Hadoop Ecosystem & Related Tools Questions
Overview of the Hadoop ecosystem components (e.g., HDFS, MapReduce, YARN) and related tools (Hive, Pig, HBase, Sqoop, Flume, Oozie, Hue, etc.). Covers batch and streaming data processing, data ingestion and ETL pipelines, data warehousing in Hadoop, and operational considerations for deploying and managing Hadoop-based data pipelines in modern data architectures.
MediumTechnical
0 practiced
You are seeing thousands of small files (~10KB each) in HDFS causing NameNode memory pressure and slow map tasks. Propose three different approaches to mitigate the small-files problem, explain trade-offs for each, and outline when each approach is most appropriate.
HardTechnical
0 practiced
Design a policy enforcement system for data governance that integrates Hive, HBase, and Spark. The system should support row and column-level authorization, dynamic masking, audit logging, and a central policy catalog. Explain enforcement points (query engine plugins, data masking during ETL), policy distribution, and performance considerations.
HardTechnical
0 practiced
Interactive SQL on your Hive/Impala cluster shows a long tail latency for a subset of queries. Propose a systematic approach to reduce tail latency: include strategies such as query result caching, resource isolation (queues/tenants), adaptive query planning, data layout changes (partitioning/bucketing), and client-side strategies (retries, hedged requests).
MediumSystem Design
0 practiced
Design monitoring and alerting for a Hadoop-based daily ingestion pipeline. List specific metrics (job duration, success/failure count, data volume, late partitions, data quality checks), choose monitoring tools (Ambari/Cloudera Manager, Prometheus + Grafana), and propose alert thresholds and runbook actions for key alerts.
EasyTechnical
0 practiced
Describe the common Hadoop file formats Avro, Parquet, and ORC. For each format, explain whether it is row or columnar, how it handles schema (schema-on-read vs schema-in-file), typical compression choices, and which format you would choose for: (a) streaming events with schema evolution, (b) large analytical queries with column pruning.
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