*Result*: A Systematic Review of Automated Classification for Simple and Complex Query SQL on NoSQL Database.
*Further Information*
*A data lake (DL), abbreviated as DL, denotes a vast reservoir or repository of data. It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured, structured, and unstructured information. These systems use a flat architecture and run different types of data analytics. NoSQL databases are nontabular and store data in a different manner than the relational table. NoSQL databases come in various forms, including key-value pairs, documents, wide columns, and graphs, each based on its data model. They offer simpler scalability and generally outperform traditional relational databases. While NoSQL databases can store diverse data types, they lack full support for atomicity, consistency, isolation, and durability features found in relational databases. Consequently, employing machine learning approaches becomes necessary to categorize complex structured query language (SQL) queries. Results indicate that the most frequently used automatic classification technique in processing SQL queries on NoSQL databases is machine learning-based classification. Overall, this study provides an overview of the automatic classification techniques used in processing SQL queries on NoSQL databases. Understanding these techniques can aid in the development of effective and efficient NoSQL database applications. [ABSTRACT FROM AUTHOR]
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