Hadoop Ecosystem Training
Introduction to Hadoop Ecosystem
Gain a comprehensive overview of the Hadoop ecosystem. Learn about the key components and tools that integrate with Hadoop to build a robust big data processing and analysis environment.
Hadoop Distributed File System (HDFS)
Explore the Hadoop Distributed File System (HDFS), which provides scalable and reliable storage for large datasets. Understand its architecture, data storage mechanisms, and management operations.
YARN (Yet Another Resource Negotiator)
Learn about YARN, the resource management layer of Hadoop. Discover how YARN manages cluster resources, schedules tasks, and monitors job execution to optimize the performance of the Hadoop cluster.
MapReduce
Dive into MapReduce, the programming model used for processing large-scale data across distributed systems. Understand the MapReduce framework, its components, and how it integrates with Hadoop for data processing.
Apache Hive
Discover Apache Hive, a data warehouse system built on top of Hadoop. Learn how Hive provides a SQL-like interface for querying and managing large datasets and how it integrates with HDFS and other Hadoop components.
Apache Pig
Explore Apache Pig, a high-level platform for creating MapReduce programs. Learn about Pig Latin, its scripting language, and how Pig simplifies data processing tasks in the Hadoop ecosystem.
Apache HBase
Learn about Apache HBase, a distributed, scalable NoSQL database that runs on top of HDFS. Understand its architecture, data model, and use cases for real-time read/write access to large datasets.
Apache ZooKeeper
Discover Apache ZooKeeper, a centralized service for maintaining configuration information, naming, and providing distributed synchronization. Learn how ZooKeeper supports other components in the Hadoop ecosystem.
Apache Flume
Explore Apache Flume, a distributed service for efficiently collecting, aggregating, and moving large amounts of log data. Understand how Flume integrates with Hadoop to handle data ingestion from various sources.
Apache Oozie
Learn about Apache Oozie, a workflow scheduler system for managing Hadoop jobs. Discover how Oozie helps in scheduling, coordinating, and managing complex data processing workflows within the Hadoop ecosystem.
Apache Spark
Understand Apache Spark, a fast and general-purpose cluster computing system. Learn how Spark complements Hadoop by providing in-memory processing capabilities and advanced analytics for big data.
Hands-On Labs and Projects
Engage in hands-on labs and projects to apply your knowledge of the Hadoop ecosystem. Work on real-world scenarios to develop practical skills in using and integrating various components of the ecosystem.
Hadoop Ecosystem Syllabus
1. Introduction to Big Data and Hadoop
- Understanding Big Data
- Challenges with Traditional Systems
- Introduction to Hadoop
- History of Hadoop
- Hadoop Architecture Overview
2. Hadoop Distributed File System (HDFS)
- HDFS Architecture
- HDFS Operations
- HDFS Commands
- HDFS Federation and High Availability
- HDFS Permissions and Security
3. MapReduce
- MapReduce Basics
- MapReduce Architecture
- Writing MapReduce Jobs
- MapReduce Phases (Map, Shuffle, Reduce)
- MapReduce Job Optimization
- MapReduce Best Practices
4. YARN (Yet Another Resource Negotiator)
- YARN Architecture
- YARN Components (Resource Manager, Node Manager, Application Master)
- YARN Scheduling
- YARN Capacity Scheduler
- YARN Fair Scheduler
5. Hadoop Ecosystem Tools
- Apache Pig
- Pig Latin Basics
- Pig Execution Modes
- Pig Optimization Techniques
- Apache Hive
- Hive Query Language (HiveQL)
- Hive Architecture
- Hive Optimization Techniques
- Apache HBase
- HBase Architecture
- HBase Data Model
- HBase Operations
- Apache Sqoop
- Importing and Exporting Data with Sqoop
- Sqoop Connectors
- Apache Flume
- Flume Architecture
- Flume Sources, Channels, and Sinks
- Apache Oozie
- Oozie Workflow
- Oozie Coordinator and Bundle
6. Data Ingestion and Integration
- Real-time and Batch Data Ingestion
- Data Integration Best Practices
- Data Serialization Formats (Avro, Parquet, Sequence File, etc.)
7. Data Analysis and Visualization
- Introduction to Data Analysis
- Introduction to Data Visualization
- Tools for Data Analysis (Apache Zeppelin, Jupyter Notebooks, etc.)
- Tools for Data Visualization (Tableau, Power BI, etc.)
8. Hadoop Security
- Kerberos Authentication
- Hadoop Security Best Practices
- Hadoop Encryption
- Hadoop Audit Logging
9. Monitoring and Management
- Hadoop Cluster Monitoring
- Log Management
- Hadoop Backup and Recovery
- Hadoop Cluster Upgrades
10. Advanced HDFS
- HDFS Federation and High Availability in Depth
- NameNode Federation
- HDFS Snapshots
- HDFS Quotas
- Erasure Coding in HDFS
11. Advanced MapReduce
- Combiners and Partitioners
- Custom Input and Output Formats
- Secondary Sorting
- MapReduce Counters and Distributed Cache
- MapReduce Performance Tuning
- MapReduce Output Compression
12. YARN Optimization
- Node Labels and Node Attributes
- YARN Resource Allocation Tuning
- YARN Resource Localization
- YARN NodeManager Disk Health Checks
- YARN Timeline Service and History Server
13. Advanced Apache Hive
- Hive Transactions and ACID Support
- Hive Vectorization and Cost-Based Optimization
- Hive Indexes and Materialized Views
- Hive Windowing Functions
- Hive Authorization and Row-Level Security
14. Advanced Apache Spark
- Spark SQL Optimization
- Spark Structured Streaming
- Spark MLlib for Machine Learning
- Spark GraphX for Graph Processing
- Spark Performance Tuning
15. Apache Kafka Streams
- Kafka Streams DSL
- Stateful Stream Processing
- Exactly Once Semantics
- Kafka Connect Sink and Source Connectors
- Kafka Security with SSL and SASL
16. Advanced Data Analysis
- Advanced Analytics Techniques (Predictive Analytics, Prescriptive Analytics)
- Time Series Analysis with Hadoop
- Graph Analytics with Hadoop
- Text Analytics with Hadoop
- Spatial Analytics with Hadoop
17. Security Enhancements
- Apache Ranger for Authorization and Audit
- Apache Knox for Gateway Security
- Hadoop Encryption Zones
- Secure Hadoop Cluster Deployment Best Practices
18. Cloud Integration
- Hadoop on Cloud Platforms (AWS EMR, Azure HDInsight, Google Cloud Dataproc)
- Hybrid Cloud Deployment Strategies
- Hadoop Cluster Autoscaling
19. Real-world Use Cases and Best Practices
- Hadoop for IoT Analytics
- Hadoop for Financial Analytics
- Hadoop for Healthcare Analytics
- Hadoop for E-commerce Analytics
- Industry-specific Case Studies and Solutions
20. Performance Benchmarking and Troubleshooting
- Hadoop Cluster Performance Benchmarking
- Hadoop Cluster Troubleshooting Techniques
- Performance Monitoring with Grafana and Prometheus
21. Emerging Technologies and Trends
- Serverless Hadoop with AWS Glue and Azure Databricks
- Edge Computing with Hadoop
- Quantum Computing and Hadoop
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details