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The online Advanced program in Big Data Architect led by Intellipaat lets you gain proficiency in Hadoop development, Spark, Python, Hadoop analysis, Hadoop administration, Apache Storm, NoSQL databases, Hadoop testing, Splunk Developer and Admin, and more. You will receive PLA credits from Belhaven University after successfully finishing the program, which you can use towards Belhaven University degree programs.
Learning Format
Online
Duration
7 Months
Career Services
by Intellipaat
12 PLA Credits from
Belhaven University
EMI Starts
at ₹8,000/month*
3109
1493
24542
This Advanced program includes 13 courses and 33 industry-based projects. Additionally, you will receive four self-paced courses namely, Spark Fundamentals I and II, Spark MLlib, and Python for Data Science.
About Belhaven University
Belhaven University is known for its nationally recognized academics, top-rated faculty, and affordability. Belhaven strives for excellence in higher education and has achieved prominence in education dating back to 1883. Every student is encouraged to develop and grow to the best of their potential.
Key Achievements of Belhaven University:
Upon the completion of this program, you will:
55% Average Salary Hike
40 LPA Highest Salary
11000+ Career Transitions
300+ Hiring Partners
Career Transition Handbook
*Past record is no guarantee of future job prospects
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
Skills to Master
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
Tools to Master
Module 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 – ETL Connectivity with Hadoop Ecosystem (Self-Paced)
Module 25 – Hadoop Application Testing
Module 26 – Roles and Responsibilities of Hadoop Testing Professional
Module 27 – Framework Called MRUnit for Testing of MapReduce Programs
Module 28 – Unit Testing
Module 29 – Test Execution
Module 30 – Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
Module 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
Module 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
Module 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
Module 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
Module 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
Module 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
Module 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
Module 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
Module 01 – Introduction to Kafka
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
Module 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
Module 01 – Core Java Concepts
Module 02 – Writing Java Programs using Java Principles
Module 03 – Language Conceptual
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
Projects will be a part of your big data architect advanced program to consolidate your learning. It will ensure that you have real-world experience in big data.
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Admission Details
The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.
Submit Application
Tell us a bit about yourself and why you want to join this program
Application Review
An admission panel will shortlist candidates based on their application
Admission
Selected candidates will be notified within 1–2 weeks
Total Admission Fee
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This online Advanced program will give you hands-on experience and help you become a successful Big Data Architect. The program includes various courses like Hadoop, Apache Storm, Spark, Python, NoSQL databases, etc. After completing the program, you will receive PLA Credits from Belhaven University.
As part of the course work, you will gain experience in several industry-based projects and assignments that are highly relevant in the corporate world. Upon completion of the course, you will be qualified to apply to some of the top-paying jobs around the world.
You can enroll in our big data architect advanced program. Once you successfully complete the program, along with the projects and assignments, you will receive your advanced certification in big data architecture.
Our Learning Management System (LMS) provides a customized learning experience with both live sessions & self-paced videos. The online classroom training sessions include one-on-one doubt-clearing sessions as well. In case you miss a live session, you will be able to avail the recorded video of the session so you don’t miss the lesson.
To get more information about this big data architect advanced program, you can reach out to our course advisors. They will provide you with all the right kinds of assistance that you need relating to the Advanced program.
PLA Credits assessed for this course are 12.
*Note, there is a maximum number of PLA credits that may be applied toward a degree program. Refer to the Belhaven University website for the latest on PLA policies.
Please note that the course fees is non-refundable and we will be at every step with you for your upskilling and professional growth needs.
Due to any reason you want to defer the batch or restart the classes in a new batch then you need to send the batch defer request on [email protected] and only 1 time batch defer request is allowed without any additional cost.
Learner can request for batch deferral to any of the cohorts starting in the next 3-6 months from the start date of the initial batch in which the student was originally enrolled for. Batch deferral requests are accepted only once but you should not have completed more than 20% of the program. If you want to defer the batch 2nd time then you need to pay batch defer fees which is equal to 10% of the total course fees paid for the program + Taxes.
Yes, Intellipaat certification is highly recognized in the industry. Our alumni work in more than 10,000 corporations and startups, which is a testament that our programs are industry-aligned and well-recognized. Additionally, the Intellipaat program is in partnership with the National Skill Development Corporation (NSDC), which further validates its credibility. Learners will get an NSDC certificate along with Intellipaat certificate for the programs they enroll in.
What is included in this course?
