🐘Hadoop

NEW

Also known as: Apache Hadoop

Open-source framework for distributed storage and parallel processing of large datasets across clusters.

DESCRIPTION

Apache Hadoop provides HDFS for distributed storage and MapReduce for parallel computation across commodity hardware. YARN manages resources, and ecosystem tools like Hive and Pig enable SQL-like processing. Hadoop pioneered scalable big data processing in on-prem clusters. Cloud-native services and Spark have shifted workloads, yet Hadoop remains relevant for batch processing and archival storage. Modern deployments often integrate Hadoop storage with cloud compute.

ALTERNATIVE DEFINITIONS

Apache Hadoop

Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

Official definition emphasizing distributed processing and programming model.

Source

Wikipedia

Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation.

Highlights utility collection and massive-scale computation.

Source

IBM

Hadoop is an open-source framework for distributed storage and processing of big data using the MapReduce programming model.

Emphasizes MapReduce model for big data processing.

Source

RELATED TERMS

HDFS MapReduce YARN Apache Hive Pig Apache HBase Distributed Computing Big Data Cluster

COMPETING TERMS

Apache Spark Cloud Data Warehouse

KNOWLEDGE AREAS

Data Engineering Infrastructure Data Storage & Operations Cloud

TARGET AUDIENCE

Data Engineer Data Architect Cloud Architect

EXTERNAL SOURCES

• Wikipedia - Apache Hadoop
• Apache Hadoop - Official Hadoop documentation
• Apache Hadoop - HDFS architecture and design
• Cloudera - Enterprise Hadoop ecosystem docs
• Apache Spark - Complementary processing engine

TOP QUESTIONS

What components make up Hadoop?

HDFS for storage, MapReduce for computation, and YARN for resource management; plus ecosystem tools like Hive, Pig, and HBase.

Category: Overview

Why was Hadoop transformative for big data?

It enabled scalable, fault-tolerant processing on commodity clusters, democratizing large-scale analytics.

Category: Relevancy

How does Hadoop compare to Spark?

Spark provides in-memory processing and broader APIs, often outperforming MapReduce for iterative workloads.

Category: Distinction

What are common mistakes in Hadoop deployments?

Underestimating ops complexity, misconfiguring HDFS replication, and poor resource management lead to bottlenecks and failures.

Category: Mistakes

Is Hadoop still relevant in cloud-native architectures?

Yes for batch storage/processing and hybrid setups, though many workloads shift to managed cloud services and Spark.

Category: Opinion

How is data secured in Hadoop?

Use Kerberos, HDFS encryption, Ranger/Sentry for authorization, and network-level controls.

Category: Requirements

What workloads suit Hadoop best?

Large-scale batch ETL, log processing, and archival analytics over petabyte datasets.

Category: Example

How does HDFS ensure fault tolerance?

Replication across nodes and rack-aware placement provide resilience against node and rack failures.

Category: Concept

What skills are needed to manage Hadoop?

Linux administration, cluster operations, resource tuning, and familiarity with ecosystem tools like Hive and Spark.

Category: Requirements