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Distributed computing frameworks for processing massive datasets at scale.
Big Data tools are software libraries and frameworks designed to handle, process, analyze, and derive insights from exceptionally large datasets that are too complex for traditional data processing tools. These tools are used for distributed computing, where data is processed in parallel across clusters of computers, enabling efficient analysis of vast amounts of data. They support a range of tasks from batch processing to real-time data streaming and are pivotal in industries like finance, healthcare, marketing, and technology for tasks like predictive modeling, data mining, and machine learning on large-scale datasets.
Distributed Storage and Processing Framework
Framework that allows for distributed processing of large datasets across clusters of computers using simple programming models. Designed to scale from single servers to thousands of machines, each offering local computation and storage. Uses HDFS for distributed storage and MapReduce for processing.
Unified Batch and Stream Processing
Advanced unified programming model for defining and executing data processing workflows that can run on any execution engine. Provides portability across multiple execution environments including Apache Flink, Apache Spark, and Google Cloud Dataflow. Ideal for building flexible, scalable data pipelines.