Hadoop Big Data Test

This skill assesses knowledge of importing and streaming data into Hadoop using tools like Sqoop and Flume. Candidates should know how to move data between relational databases and HDFS, configure Flume agents, sinks, and sources, and manage data ingestion workflows. Mastery here ensures candidates can build robust pipelines that feed downstream analytics and machine learning models.

This added skill emphasizes practical data manipulation using Hadoop tools. It includes designing efficient Hive queries for joins and aggregations, choosing the right tool (Hive vs Pig), analyzing transformation results, and handling nulls or skewed data distributions. This is key for data engineers and analysts who convert raw input into insights, ensuring processing logic aligns with business goals.

YARN (Yet Another Resource Negotiator) is Hadoop’s cluster resource manager. This skill area tests knowledge of how YARN schedules and allocates CPU and memory resources across jobs and applications. It includes understanding the ResourceManager, NodeManager, container lifecycle, scheduling policies (e.g., Fair, Capacity), and Docker-based execution. Proficiency here ensures candidates can troubleshoot resource contention, manage application parallelism, and optimize cluster performance.

This skill evaluates proficiency in declarative data processing using HiveQL and Pig Latin. Hive enables SQL-like queries over distributed data, while Pig offers a procedural data flow language for ETL tasks. Candidates should understand schema definition, partitioning, bucketing, joins, and transformations. These tools abstract complexity and are widely used in data warehousing and reporting scenarios on Hadoop.

MapReduce is Hadoop’s core data processing paradigm. This skill evaluates a candidate’s ability to write, configure, and optimize MapReduce jobs. It covers map/reduce functions, combiners, partitioners, shuffle and sort, speculative execution, and counters. Optimizing MapReduce directly impacts performance and resource utilization. A solid grasp of this model is essential for developers working with legacy Hadoop systems or where batch processing still dominates.

Apache Spark is often deployed alongside Hadoop for faster in-memory processing. This skill evaluates knowledge of integrating Spark with HDFS and YARN, understanding RDDs vs DataFrames, job DAGs, Spark submit options, and performance tuning. Candidates should demonstrate how to leverage Spark for faster ETL, iterative ML, or SQL workloads on top of Hadoop's data layer.

This skill focuses on managing multi-stage data processing using Oozie or other orchestration tools. It includes defining workflows, coordinators, bundles, retry logic, and error handling. Effective orchestration ensures pipeline reliability, reusability, and observability. This is essential for production environments where tasks like ingestion, transformation, and export are chained and scheduled regularly.

This area evaluates familiarity with the broader Hadoop ecosystem and governance practices. It includes tools like HBase, Zookeeper, Ambari, Atlas, and understanding of metadata, lineage, and data cataloging. Candidates should also grasp how different components interact, enabling scalable and secure data architecture. Strong knowledge here ensures operational cohesion and accountability across teams.

This skill tests understanding of Hadoop’s security model, including Kerberos authentication, Ranger/ACL policies, encryption at rest and in transit, and fine-grained authorization. Data engineers working in enterprise contexts must ensure compliance with security standards and prevent unauthorized access to sensitive data. This skill ensures readiness for roles in regulated or sensitive data environments.

This skill area covers cluster setup, XML configuration files (e.g., core-site.xml, hdfs-site.xml), federation, HA setup, and upgrade processes. Candidates should be comfortable with both on-prem and cloud-based deployments. Cluster configuration is foundational for Hadoop admins and architects to ensure scalability, resilience, and maintainability of the ecosystem.

This skill assesses the ability to diagnose and resolve Hadoop job or cluster issues using logs, metrics, and monitoring tools like Ambari, Ganglia, or custom dashboards. It includes interpreting YARN job logs, Spark UIs, resource usage patterns, and system alerts. Proficiency here is crucial for operational stability, especially in 24/7 data platforms.

Modern data teams often run Hadoop on AWS EMR, GCP Dataproc, or Azure HDInsight. This skill tests understanding of ephemeral HDFS, autoscaling clusters, preemptible nodes, object store integration (e.g., S3, GCS), and cost management. Candidates who master this area are prepared for hybrid or fully cloud-native architectures.