Big Data Hadoop Practice Exam
Big Data Hadoop Practice Exam
About Big Data Hadoop Exam
The Big Data Hadoop Certification Exam is designed to assess a candidate’s proficiency in managing and analyzing large-scale datasets using the Hadoop ecosystem. As organizations increasingly rely on massive volumes of data to drive strategic decisions, professionals with expertise in Hadoop and related big data tools are in high demand. This certification evaluates both theoretical knowledge and hands-on experience with core Hadoop components, including HDFS (Hadoop Distributed File System), MapReduce, YARN, and the broader ecosystem tools like Hive, Pig, HBase, Sqoop, Flume, and Spark. It is intended to confirm a candidate's ability to handle real-world data processing, data ingestion, storage, and analytical tasks using Hadoop technologies.
Who should take the Exam?
The Big Data Hadoop Certification Exam is ideally suited for professionals involved in or aspiring to work with large-scale data systems. It is particularly beneficial for:
- Data Engineers who design and implement scalable data pipelines using Hadoop technologies.
- Data Analysts and Business Intelligence Professionals seeking to analyze and report on large datasets.
- Software Developers and IT Professionals looking to expand their expertise into big data environments.
- System Administrators responsible for setting up and managing Hadoop clusters.
- Data Scientists aiming to work with Hadoop-based frameworks for data storage and processing.
- Students and Graduates with a background in computer science, IT, or data analytics who wish to validate their skills in Hadoop.
Skills Required
Candidates preparing for the Big Data Hadoop Certification Exam should possess a combination of technical knowledge, hands-on experience, and a solid grasp of big data concepts:
- Understanding of Distributed Computing Principles and how Hadoop solves large-scale data challenges.
- Familiarity with Hadoop Architecture, including HDFS, YARN, and MapReduce.
- Proficiency in Hadoop Ecosystem Tools such as Hive, Pig, HBase, Sqoop, Flume, and Spark.
- Basic Programming Skills, particularly in Java, Python, or Scala.
- Data Ingestion and ETL Workflows, including batch and real-time data processing.
- Querying and Analyzing Large Datasets using HiveQL or Pig Latin.
- Cluster Management and Performance Tuning for Hadoop environments.
- Knowledge of Data Security and Governance within the Hadoop ecosystem.
Knowledge Gained
Upon passing the certification, candidates will gain in-depth expertise in the following areas:
- Building and Deploying Hadoop Applications to manage and analyze big data workloads.
- Setting Up and Configuring Hadoop Clusters for efficient and secure data processing.
- Storing and Retrieving Data from HDFS, with considerations for performance and fault tolerance.
- Writing and Optimizing MapReduce Jobs for processing structured and unstructured data.
- Utilizing Hive, Pig, and HBase to perform complex data analysis and processing tasks.
- Importing and Exporting Data Using Sqoop and Handling Streaming Data with Flume.
- Integrating Hadoop with Spark for advanced analytics and in-memory computing.
- Ensuring Data Security, Access Control, and Compliance within the Hadoop framework.
Course Outline
Domain 1 - Introduction to Big Data and Hadoop- Understanding the characteristics of big data (volume, velocity, variety, veracity)
- Limitations of traditional systems and the need for Hadoop
- Hadoop architecture overview and components
Domain 2 - Hadoop Distributed File System (HDFS)
- Architecture and replication
- Data blocks and fault tolerance
- Read/write operations and file commands
Domain 3 - MapReduce Framework
- MapReduce programming model
- Developing MapReduce jobs
- Combiner, Partitioner, and Reducer roles
- Performance tuning of MapReduce applications
Domain 4 - YARN (Yet Another Resource Negotiator)
- YARN architecture and components
- Resource Manager, Node Manager, Application Master
- Job scheduling and execution flow
Domain 5 - Apache Hive
- Hive architecture and components
- Writing HiveQL queries
- Partitioning, bucketing, and optimization
- Integrating Hive with HDFS and other tools
Domain 6 - Apache Pig
- Pig architecture and data flow
- Writing Pig Latin scripts
- Use cases and comparison with Hive
Domain 7 - NoSQL with HBase
- Introduction to HBase and column-oriented databases
- HBase architecture and data modeling
- Performing CRUD operations in HBase
Domain 8 - Data Ingestion Tools
- Apache Sqoop: Import/export between Hadoop and RDBMS
- Apache Flume: Streaming data into Hadoop
- Best practices for data ingestion pipelines
Domain 9 - Apache Spark Integration
- Introduction to Spark and its components
- Using Spark for batch and real-time processing
- Integration with Hadoop and HDFS
Domain 10 - Cluster Configuration and Management
- Setting up a multi-node Hadoop cluster
- Managing cluster resources and users
- Monitoring and troubleshooting Hadoop jobs
Domain 11 - Security and Governance
- Authentication, authorization, and encryption
- Role-based access control
- Data lineage and auditing
