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Sensor data management in the cloud: Data storage, data ingestion, and data retrieval
Author(s) -
Sangat Prajwol,
IndrawanSantiago Maria,
Taniar David
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4354
Subject(s) - nosql , spark (programming language) , computer science , scalability , data management , cloud computing , database , big data , computer data storage , data processing , data retrieval , data science , data mining , operating system , programming language
Summary Sensors are widely used in the field of manufacturing, railways, aerospace, cars, medicines, robotics, and many other aspects of our everyday life. There is an increasing need to capture, store, and analyse the dynamic semi‐structured data from those sensors. A similar growth of semi‐structured data in the modern web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large‐scale data processing frameworks for parallel analysis. NoSQL data store such as MongoDB and data processing framework such as Apache Hadoop has been studied for scientific data analysis. However, there has been no study on MongoDB with Apache Spark, and there is a limited understanding of how sensor data management can benefit from these technologies, specifically for ingesting high‐velocity sensor data and parallel retrieval of high volume data. In this paper, we evaluate the performance of MongoDB sharding and no‐sharding databases with Apache Spark, to identify the right software environment for sensor data management.