New Features in Apache Spark 2.3: Continuous Streaming, Kubernetes Scheduler, Pandas UDFs, and MLlib Enhancements

Apache Spark 2.3 introduces major upgrades such as millisecond‑latency continuous streaming, stream‑to‑stream joins, a native Kubernetes scheduler backend, accelerated Pandas UDFs, and several MLlib improvements, all aimed at making big‑data processing faster, easier, and smarter.

Apache SparkBig DataContinuous Processing

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New Features in Apache Spark 2.3: Continuous Streaming, Kubernetes Scheduler, Pandas UDFs, and MLlib Enhancements

dbaplus Community

Nov 27, 2015 · Big Data

Why Spark Is the Next Big Thing in Big Data: Core Concepts Explained

This article provides a comprehensive overview of Apache Spark, covering its origins, core concepts such as RDDs, transformations, actions, dependencies, execution modes, and key components like Spark SQL, Streaming, MLlib, and GraphX, while also offering practical code examples and visual illustrations.

DataFramesGraphXMLlib

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Why Spark Is the Next Big Thing in Big Data: Core Concepts Explained

Qunar Tech Salon

Aug 18, 2015 · Big Data

Overview of Spark Big Data Analytics Framework Components

Spark’s big‑data analytics ecosystem comprises core components such as the in‑memory RDD data structure, Streaming for real‑time processing, GraphX for graph analytics, MLlib for machine‑learning, Spark SQL for querying, the Tachyon file system, and SparkR, each enabling scalable, distributed computation.

Big DataGraphXMLlib

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Overview of Spark Big Data Analytics Framework Components