Description:
Hadoop is an open-source framework that allows you to store and process large datasets in a distributed computing environment. It is designed to scale from a single server to thousands of machines, each offering local computation and storage. It enables the processing of vast amounts of data quickly and efficiently, which is essential in the era of big data.
Hadoop Works
Data Storage: Data is first loaded into HDFS. The data is divided into blocks and distributed across the cluster.
Job Submission: The user submits a job (a MapReduce program) to the ResourceManager (via the JobTracker in older versions).
Job Scheduling: YARN schedules and allocates resources for the job based on available resources in the cluster.
Map Phase: The data is processed by mappers, where each piece of data is read, processed, and transformed into key-value pairs.
Shuffle and Sort: The output from the mappers is shuffled and sorted to bring together similar data (based on keys).
Reduce Phase: The reducers process the sorted data to generate the final output.
Data Output: The results of the job are written back to HDFS.
Hadoop Ecosystem
Hive: A data warehousing system that allows users to query data using SQL-like syntax.
HBase: A distributed NoSQL database that is integrated with HDFS for real-time data access.
Pig: A high-level platform for processing data using a scripting language that abstracts the MapReduce complexity.
Sqoop: A tool for transferring data between Hadoop and relational databases.
Flume: A service for collecting and aggregating log data from various sources.
Oozie: A workflow scheduler system for Hadoop that manages the execution of jobs.
Zookeeper: A coordination service that helps maintain consistency across distributed systems.
Use Cases of Hadoop
Data Warehousing: Storing large amounts of structured and unstructured data.
Log Processing: Collecting and analyzing logs from various systems in real time.
Data Mining: Mining large datasets for insights using algorithms and machine learning.
Search Engines: Indexing vast amounts of web data and enabling fast searches.
Machine Learning: Processing large datasets for training machine learning models.
Social Media Analysis: Analyzing user-generated content and interactions at scale.
Features of Hadoop
Data Replication: HDFS replicates data blocks (typically 3 replicas by default) to ensure fault tolerance and high availability. If one node fails, the data is still available from other replicas.
Fault Tolerance: Even when nodes or disks fail, HDFS can continue functioning without loss of data.
Cost-Effectiveness: Hadoop can run on commodity hardware (inexpensive, non-proprietary machines), which reduces the cost of storage and computation compared to traditional data processing systems.
Data Locality: Hadoop processes data where it is stored, a concept known as data locality.
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