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Apache Hadoop

Rating:

3.9 / 5.0

Tags

big data, data processing, distributed storage, analytics, open-source, hadoop ecosystem, HDFS, MapReduce, YARN

Categories:
Data Analysis
Creator Apache Software Foundation
Date 2006-04-01
Platforms Software framework, computing system
Status Active
Website hadoop.apache.org
Price Model Free (Open Source)
Sections:
Big Data Processing

Pricing Details

Free and open-source. Costs are related to infrastructure and managed services on cloud platforms.

Features

Distributed File System (HDFS); Parallel processing (MapReduce); Cluster resource management (YARN); Scalability; Fault tolerance; Cost-effectiveness; Flexibility; Data replication; Data locality; Open-source; Ecosystem of related tools.

Integrations

Integration with Hadoop ecosystem tools (Spark, Hive, Pig, Impala, Kafka, etc.); Compatibility with cloud platforms (AWS S3, Azure Blob Storage, Google Cloud Storage); Integration with databases (HBase, Cassandra); Support for various data formats; APIs for various programming languages.

Preview

Apache Hadoop is a foundational open-source framework for working with big data. It provides a distributed file system (HDFS) for reliable storage of very large datasets across clusters of commodity servers and a parallel data processing model (MapReduce), as well as a cluster resource manager (YARN) for efficient task and resource allocation. While Hadoop itself does not include artificial intelligence algorithms, it is a critically important foundation for running machine learning and data analytics workloads at scale. Hadoop enables storing and processing petabytes of data, making it available to analytical engines and machine learning libraries such as Apache Spark (with MLlib), Apache Hive, Apache Pig, and other tools in the Hadoop ecosystem. It is widely applied in various industries, including finance (risk analysis, fraud detection), healthcare (processing medical records), retail (customer behavior analysis), and telecommunications (traffic management). Hadoop is characterized by high scalability, fault tolerance, and cost-effectiveness through the use of standard hardware. The project is actively developed and supported by a large community.