Course Details
Data Science and Big Data Mastery
Big Data
Created On
September 16, 2023Description
Big Data refers to the vast and complex sets of information that are too large and intricate for traditional data processing techniques to handle effectively. These datasets are characterized by their immense volume, high velocity, diverse variety, and the need for sophisticated techniques to capture, store, process, analyze, and extract valuable insights from them. The concept of Big Data has gained prominence in recent years due to the explosive growth of digital information generated by various sources, including social media, sensors, mobile devices, and more.
Overview
The **Data Science and Big Data Mastery** is a comprehensive course designed to equip learners with the knowledge and skills required to harness the power of big data technologies and tools. This hands-on program covers a wide range of topics, from foundational concepts to advanced techniques, enabling participants to become proficient in managing and analyzing massive datasets. Through a combination of theory and practical projects, students will gain the expertise necessary to thrive in the field of big data. **This comprehensive course will empower you with the knowledge and skills to harness the potential of big data technologies and tools, enabling you to shape the future of data-driven decision-making**.
Features
- Comprehensive curriculum covering a wide range of big data technologies and tools.
- Hands-on projects and exercises to reinforce learning.
- Expert instructors with real-world industry experience.
- Access to cloud-based platforms for practical exercises.
- A dedicated community for collaboration and support
- Certification upon successful completion.
- Revision Classes
- Industry real-time projects
- Every week doubt clearing session.
- Doubt clearing through mail and chat support team
- Assignment in all the module
- Quiz in every module
- Live project with real-time implementation
- Resume building
- Career guidance & Interview Preparation
- Internal Hiring
- Mock Interview Anytime
What you'll learn
- Foundations of Big Data
- Practical Linux Proficiency
- Python for Data Analysis
- SQL Mastery
- Hadoop and HDFS Expertise
- Database Management with Hive
- NoSQL Databases
- Stream Processing and Cloud Integration
Prerequisites
Curriculum
- 15 modules
Overview: This module provides a foundational understanding of big data, introducing learners to the concept and its significance in today's data-driven world.
Topics Covered:
What is Big Data?
The 5 Vs of Big Data (Volume, Velocity, Variety, Veracity, Value)
Challenges and Solutions in Handling Big Data
Exploding Data Problem: Causes and Implications
Overview: Linux is a fundamental skill for working with big data technologies. This module equips learners with Linux essentials and system administration skills.
Topics Covered:
Linux Fundamentals
Introduction to Linux
Linux Commands and File System
Users, Permissions, and Security
Shell Scripting for Automation
Networking and System Administration
Overview: Python is a versatile programming language widely used in big data analysis. This module covers Python from basics to advanced topics.
Topics Covered:
Python Basics: Data Types, Control Flow, and Loops
Advanced Python: Functions, Object-Oriented Programming, Exception Handling, File Handling
Overview: Structured Query Language (SQL) is essential for data querying and manipulation. This module covers SQL from the basics to advanced techniques.
Topics Covered:
Basic SQL: Introduction, Queries, Aggregate Functions, DDL, DML, DCL
Intermediate SQL: Data Manipulation, Transactions, Joins, Subqueries, Pivoting Data
Advanced SQL: Common Table Expressions (CTEs), Window Functions, Stored Procedures, Database Design Principles
Overview: Hadoop is a core technology for big data processing. This module introduces Hadoop and its components.
Topics Covered:
Hadoop Fundamentals: Introduction, Ecosystem Components, Architecture
Hadoop Distributed File System (HDFS): Design, Features, Commands
HDFS Commands: File Operations, Directory Management
Hive: Introduction, Data Modeling, Optimization Techniques
Overview: HBase is a NoSQL database used for handling large-scale data. This module covers its architecture and advanced concepts.
Topics Covered:
Introduction to HBase: Architecture, CRUD Operations, Filters
Advanced HBase: Performance Tuning, Data Versioning, Replication, Backup, Recovery, Security
Overview: MongoDB is a popular NoSQL database. This module covers its basics and advanced topics.
Topics Covered:
MongoDB Basics: CRUD Operations, Indexes, Aggregation Framework, Data Modeling
Advanced MongoDB: Data Management, Replication, Sharding, Security
Overview: Cassandra is another NoSQL database known for its scalability. This module explores its architecture and advanced concepts.
Topics Covered:
Introduction to Cassandra DB: Architecture, Data Modeling, CQL
Data Modeling in Cassandra: Data Types, Keyspaces, Clustering, Denormalization, Indexes
Advanced Cassandra DB Concepts: Consistency Levels, Data Replication, Security, Performance Tuning
Overview: Kafka is a distributed streaming platform. This module covers its architecture and practical usage.
Topics Covered:
Introduction to Kafka: Architecture, Topics, Brokers, Producers, Consumers
Kafka Producer and Consumer APIs: Message Batching, Partitioning
Kafka Stream Processing: Streams API, Windowing, Aggregations, Kafka Connect
Overview: PySpark is a Python library for Apache Spark. This module introduces learners to Spark and its usage with Python.
Topics Covered:
Introduction to PySpark: RDDs, DataFrames
SQL in PySpark: Basic Operations, Aggregations, Joins
PySpark Streaming: Real-time Data Processing, Deployment, Optimization
Overview: Apache Airflow is a platform for orchestrating complex data workflows. This module covers its components and practical application.
Topics Covered:
Airflow Components: Executors, Plugins
Task Scheduling and Monitoring
Building and Managing Data Pipelines
Overview: Snowflake is a cloud-based data warehousing platform. This module introduces learners to Snowflake and its capabilities.
Topics Covered:
Introduction to Snowflake: Architecture, Data Loading
Querying Data in Snowflake: SQL Syntax, Query Optimization
Advanced Features in Snowflake
Overview: Amazon Web Services (AWS) is a leading cloud platform. This module provides an introduction to AWS and its services relevant to big data.
Topics Covered:
Introduction to AWS: EC2, S3, IAM
Databases, Monitoring, Scaling
Hadoop Setup on Amazon EMR
Overview: Microsoft Azure is another major cloud platform. This module introduces Azure services for big data processing.
Topics Covered:
Introduction to Azure for Big Data: Services, Architectures
Azure Blob Storage and Data Lake
Azure Data Factory, Azure Databricks
Best Practices for Performance and Cost Management
Overview: In this final module, learners apply their knowledge to real-world scenarios through practical projects.
Sample Projects:
ETL Data Pipeline on AWS EMR Cluster
Modern ETL Data Pipeline using Informatica Cloud
Data Pipeline based on Messaging using PySpark and Airflow
Hive Project for E-commerce Data Warehousing
Financial Complaint Analysis
AWS Glue Data Pipeline
Instructors
Skoliko Faculty
₹15500.00
- 15 Modules
- 90 Hours
- Big Data
Login to Purchase the Course