China
Africa
Explore chapters and articles related to this topic
The Role of NonSQL Databases in Big Data
Published in Kuan-Ching Li, Beniamino DiMartino, Laurence T. Yang, Qingchen Zhang, Smart Data, 2019
Although the NoSQL databases share the general characteristics mentioned, there are other characteristics that allow to differentiate them and use them to make a classification. Although there is no single classification, however, it is possible to differentiate them according to the underlying data model. So we can talk about the following types of families [19]: Key-value data bases: Riak, Redis, Dynamo, Voldemort.Document-oriented databases: MongoDB, CouchDB.Database based on columns: Cassandra, Hypertable, HBase, SimpleDB.Graph databases: Neo4J, Infinite Graph.
Databases
Published in Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, Big Data and Social Science, 2020
Dozens of different NoSQL DBMSs exist, with widely varying characteristics as summarized in Table 4.2. The simplest are key–value stores, such as Redis, Amazon Dynamo, Apache Cassandra, and Project Voldemort. We can think of a key–value store as a relational database with a single table that has only two columns, key and value, and that supports only two operations: store (or update) a key–value pair and retrieve the value for a given key.
Big Data and Social Science: Data Science Methods and Tools for Research and Practice
Published in Technometrics, 2021
Chapter 4 of “Databases,” by I. Foster and P. Heus, shows different approaches to storing data in ways that facilitate rapid, scalable, and reliable exploration and analysis, convenient for using in any software, particularly, in SAS, Stata, SPSS, or R. It describes relational DBMSs and Structured Query Language (SQL), optimizing databases and cleaning data, and embedding queries in Python. For extremely large databases, the alternative technologies have been developed of no SQL, or not only SQL, which are commonly referred as NoSQL approaches. For example, there are such NoSQL DBMSs of simple key-value structure as Redis, Amazon Dynamo, Apache Cassandra, and Project Voldemort. The spatial databases with socioeconomic data associated with jobs in cities and states are also discussed.