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Spark enables fast, large-scale data processing through in-memory computing. It supports various data sources and provides built-in features for machine learning and SQL.
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Apache Spark is a distributed open-source framework for processing large data sets through batch processing, streaming, SQL, and machine learning.
Spark grew from the need to organize recurring data processing across clusters with more reuse and less effort than isolated batch programs. The Apache project brings together the resulting APIs and libraries for different analytical tasks.
A Spark application describes processing as a directed plan: data is read, reshaped through transformations, and computed only when an action occurs. The scheduler divides the plan into tasks for cluster nodes; APIs such as SQL and DataFrame hide distribution, while caching can accelerate repeated access.
Transformations describe new data structures without executing the computation immediately.
Actions trigger the planned processing and return results or write data.
DataFrames give distributed data a tabular structure and enable optimization.
A scheduler distributes tasks to compute nodes and coordinates their results.
Spark is useful for large batch and streaming pipelines, SQL analytics, and distributed machine learning. Performance depends heavily on partitioning, shuffle, memory, and cluster operations; a familiar API does not replace cost and runtime analysis.
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