Spark is an open-source, distributed computing system designed for big data processing and analytics. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Spark supports various high-level tools, including SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data processing.
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About Spark
Spark was created in 2009 by researchers at UC Berkeley's AMPLab to address limitations in the MapReduce system, particularly its inefficiency with iterative algorithms and interactive data analysis. The project was open-sourced in 2010 and later donated to the Apache Software Foundation in 2013, where it became an Apache Top-Level Project.
Spark's strengths include its speed, ease of use, versatility, and advanced analytics capabilities. Weaknesses involve high memory consumption and complexity in managing cluster resources. Competitors include Apache Hadoop, Apache Flink, and Dask.
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How to hire a Spark expert
A Spark expert must have strong skills in Scala or Python programming, proficiency in distributed computing concepts, experience with Hadoop and HDFS, knowledge of SQL for querying data, familiarity with Spark's core components like RDDs, DataFrames, and Datasets, and expertise in using Spark's libraries such as MLlib for machine learning and Spark Streaming for real-time data processing.
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