Efficient Data Processing with Apache Spark
Apache Spark has revolutionized the landscape of big data processing by harnessing the power of distributed computing to handle massive datasets. However, as Spark applications increase in size and complexity, effective performance tuning becomes essential.
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MaRe: Processing Big Data with application containers on Apache Spark. [PDF]
Gigascience, 2020 Capuccini M, Dahlö M, Toor S, Spjuth O.
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pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP. [PDF]
BMC Bioinformatics, 2020 Chen W, Yao C, Guo Y, Wang Y, Xue Z.
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SparkRA: Enabling Big Data Scalability for the GATK RNA-seq Pipeline with Apache Spark. [PDF]
Genes (Basel), 2020 Al-Ars Z, Wang S, Mushtaq H.
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Enabling scalable single-cell transcriptomic analysis through distributed computing with Apache spark. [PDF]
Sci RepAdil A +4 more
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SparkDWM: a scalable design of a Data Washing Machine using Apache Spark. [PDF]
Front Big DataHagan NKA, Talburt JR.
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Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning. [PDF]
Sensors (Basel)Su N, Huang S, Su C.
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Data Streaming with Apache Spark
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTIn today’s fast-paced world, real-time data streaming is crucial for many businesses and industries. With tools like Apache Spark and Hadoop, organizations can process vast amounts of data as it’s created, gaining insights in seconds rather than hours.
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Using Apache Spark on genome assembly for scalable overlap-graph reduction. [PDF]
Hum Genomics, 2019 Paul AJ +5 more
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SparkGA2: Production-quality memory-efficient Apache Spark based genome analysis framework. [PDF]
PLoS One, 2019 Mushtaq H, Ahmed N, Al-Ars Z.
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