IRJET - Survey Paper on Map Reduce Processing using HADOOP
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This document summarizes a survey paper on MapReduce processing using Hadoop. It discusses how big data is growing rapidly due to factors like the internet and social media. Traditional databases cannot handle big data. Hadoop uses MapReduce and HDFS to store and process extremely large datasets across commodity servers in a distributed manner. HDFS stores data in a distributed file system, while MapReduce allows parallel processing of that data. The paper describes the MapReduce process and its core functions like map, shuffle, reduce. It explains how Hadoop provides advantages like scalability, cost effectiveness, flexibility and parallel processing for big data.
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Big data Analytics HadoopMishika Bharadwaj big data analytics using hadoop and basics of hadoop architecture mapreduce hive pig jaql and a mini project based on hadoop
Big Data-Survey
Big Data-Surveyijeei-iaes Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
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11. From Hadoop to Spark 1:2
11. From Hadoop to Spark 1:2Fabio Fumarola In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop.
Finally we introduce spark using a docker image and we show how to use anonymous function in spark.
The topics of the next slides will be
- Spark Shell (Scala, Python)
- Shark Shell
- Data Frames
- Spark Streaming
- Code Examples: Data Processing and Machine Learning
Apache Hadoop and Spark: Introduction and Use Cases for Data Analysis
Apache Hadoop and Spark: Introduction and Use Cases for Data AnalysisTrieu Nguyen This document provides an introduction to Apache Hadoop and Spark for data analysis. It discusses the growth of big data from sources like the internet, science, and IoT. Hadoop is introduced as providing scalability on commodity hardware to handle large, diverse data types with fault tolerance. Key Hadoop components are HDFS for storage, MapReduce for processing, and HBase for non-relational databases. Spark is presented as improving on MapReduce by using in-memory computing for iterative jobs like machine learning. Real-world use cases of Spark at companies like Uber, Pinterest, and Netflix are briefly described.
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