Hadoop Developer Practice Exam
Hadoop Developer Practice Exam
About Hadoop Developer Exam
The Hadoop Developer Exam evaluates a candidate’s proficiency in developing robust, scalable applications using the Hadoop ecosystem. It focuses on core concepts like MapReduce, HDFS, Hive, Pig, Sqoop, and performance tuning techniques necessary for efficient big data application development.
Who should take the Exam?
This exam is ideal for:
- Software developers building data-intensive applications
- Big data engineers and ETL developers working with Hadoop tools
- Data analysts and architects managing large-scale datasets
- Java developers transitioning into big data roles
- Students or professionals preparing for Hadoop certification exams
Skills Required
- Strong programming knowledge in Java (or Python)
- Understanding of distributed computing principles
- Familiarity with Hadoop architecture and its core components
- Ability to write and optimize MapReduce programs
Knowledge Gained
- Proficiency in HDFS and YARN architecture
- Developing MapReduce jobs for data processing
- Using Hive, Pig, and Sqoop for data manipulation and integration
- Debugging, optimizing, and deploying Hadoop jobs in real-world scenarios
- Understanding workflow schedulers like Oozie
Course Outline
The Hadoop Developer Exam covers the following topics -
Module 1 – Introduction to Hadoop and Big Data
- Hadoop ecosystem overview
- Characteristics of big data and distributed processing
- HDFS and YARN architecture
Module 2 – MapReduce Programming
- Writing Mapper, Reducer, and Driver classes
- Input/output formats and data flow
- Combiner, partitioner, and counters
Module 3 – Working with Hive and Pig
- HiveQL for querying large datasets
- Creating and managing Hive tables and partitions
- Pig scripting for data transformation and analysis
Module 4 – Data Ingestion with Sqoop and Flume
- Importing/exporting data using Sqoop
- Streaming logs with Flume
- Connecting Hadoop with relational databases
Module 5 – Performance Tuning and Optimization
- Best practices for writing efficient MapReduce code
- Managing memory and resource allocation
- Job counters, logs, and debugging tools
Module 6 – Real-World Project and Workflow Automation
- End-to-end data processing pipelines
- Automating tasks using Oozie
- Integrating Hadoop jobs into production environments
