Hadoop Testing Training
Introduction to Hadoop Testing
Gain an understanding of the importance of testing in Hadoop environments. Learn about different testing methodologies, strategies, and tools used to ensure the reliability and performance of Hadoop applications.
Setting Up a Testing Environment
Learn how to set up a testing environment for Hadoop. Understand the requirements for hardware and software, and get hands-on experience with configuring a test cluster to simulate real-world scenarios.
Unit Testing for MapReduce
Explore unit testing for MapReduce jobs. Learn about tools and frameworks such as MRUnit that help in testing MapReduce components, validating data processing logic, and ensuring correct job execution.
Testing Hadoop Applications with Apache Hive
Discover techniques for testing Hive queries and applications. Understand how to validate Hive scripts, perform query optimization checks, and ensure data integrity within the Hive data warehouse.
Integration Testing for Hadoop Ecosystem
Learn about integration testing for Hadoop components and applications. Understand how to test the interaction between different components like HDFS, YARN, Hive, and Pig to ensure seamless data processing workflows.
Performance Testing and Benchmarking
Dive into performance testing and benchmarking for Hadoop clusters. Learn how to measure system performance, identify bottlenecks, and use tools to benchmark Hadoop jobs and configurations.
Testing Data Quality and Consistency
Explore methods for testing data quality and consistency within Hadoop. Learn how to detect and address data anomalies, ensure data accuracy, and perform data validation across different sources and formats.
Automation in Hadoop Testing
Understand how to automate Hadoop testing processes. Learn about tools and frameworks for automating test execution, data generation, and result verification to streamline the testing lifecycle.
Monitoring and Logging for Testing
Discover the role of monitoring and logging in Hadoop testing. Learn how to use monitoring tools and log analysis to track test execution, identify issues, and gather insights for troubleshooting.
Handling Large-Scale Test Data
Learn strategies for handling large-scale test data in Hadoop environments. Understand how to generate, manage, and manipulate large datasets for comprehensive testing of Hadoop applications and processes.
Hands-On Labs and Projects
Engage in hands-on labs and projects to apply your Hadoop testing knowledge. Work on real-world scenarios to develop practical skills in testing MapReduce jobs, Hive queries, and other Hadoop components.
Hadoop Testing Syllabus
1. Introduction to Big Data Testing
- High Availability
- Scaling
- Advantages and Challenges
2. Introduction to Big Data
- What is Big Data
- Big Data Opportunities and Challenges
- Characteristics of Big Data
3. Introduction to Big Data Testing
- Big Data Testing Distributed File System
- Comparing Big Data Testing & SQL
- Industries Using Big Data Testing
- Data Locality
- Big Data Testing Architecture
- MapReduce & HDFS
- Using the Big Data Testing Single Node Image (Clone)
4. Big Data Testing Distributed File System (HDFS)
- HDFS Design & Concepts
- Blocks, Name Nodes, and Data Nodes
- HDFS High-Availability and HDFS Federation
- Big Data Testing DFS Command-Line Interface
- Basic File System Operations
- Anatomy of File Read and Write
- Block Placement Policy and Modes
- Configuration Files in Detail
- Metadata, FS Image, Edit Log, Secondary Name Node, and Safe Mode
- Adding and Decommissioning Data Nodes Dynamically
- FSCK Utility (Block Report)
- Overriding Default Configuration at System and Programming Levels
- HDFS Federation
- ZOOKEEPER Leader Election Algorithm
- Exercises and Small Use Cases on HDFS
5. MapReduce
- MapReduce Functional Programming Basics
- Map and Reduce Basics
- How MapReduce Works
- Anatomy of a MapReduce Job Run
- Legacy Architecture: Job Submission, Initialization, Task Assignment, Execution, Progress, and Status Updates
- Job Completion and Failures
- Shuffling and Sorting
- Splits, Record Reader, Partition, Types of Partitions & Combiner
- Optimization Techniques: Speculative Execution, JVM Reuse, Number of Slots
- Types of Schedulers and Counters
- Comparisons Between Old and New API at Code and Architecture Levels
- Getting Data from RDBMS into HDFS Using Custom Data Types
- Distributed Cache and Big Data Testing Streaming (Python, Ruby, and R)
- Sequential Files and Map Files
- Enabling Compression Codecs
- Map Side Join with Distributed Cache
- Types of I/O Formats: Multiple Outputs, NLINEInputFormat
- Handling Small Files Using CombineFileInputFormat
6. MapReduce Programming – Java
- Hands-on “Word Count” in MapReduce in Standalone and Pseudo Distribution Mode
- Sorting Files Using Big Data Testing Configuration API
- Emulating “grep” for Searching Inside a File
- DBInput Format
- Job Dependency API Discussion
- Input Format API Discussion, Split API Discussion
- Custom Data Type Creation
7. NoSQL
- ACID in RDBMS vs. BASE in NoSQL
- CAP Theorem and Types of Consistency
- Types of NoSQL Databases in Detail
- Columnar Databases in Detail (HBase and Cassandra)
- TTL, Bloom Filters, and Compensation
8. HBase
- HBase Installation and Concepts
- HBase Data Model and Comparison with RDBMS and NoSQL
- Master & Region Servers
- HBase Operations (DDL and DML) Through Shell and Programming
- Catalog Tables
- Block Cache and Sharding
- SPLITS
- Data Modeling (Sequential, Salted, Promoted, and Random Keys)
- Java APIs and REST Interface
- Client-Side Buffering and Processing 1 Million Records
- HBase Counters
- Enabling Replication and HBase RAW Scans
- HBase Filters
- Bulk Loading and Co-Processors (Endpoints and Observers)
- Real-World Use Case Consisting of HDFS, MapReduce, and HBase
9. Hive
- Hive Installation, Introduction, and Architecture
- Hive Services, Hive Shell, Hive Server, and Hive Web Interface (HWI)
- Meta Store and HiveQL
- OLTP vs. OLAP
- Working with Tables
- Primitive and Complex Data Types
- Working with Partitions
- User Defined Functions
- Hive Bucketed Tables and Sampling
- External Partitioned Tables
- Dynamic Partition
- ORDER BY vs. DISTRIBUTE BY vs. SORT BY
- Bucketing and Sorted Bucketing with Dynamic Partition
- RC File
- Indexes and Views
- Map Side Joins
- Compression on Hive Tables and Migrating Hive Tables
- Dynamic Substitution of Hive
- Log Analysis on Hive
- Accessing HBase Tables Using Hive
- Hands-on Exercises
10. Pig
- Pig Installation
- Execution Types
- Grunt Shell
- Pig Latin
- Data Processing
- Schema on Read
- Primitive and Complex Data Types
- Tuple Schema, BAG Schema, and MAP Schema
- Loading and Storing
- Filtering, Grouping, and Joining
- Debugging Commands
- Validations and Type Casting in Pig
- Working with Functions
- User Defined Functions
- Types of Joins in Pig and Replicated Join
- SPLITS and Multi-query Execution
- Error Handling, FLATTEN, and ORDER BY
- Parameter Substitution
- Nested For Each
- User Defined Functions, Dynamic Invokers, and Macros
- Accessing HBase Using Pig
- Loading and Writing JSON Data Using Pig
- Piggy Bank
- Hands-on Exercises
11. Sqoop
- Sqoop Installation
- Import Data (Full Table, Subset, Target Directory, Protecting Password, File Formats, Compressing, Control Parallelism)
- Incremental Import (New Data, Last Imported Data, Storing Password in Metastore)
- Free Form Query Import
- Export Data to RDBMS, Hive, and HBase
- Hands-on Exercises
12. HCatalog
- HCatalog Installation
- Introduction to HCatalog
- Interoperability with Hive and Pig
- Data Access and Metadata
- Hands-on Exercises
13. Oozie
- Oozie Installation and Configuration
- Introduction to Oozie Workflow and Coordinator
- Creating and Running Oozie Workflows
- Job Scheduling
- Complex Workflows and Actions
- Hands-on Exercises
14. Flume
- Flume Installation
- Introduction to Flume Architecture
- Flume Agents, Sources, Channels, and Sinks
- Data Collection and Aggregation
- Hands-on Exercises
15. Kafka
- Kafka Installation
- Introduction to Kafka Architecture
- Producers, Consumers, Topics, and Partitions
- Message Retention and Fault Tolerance
- Hands-on Exercises
16. Testing Big Data Applications
- Big Data Testing Approaches
- Unit Testing, Integration Testing, and Functional Testing
- Performance Testing and Benchmarking
- Data Quality and Integrity Testing
- Tools and Frameworks for Big Data Testing
- Hands-on Testing Scenarios
17. Case Studies and Real-World Applications
- Industry Case Studies
- Real-World Applications and Use Cases
- Discussion and Analysis
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