Compared with "classical" web, multi-tier applications, mobile applications have common and specific requirements concerning data persistence and processing. In mobile apps, database features can be distinctly analyzed for the client (minimalistic, isolated, memory-only) and the server (data rich, centralized, distributed, synchronized and disk-based) layers. Currently, a few lite relational database products reign the persistence for client platforms of mobile applications. There are two main objectives of this paper. First is to investigate storage options for major mobile platforms. Second is to point out some major differences between SQL and NoSQL datastores in terms of deployment, data model, schema design, data definition and manipulation. As NoSQL movement lacks standardization, from NoSQL products family MongoDB was chosen as reference, due to its strengths and popularity among developers. PostgreSQL serves the position of SQL DBMSs representative due to its popularity and conformity with SQL standards.
Big Data systems manage and process huge volumes of data constantly generated by various technologies in a myriad of formats. Big Data advocates (and preachers) have claimed that, relative to classical, relational/SQL Data Base Management Systems, Big Data technologies such as NoSQL, Hadoop and in-memory data stores perform better. This paper compares data processing performance of two systems belonging to SQL (PostgreSQL/Postgres XL) and Big Data (Hadoop/Hive) camps on a distributed five-node cluster deployed in cloud. Unlike benchmarks in use (YCSB, TPC), a series of R modules were devised for generating random non-aggregate queries on different subschema (with increasing data size) of TPC-H database. Overall performance of the two systems was compared. Subsequently a number of models were developed for relating performance on the system and also on various query parameters such as the number of attributes in SELECT and WHERE clause, number of joins, number of processing rows etc.
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