Ecosistema Hadoop

Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It’s designed to handle massive amounts of data across clusters of commodity hardware. Here are some key components of the Hadoop ecosystem:

1. Hadoop Distributed File System (HDFS): HDFS is the storage component of Hadoop, designed to store data across multiple nodes in a cluster. It provides fault tolerance and high availability.

2. MapReduce: MapReduce is a programming model and processing engine used for distributed data processing in Hadoop. It breaks down tasks into smaller sub-tasks and processes them in parallel across the cluster.

3. YARN (Yet Another Resource Negotiator): YARN is the resource management layer of Hadoop. It manages and allocates resources to applications running on the cluster, making it more versatile than the original MapReduce-only model.

4. HBase: HBase is a NoSQL database that runs on top of Hadoop. It is designed for real-time, random read/write access to large datasets and is often used for applications that require low-latency data access.

5. Hive: Hive is a data warehousing and SQL-like query language for Hadoop. It provides an interface for querying and analyzing data stored in HDFS using HiveQL, which is similar to SQL.

6. Pig: Pig is a high-level scripting language used for data analysis and transformation in Hadoop. It simplifies complex data processing tasks.

7. Spark: While not part of the core Hadoop ecosystem, Apache Spark is often used alongside Hadoop for data processing. It offers in-memory processing and supports various programming languages, making it faster and more flexible than MapReduce.

8. Impala: Impala is an open-source SQL query engine for Hadoop. It allows users to run interactive SQL queries on data stored in HDFS, providing real-time analytics capabilities.

9. Kafka: Kafka is a distributed messaging system often used in conjunction with Hadoop for real-time data streaming and processing.

10. ZooKeeper: ZooKeeper is a distributed coordination service used for managing configuration information, synchronization, and distributed systems in Hadoop clusters.