ArchGen

Hadoop Architecture Diagram

Focus: HDFS + YARN + Hive. Key areas: Sqoop, JDBC, YARN.

Use this as a block diagram of the system when explaining architecture.

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Diagram caption: Hadoop Architecture Diagram (HDFS + YARN + Hive) has 4 layers: Ingestion & Access, HDFS Storage, Processing & Query, Coordination & Security.

Prompt

Hadoop architecture diagram for a data platform. Batch data arrives via Sqoop from relational databases and Flume/Kafka for log streams. Store data in HDFS with NameNode metadata and replicated DataNodes. Processing runs on YARN with Spark and MapReduce; SQL analytics use Hive and Presto. Coordinate services with ZooKeeper and secure the cluster with Kerberos and Ranger. Include monitoring and capacity management.
Highlights
  • Key flows · Ingestion flow: batch data lands via Sqoop and streaming logs via Kafka/Flume, both written into HDFS for durable storage.
  • Key flows · Processing flow: YARN schedules Spark and MapReduce jobs, outputs are stored back in HDFS and exposed through Hive for SQL analytics.
  • Layer details · HDFS Storage: Modules include NameNode, DataNodes, Metadata & Replication.

Overview

Hadoop Architecture Diagram (HDFS + YARN + Hive) has 4 layers: Ingestion & Access, HDFS Storage, Processing & Query, Coordination & Security.

Layer details

Show all (4)
  • Ingestion & Access: Modules include Batch Ingestion, Stream Ingestion, Client Access.
  • HDFS Storage: Modules include NameNode, DataNodes, Metadata & Replication.
  • Processing & Query: Modules include YARN Resource Manager, Batch Processing, SQL & BI Access.
  • Coordination & Security: Modules include Cluster Coordination, Security & Governance, Monitoring & Capacity.

Module responsibilities

Show all (12)
  • Ingestion & Access / Batch Ingestion: Extract source data; Load to HDFS; Handle retries
  • Ingestion & Access / Stream Ingestion: Ingest streams; Buffer events; Route data
  • Ingestion & Access / Client Access: Expose APIs; Authorize access; Submit jobs
  • HDFS Storage / NameNode: Track files; Manage replication; Coordinate cluster
  • HDFS Storage / DataNodes: Store data blocks; Serve reads/writes; Report health
  • HDFS Storage / Metadata & Replication: Protect durability; Optimize placement; Enable recovery
  • Processing & Query / YARN Resource Manager: Allocate resources; Manage queues; Enforce SLAs
  • Processing & Query / Batch Processing: Process datasets; Run aggregations; Persist outputs
  • Processing & Query / SQL & BI Access: Provide SQL layer; Optimize queries; Serve analytics
  • Coordination & Security / Cluster Coordination: Coordinate services; Maintain quorum; Handle failover
  • Coordination & Security / Security & Governance: Secure access; Protect data; Track usage
  • Coordination & Security / Monitoring & Capacity: Monitor health; Plan capacity; Detect failures

Key flows

Show all (3)
  • Ingestion flow: batch data lands via Sqoop and streaming logs via Kafka/Flume, both written into HDFS for durable storage.
  • Processing flow: YARN schedules Spark and MapReduce jobs, outputs are stored back in HDFS and exposed through Hive for SQL analytics.
  • Governance flow: Kerberos authenticates users, Ranger enforces policies, and audit logs are collected for compliance.