elastic Compute and Storage Volumes
Module 04 – Virtual Private Cloud
Module 05 – Storage – Simple Storage Service (S3)
Module 06 – Databases and In-Memory DataStores
Module 07 – Data Storage
Module 08 – Data Processing
Module 09 – Data Analysis
Module 09 – Data Visualization and Data Security
24 Hours 10 Module
PreviewModule 01 – Introduction to Hadoop and Its Ecosystem, MapReduce and HDFS
Module 02 – MapReduce
Module 03 – Introduction to Pig and Its Features
Module 04 – Introduction to Hive
Module 05 – Hadoop Stack Integration Testing
Module 06 – Roles and Responsibilities of Hadoop Testing
Module 07 – Framework Called MRUnit for Testing of MapReduce Programs
Module 08 – Unit Testing
Module 09 – Test Execution of Hadoop: Customized
Module 10 – Test Plan Strategy Test Cases of Hadoop Testing
12 Hours 10 Module
PreviewModule 01 – Understanding the Architecture of Storm
Module 02 – Installation of Apache Storm
Module 03 – Introduction to Apache Storm
Module 04 – Apache Kafka Installation
Module 05 – Apache Storm Advanced
Module 06 – Storm Topology
Module 07 – Overview of Trident
Module 08 – Storm Components and Classes
Module 09 – Cassandra Introduction
Module 10 – Boot Stripping
12 Hours 6 Module
PreviewModule 01 – What is Kafka – An Introduction
Module 02 – Multi Broker Kafka Implementation
Module 03 – Multi Node Cluster Setup
Module 04 – Integrate Flume with Kafka
Module 05 – Kafka API
Module 06 – Producers & Consumers
24 Hours 13 Module
PreviewModule 01 – Introduction to the Basics of Python
Module 02 – Sequence and File Operations
Module 03 – Functions, Sorting, Errors and Exception, Regular Expressions, and Packages
Module 04 – Python: An OOP Implementation
Module 05 – Debugging and Databases
Module 06 – Introduction to Big Data and Apache Spark
Module 07 – Python for Spark
Module 08 – Python for Spark: Functional and Object-Oriented Model
Module 09 – Apache Spark Framework and RDDs
Module 10 – PySpark SQL and Data Frames
Module 11 – Apache Kafka and Flume
Module 12 – PySpark Streaming
Module 13 – Introduction to PySpark Machine Learning
16 Hours 12 Module
PreviewModule 01 – Advantages and Usage of Cassandra
Module 02 – CAP Theorem and No SQL DataBase
Module 03 – Cassandra fundamentals, Data model, Installation and setup
Module 04 – Cassandra Configuration
Module 05 – Summarization, node tool commands, cluster, Indexes, Cassandra & MapReduce, Installing Ops-center
Module 06 – Multi Cluster setup
Module 07 – Thrift/Avro/Json/Hector Client
Module 08 – Datastax installation part,· Secondary index
Module 09 – Advance Modelling
Module 10 – Deploying the IDE for Cassandra applications
Module 11 – Cassandra Administration
Module 12 – Cassandra API and Summarization and Thrift
16 Hours 17 Module
PreviewModule 01 – Core Java Concepts
Module 02 – Writing Java Programs using Java Principles
Module 03 – Language Conceptuals
Module 04 – Operating with Java Statements
Module 05 – Concept of Objects and Classes
Module 06 – Introduction to Core Classes
Module 07 – Inheritance in Java
Module 08 – Exception Handling in Detail
Module 09 – Getting started with Interfaces and Abstract Classes
Module 10 – Overview of Nested Classes
Module 11 – Getting started with Java Threads
Module 12 – Overview of Java Collections
Module 13 – Understanding JDBC
Module 14 – Java Generics
Module 15 – Input/Output in Java
Module 16 – Getting started with Java Annotations
Module 17 – Reflection and its Usage
16 Hours 10 Module
PreviewModule 01 – Introduction to Linux
Module 02 – File Management
Module 03 – Files and Processes
Module 04 – Introduction to Shell Scripting
Module 05 – Conditional, Looping statements and Functions
Module 06 – Text Processing
Module 07 – Scheduling Tasks
Module 08 – Advanced Shell Scripting
Module 09 – Database Connectivity
Module 10 – Linux Networking
Projects will be a part of your Big Data Master’s program to consolidate your learning. It will ensure that you have real-world experience in Big Data.
This Big Data Architect course is created to help you get into top organizations. Here, you will work on various real-world projects, assignments, and case studies which will enhance your skills in Big Data and fast-track your career in this domain.
This is a comprehensive course that is designed to clear multiple certifications such as:
The average income of a Big Data Architect is US$144,315 per year.
PayScaleGlobal Hadoop Market is estimated to go up to US$84.6 billion by the end of the year.
Business WireThere are over 3,000 jobs in the United States alone for Big Data Architects
LinkedInLand Your Dream Job Like Our Alumni
Intellipaat’s Masters Course is a structured learning path specially designed by industry experts which ensures that you transform into Big Data expert. Individual courses at Intellipaat focus on one or two specializations. However, if you have to masters big data then this program is for you
Intellipaat offers courses on Big Data Hadoop, Data Science, Machine Learning, Artificial Intelligence Certification, Python Course, Python for Data Science, Data Analysis Training, Business Analytics Certification
3 technical 1:1 sessions per month will be allowed.
Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.
Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.
No, our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.
