Hadoop Big Data Certification

• Educational Background: No prior experience with Hadoop or Big Data is required; however, a basic understanding of programming concepts, databases, and data management will enhance the learning experience. 

• Language Proficiency: Learners should have a good command of English, as all course materials, assessments, and discussions are conducted in English. 

• Interest in Big Data: This course is ideal for individuals with a keen interest in data analysis, database management, and technology, and those looking to advance in the Big Data and analytics field. 

• Master Hadoop Ecosystem: Gain a comprehensive understanding of Hadoop and its ecosystem, including components like HDFS, MapReduce, and YARN, to process large datasets effectively. 

• Develop Data Processing Skills: Learn to use various Hadoop tools and frameworks such as Hive, Pig, and HBase to process, manage, and analyse big data efficiently. 

• Implement Big Data Solutions: Acquire the practical skills needed to design and implement data storage, processing, and analysis pipelines using Hadoop and related technologies. 

• Optimise Data Performance: Understand how to optimise performance, ensure data reliability, and implement fault-tolerant systems within the Hadoop ecosystem. 

• Prepare for Advanced Big Data Roles: Lay a strong foundation for further career growth in Big Data, analytics, and data engineering, positioning yourself for advanced roles in the field. 

The Hadoop Big Data Certification is ideal for professionals and learners aiming to develop their skills in managing and processing large datasets. Below are the individuals who can benefit from this course: 

• Data Engineer 

• Data Scientist 

• Business Intelligence Analyst 

• IT Professional 

• Database Administrator 

• Big Data Analyst 

• Software Engineer 

• System Architect 

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