Advanced Techniques for Big Data Management in 2025

2025 Mastering Big Data with Hadoop Ecosystem

Unleash the Power of Big Data with Hadoop

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Last Updated: 2024-12-24 12:12:34 English Course Type: Self Learning

You will acquire a comprehensive understanding of Big Data technologies and practical skills to apply them effectively. Here are the specific skills you'll develop: Big Data Fundamentals: Grasp the core concepts of Big Data, including the V's (Volume, Velocity, Variety, Veracity, and Value).
Understand the challenges and opportunities associated with Big Data.
Hadoop Ecosystem Mastery: Learn to work with the Hadoop Distributed File System (HDFS) for storing and managing large datasets.
Master the Hadoop MapReduce framework for parallel processing of large datasets.
Gain proficiency in using YARN for resource management and job scheduling.
Data Warehousing with Hive: Learn to use HiveQL to query and analyze large datasets stored in HDFS.
Understand data partitioning and bucketing techniques for efficient data access.
Master Hive's built-in functions and user-defined functions for data transformation and analysis.
Data Ingestion and Export with Sqoop: Learn to efficiently transfer data between relational databases and Hadoop using Sqoop. Master import and export operations, including data cleaning and transformation.
NoSQL Database with HBase: Gain hands-on experience with HBase, a distributed, column-oriented NoSQL database.
Learn to design and create HBase tables for efficient data storage and retrieval. Understand HBase's data model and query patterns.

Dive into the world of Big Data and master the art of handling massive datasets with Hadoop!

This comprehensive course is designed to equip you with the essential skills to harness the power of Big Data. You'll learn to leverage the Hadoop ecosystem, including HDFS, MapReduce, Hive, Sqoop, and HBase, to efficiently store, process, and analyze large-scale data.

Key Topics Covered:

Big Data Fundamentals:

Understanding the 5 V's of Big Data

Challenges and opportunities in the Big Data era

Hadoop Ecosystem:

HDFS: Distributed File System for storing massive datasets

MapReduce: Parallel processing framework for data analysis

YARN: Resource Management and Job Scheduling

Data Warehousing with Hive:

HiveQL: SQL-like language for querying data in HDFS

Data partitioning and bucketing for efficient data access

Hive's built-in functions and user-defined functions for data transformation

Data Ingestion and Export with Sqoop:

Efficiently transferring data between relational databases and Hadoop

Import and export operations, including data cleaning and transformation

NoSQL Database with HBase:

Designing and creating HBase tables for efficient data storage and retrieval

Understanding HBase's data model and query patterns

What You'll Learn:

Practical Skills: Hands-on experience with Hadoop tools and techniques

Problem-Solving: Ability to tackle complex data challenges

Industry Relevance: Stay up-to-date with the latest Big Data trends

Career Advancement: Prepare for high-demand roles in data engineering, data science, and data analytics

Who This Course Is For:

Data Analysts

Software Engineers

Data Scientists

Students and Recent Graduates

No prior Big Data experience is required. This course is designed for learners of all levels, from beginners to experienced professionals. Join us and unlock the potential of Big Data!

Basic understanding of programming concepts (Python, Java, or Scala)
Familiarity with SQL and database concepts Experience with Linux or Unix-based systems Background and Experience

Data Professionals and Aspiring Data Scientists Ideal Candidate
Data Analysts: Seeking to transition into Big Data roles, especially those working with large and complex datasets.
Software Engineers: Interested in expanding their skillset to include Big Data technologies.
Data Scientists: Aiming to deepen their knowledge of Big Data tools and techniques.
Students and Recent Graduates: Looking to gain practical experience in Big Data and prepare for industry roles.
Aspiring to secure higher-paying positions in data engineering, data science, or data analytics roles.

Section 1 : Introduction 2 Lectures
Lecture 1 :
What is Big Data? Preview
Lecture 2 :
Hadoop Platforms For hands-on
Section 2 : Hive 12 Lectures
Lecture 1 :
Introducing Hive
Lecture 2 :
Database In Hive
Lecture 3 :
Database(Demo)
Lecture 4 :
Tables In Hive
Lecture 5 :
Internal table
Lecture 6 :
External Table
Lecture 7 :
Case In Hive
Lecture 8 :
Case In Hive - Demo
Lecture 9 :
Partitioning And Bucketing
Lecture 10 :
Partitioning In Hive(Demo)
Lecture 11 :
Hive Buit-in Functions
Lecture 12 :
Performance Tuning In Hive
Section 3 : SQOOP 0 Lectures
Section 4 : SQOOP 4 Lectures
Lecture 1 :
Introducing Sqoop
Lecture 2 :
Sqoop Import To HDFS
Lecture 3 :
Sqoop Export From HDFS
Lecture 4 :
Sqoop Import To Hive Table
Section 5 : Hbase 4 Lectures
Lecture 1 :
Introduction To Hbase
Lecture 2 :
Hbase Platform Introduction & Common Shell Operations
Lecture 3 :
Hbase Table Creation
Lecture 4 :
Inserting Data Into Hbase Table

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Mukund Kumar Mishra,

1831 Course Views

5 Courses

Mukund Kumar is a seasoned IT professional with over 16 years of experience. He has held roles as a Senior Tech Architect and Senior Tech Manager, specializing in Hadoop and Azure technologies. Based in India, Mukund excels in creating and delivering technical content, including articles, tutorials, and Udemy courses. His expertise lies in data engineering, Snowflake, Kubernetes, and generative AI, often incorporating real-world examples into his teaching.

Mukund's contributions extend to developing certification courses, quizzes, and practice tests for different learning platforms

Additionally, he is skilled in technical and functional architecture for cloud technologies, database systems, and operational frameworks. Mukund is dedicated to exploring innovative teaching methods, such as themed assignments and engaging narratives.