Definitions
Caching for SQL-on-Hadoop are techniques and systems which store data to provide faster access to that data, for Structured Query Language (SQL) engines running on the Apache Hadoop ecosystem.
Overview
The Apache Hadoop software project (Apache Hadoop 2018) has grown in popularity for distributed computing and big data. The Hadoop stack is widely used for storing large amounts of data, and for large-scale, distributed, and fault-tolerant data processing of that data. The Hadoop ecosystem has been important for organizations to extract actionable insight from the large volumes of collected data, which is difficult or infeasible for traditional data processing methods.
The main storage system for Hadoop is the Hadoop Distributed File System (HDFS). It is a distributed storage system which provides fault-tolerant and scalable storage. The main data processing framework for Hadoop is MapReduce, which is based on the Google MapReduce project (Dean and Ghemawat 2008). MapReduce...
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References
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Pang, G., Li, H. (2019). Caching for SQL-on-Hadoop. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_249
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DOI: https://doi.org/10.1007/978-3-319-77525-8_249
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