Apache Spark beyond Hadoop MapReduce
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This presentation discusses Apache Spark and how it overcomes limitations of MapReduce. It begins by describing strengths of MapReduce like scalability and fault tolerance. It then covers limitations of MapReduce like lack of support for real-time and iterative processing. The presentation explains how Spark addresses these limitations by keeping data in-memory and supporting features like machine learning libraries. It highlights other Spark features such as DataFrames and SparkSQL and concludes by advertising an Apache Spark training course from Edureka.
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Comprendre la technologie Blockchain, son fonctionnement, ses contraintes et limites
Où en est-on ? Quelle est la maturité des technologies ?
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Aux directions Digital & Systèmes d’Information pour soulever le capot et voir ce qu’il y a derrière le «buzz word»
Aux directions des Opérations qui sont intéressées par une réduction de leurs coûts d’opération et de leurs infrastructures de transaction
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http://www.indicthreads.com
http://pune15.indicthreads.com
Spark
SparkHeena Madan Spark is an open-source distributed computing framework used for processing large datasets. It allows for in-memory cluster computing, which enhances processing speed. Spark core components include Resilient Distributed Datasets (RDDs) and a directed acyclic graph (DAG) that represents the lineage of transformations and actions on RDDs. Spark Streaming is an extension that allows for processing of live data streams with low latency.
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Apache spark with java 8Janu Jahnavi https://www.learntek.org/apache-spark-with-java/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
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- 4. Slide 4Slide 4Slide 4 www.edureka.co/apache-spark-scala-training Simple Scalable Fault Tolerant Minimal data motion Strength of MapReduce Independent of a programming language, such as Java, C++ or Python. It can process petabytes of data, stored in HDFS on one cluster MapReduce takes care of failures using the replicated copies. Process moves towards data to minimize Disk I/O
- 6. Slide 6Slide 6Slide 6 www.edureka.co/apache-spark-scala-training Real Time Complex Algorithm Re-reading and parsing Data Minimal Data Motion Graph Processing Iterative Tasks Random Access Limitations Of MR
- 7. Slide 7Slide 7Slide 7 www.edureka.co/apache-spark-scala-training Feature Comparison with Spark Fast 100x faster than MapReduce Batch Processing Batch and Real-time Processing Stores Data on Disk Stores Data in Memory Written in Java Written in Scala Hadoop MapReduce Hadoop Spark Source: Databrix
- 8. What are the MR limitations and how Spark overcomes it?
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- 15. Slide 15Slide 15Slide 15 www.edureka.co/apache-spark-scala-training Spark Features makes its Architecture better than MR
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