What is Hadoop Big Data Certification?
The Hadoop Big Data Certification Training is designed to provide Learners with a comprehensive understanding of the Apache Hadoop ecosystem, including HDFS (Hadoop Distributed File System), MapReduce, YARN, and other tools like Hive, Pig, and HBase. This course covers big data technologies' core concepts and practical applications, enabling learners to handle complex datasets efficiently.
Learners will gain hands-on experience managing big data workflows, performing data analysis, and optimising the Hadoop ecosystem for various business challenges. The training prepares learners for Hadoop certifications and equips them with in-demand skills for big data analytics and engineering careers.
This comprehensive 2-day Hadoop Big Data Certification Training Course by Oakwood International empowers professionals to master big data tools and techniques, enhancing their expertise and career prospects in the rapidly growing field of data analytics.
Course Objectives
- To understand the fundamentals of big data and the Hadoop ecosystem
- To learn how to store, process, and analyse massive datasets using HDFS and MapReduce
- To gain expertise in tools like Hive, Pig, Sqoop, and HBase for data management and querying
- To optimise the performance of big data applications with YARN and resource management
- To develop skills for handling real-time data processing with Hadoop ecosystem tools
- To integrate Hadoop with other big data platforms for advanced analytics
- To prepare for Hadoop certifications with practical experience and exam readiness
Upon completion, Learners will have the expertise to implement and manage big data solutions using Apache Hadoop, driving impactful business outcomes.
Course Outline
Hadoop Big Data Certification
Module 1: Understanding Hadoop
- What is Web Hadoop?
- Why is Hadoop Important?
- Hadoop Architecture
- Challenges of Using Hadoop
Module 2: Processing Distributed Data
- HDFS
- MapReduce
- Architecture
- Processing Data
Module 3: Introduction to Data Storage and Processing
- Overview
- Projects for Structured Data Storage and Processing
Module 4: Defining Hadoop Cluster Requirements
- Hadoop Cluster
- Advantages
- Hadoop Cluster Architecture
- Best Practices for Building Hadoop Cluster
Module 5: Configuring a Cluster
- Types of Configuration Files Drive Hadoop Configuration
- Code Example
Module 6: Maximising HDFS Robustness
- Three Types of Failures in HDFS
- Data Disk Failure, Heartbeats, and Re-Replication
- Cluster Rebalancing
- Data Integrity
- Metadata Disk Failure
- Snapshots
Module 7: Managing Resources and Cluster Health
- Managing Resources
- Managing HDFS Cluster
- Secondary NameNode Configuration
- MapReduce Cluster Management
Module 8: Maintaining a Cluster
- FileSystem Checks
- HDFS Balancer Utility
- Add New Nodes to Cluster
- Decommissioning a Node from Cluster
- Datanode Volume Failures
- Database Backups
- HDFS Metadata Backup
- Purging Older Log Files
Module 9: Extending Hadoop and Implementing Data Ingress
- Extending Hadoop Towards Data Lake
Module 10: Extending Hadoop and Implementing Data Ingress
- Hadoop Built-in Ingress and Egress Tools
Module 11: Planning for Backup, Recovery, and Security
- Introduction to Backup and Recovery
- Goals and Objectives
Module 12: Introduction to Big Data
- What is Big Data?
- Three V’s
- Sources of Big Data
Module 13: Storing Big Data
- Introduction to Big Data Storage
- Key Requirements of Big Data Storage
- Big Data Storage Architectures
Module 14: Processing Big Data
- Introduction to Data Processing
- Big Data Processing Frameworks
- What is a Traditional Approach?
- MapReduce
- Hadoop and Big Data
- Distributed Storage System
- YARN
- Hadoop 1.0/Hadoop 2.0
- Advantages of Hadoop
- Hadoop Ecosystem
- Hortonworks Data Platform
Module 15: Tools and Techniques to Analyse Big Data
- Apache Hadoop
- Microsoft HDInsight
- NoSQL
- Hive
- Sqoop
- PolyBase
- Big Data in Excel
- Presto
Module 16: Developing a Big Data Strategy
- Steps to Develop a Big Data Strategy
- Understanding Business Objectives
- Have a Clear Strategy for Hadoop
- Build a Data-Driven Culture
- Choose the Right Platform
- Start Small
Module 17: Implementing Big Data Solution
- Steps for Implementing a Big Data Solution
- Collect and Load Data
- Process, Query, Transform Data
- Consume and Visualise Data
- Build End-To-End Solutions
Included
Included
- No course includes are available.
Offered In This Course:
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Video Content
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eLearning Materials
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Study Resources
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Completion Certificate
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Tutor Support
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Interactive Quizzes
Learning Options
Discover a range of flexible learning options designed to meet your needs. Select the format that best supports your personal growth and goals.
Online Instructor-Led Training
- Live virtual classes led by experienced trainers, offering real-time interaction and guidance for optimal learning outcomes.
Online Self-Paced Training
- Flexible learning at your own pace, with access to comprehensive course materials and resources available anytime, anywhere.
Build your future with Oakwood International
We empower you with the skills, knowledge, and confidence to excel in your career. Join us and take the first step towards realising your professional goals.
Frequently Asked Questions
Q. What topics are covered in the Hadoop Big Data Certification Training?
The course covers HDFS,
MapReduce, YARN, Hive, Pig, HBase, Sqoop, integration with other tools, and
certification preparation.
Q. How can this training benefit my career?
This training enhances your
expertise in big data technologies, qualifying you for advanced data
engineering, analytics, and extensive data management roles.
Q. Is Hadoop applicable across industries?
Yes, Hadoop is widely used in
industries such as finance, healthcare, retail, and technology to manage and
analyse large datasets.
Q. What support is provided during the training?
Learners receive comprehensive
study materials, hands-on exercises, and expert instructor guidance to ensure
effective learning and certification readiness.
Q. Is this course suitable for beginners?
Yes, the course is designed for
professionals at all levels. It starts with foundational concepts and
progresses to advanced Hadoop functionalities.