Hadoop

Process big data efficiently, analyze and store vast amounts of data cost-effectively, with scalability, security, and simplicity.

FreemiumData & AnalyticsLinux, Windows (with workarounds), API (REST and native Java), Cloud (deployable on AWS, Azure, Google Cloud)

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What is Hadoop?

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It divides data and computation across multiple machines, allowing organisations to analyse and store vast amounts of information without requiring expensive, specialised hardware. The framework consists of HDFS for distributed file storage, MapReduce for parallel processing, and YARN for cluster resource management. Hadoop works well for batch processing tasks like log analysis, data warehousing, and machine learning data preparation where speed matters less than throughput and cost-effectiveness. It's particularly suited to organisations that generate large volumes of unstructured data and need to extract insights over time rather than in real-time.

Key Features

Distributed file system (HDFS)

stores data across multiple nodes with automatic replication for fault tolerance

MapReduce processing

breaks large computation jobs into smaller tasks that run in parallel across the cluster

YARN resource manager

allocates cluster resources and schedules jobs efficiently

Fault tolerance

automatically recovers from node failures without losing data or interrupting processing

Open source and vendor-neutral

runs on commodity hardware, avoiding vendor lock-in

Scalability

grows from single servers to thousands of machines

Pros & Cons

Advantages

Cost-effective for large-scale data processing; runs on inexpensive commodity hardware
Highly fault-tolerant; data replication ensures no loss if hardware fails
Proven at scale by organisations processing petabytes of data daily
Active open-source community with extensive documentation and tools
Flexible; works with structured and unstructured data across industries

Limitations

Steep learning curve; requires understanding of distributed systems concepts and Java programming
Slower than specialised databases for queries on smaller datasets
Requires significant operational overhead to maintain and tune clusters
Not suitable for real-time processing; designed for batch jobs that may take hours

Use Cases

Analysing server logs across web infrastructure to identify patterns and issues

Building data warehouses for business analytics on historical transaction data

Preparing large datasets for machine learning model training

Processing unstructured content like images or documents at scale

Running batch ETL jobs to transform raw data into clean, usable formats