Sensor data management in the cloud: Data storage, data ingestion, and data retrieval

Prajwol Sangat, Maria Indrawan-Santiago, David Taniar

    Research output: Contribution to journalArticleResearchpeer-review

    14 Citations (Scopus)


    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.

    Original languageEnglish
    Article numbere4354
    Pages (from-to)1-10
    Number of pages10
    JournalConcurrency Computation: Practice and Experience
    Issue number1
    Publication statusPublished - 2018


    • Apache Spark
    • data ingestion
    • data retrieval
    • data storage
    • MongoDB
    • sensor data management

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