15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Big Data Engineers
- Hadoop Administrators
- Data Architects
- Software Developers
- Data Analysts
- System Administrators
- Anyone needing deep Hadoop expertise
Session Objectives
- Understand the fundamentals of Cassandra and MongoDB.
- Design and implement scalable database architectures using Cassandra.
- Utilize MongoDB for flexible and schema-less data storage.
- Master querying techniques for both Cassandra and MongoDB.
- Optimize database performance for high-throughput applications.
- Implement data modeling best practices for NoSQL databases.
- Configure and manage Cassandra and MongoDB
- Troubleshoot and debug database issues.
- Implement data security and access control.
- Integrate Cassandra and MongoDB with other data systems.
- Understand how to monitor and maintain NoSQL databases.
- •Explore advanced features of both Cassandra and MongoDB.
- Apply real world use cases for Cassandra and MongoDB.
About the Course
Build enterprise-scale Big Data solutions with our Hadoop Ecosystem Mastery Training Course. Tailored for data engineers, developers, and system administrators, this hands-on program delivers deep practical expertise in managing and processing massive datasets. Guided by industry professionals, you will learn to configure, optimize, and scale core Hadoop components—mastering HDFS for distributed storage, MapReduce for parallel computing, and YARN for resource allocation—to solve complex data challenges at scale.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of the Hadoop ecosystem.
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Subtopic 1.2:
Architecture and components of Hadoop.
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Subtopic 1.3:
Setting up a Hadoop development environment.
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Subtopic 1.4:
Understanding Hadoop distributions and tools.
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Subtopic 1.5:
Introduction to Hadoop use cases.
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Subtopic 2.1:
Architecture and design of HDFS.
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Subtopic 2.2:
Working with HDFS commands and file operations.
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Subtopic 2.3:
Configuring and managing HDFS clusters.
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Subtopic 2.4:
Data replication and fault tolerance in HDFS.
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Subtopic 2.5:
Optimizing HDFS performance.
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Subtopic 3.1:
Fundamentals of the MapReduce paradigm.
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Subtopic 3.2:
Developing MapReduce jobs in Java.
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Subtopic 3.3:
Implementing data transformations and aggregations.
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Subtopic 3.4:
Optimizing MapReduce job performance.
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Subtopic 3.5:
Understanding MapReduce input and output formats.
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Subtopic 4.1:
Architecture and components of YARN.
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Subtopic 4.2:
Managing resources with YARN.
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Subtopic 4.3:
Configuring YARN schedulers.
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Subtopic 4.4:
Understanding YARN application lifecycle.
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Subtopic 4.5:
Optimizing YARN resource allocation.
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Subtopic 5.1:
Installing and configuring Hadoop clusters.
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Subtopic 5.2:
Managing Hadoop services and daemons.
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Subtopic 5.3:
Monitoring Hadoop cluster health.
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Subtopic 5.4:
Implementing security in Hadoop clusters.
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Subtopic 5.5:
Troubleshooting Hadoop cluster issues.
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Subtopic 6.1:
Using Sqoop for data ingestion from relational databases.
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Subtopic 6.2:
Utilizing Flume for streaming data ingestion.
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Subtopic 6.3:
Implementing data processing with Pig and Hive.
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Subtopic 6.4:
Integrating Hadoop with other data sources.
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Subtopic 6.5:
Best practices for data ingestion and processing.
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Subtopic 7.1:
Implementing authentication and authorization in Hadoop.
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Subtopic 7.2:
Data encryption and access control.
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Subtopic 7.3:
Auditing and compliance in Hadoop environments.
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Subtopic 7.4:
Data governance and metadata management.
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Subtopic 7.5:
Security best practices for Hadoop.
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Subtopic 8.1:
Optimizing HDFS performance.
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Subtopic 8.2:
Tuning MapReduce and YARN jobs.
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Subtopic 8.3:
Configuring Hadoop parameters for performance.
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Subtopic 8.4:
Monitoring and analyzing Hadoop performance.
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Subtopic 8.5:
Troubleshooting performance bottlenecks.
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Subtopic 9.1:
Exploring HBase for NoSQL data storage.
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Subtopic 9.2:
Utilizing Spark for advanced data processing.
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Subtopic 9.3:
Integrating Kafka for real-time data streaming.
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Subtopic 9.4:
Using Oozie for workflow management.
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Subtopic 9.5:
Overview of other Hadoop ecosystem tools.
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Subtopic 10.1:
Implementing HDFS high availability.
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Subtopic 10.2:
Configuring YARN high availability.
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Subtopic 10.3:
Designing disaster recovery strategies for Hadoop.
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Subtopic 10.4:
Implementing backup and recovery procedures.
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Subtopic 10.5:
Ensuring data durability and availability.
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Subtopic 11.1:
Utilizing Hadoop monitoring tools.
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Subtopic 11.2:
Implementing alerting and notifications.
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Subtopic 11.3:
Analyzing Hadoop logs and metrics.
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Subtopic 11.4:
Using Ambari and Cloudera Manager.
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Subtopic 11.5:
Best practices for Hadoop monitoring.
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Subtopic 12.1:
Advanced HDFS configurations and features.
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Subtopic 12.2:
Implementing custom YARN schedulers.
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Subtopic 12.3:
Utilizing HDFS federation and security zones.
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Subtopic 12.4:
Advanced resource management in YARN.
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Subtopic 12.5:
Advanced techniques for data locality.
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Subtopic 13.1:
Implementing complex MapReduce patterns.
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Subtopic 13.2:
Utilizing MapReduce libraries and frameworks.
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Subtopic 13.3:
Advanced data partitioning and sorting.
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Subtopic 13.4:
Implementing custom input and output formats.
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Subtopic 13.5:
Advanced techniques for data aggregation.
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Subtopic 14.1:
Deploying Hadoop on cloud platforms.
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Subtopic 14.2:
Managing cloud resources for Hadoop.
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Subtopic 14.3:
Cloud-specific performance tuning.
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Subtopic 14.4:
Security considerations for cloud deployments.
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Subtopic 14.5:
Cost optimization for cloud based systems.
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Subtopic 15.1:
Emerging trends in the Hadoop ecosystem.
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Subtopic 15.2:
Integrating Hadoop with AI and machine learning platforms.
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Subtopic 15.3:
Advanced techniques for large-scale data processing.
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Subtopic 15.4:
Advanced techniques for real time processing within Hadoop.
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Subtopic 15.5:
Future of Hadoop in modern data architectures.
