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:computer: Curated list of awesome resources and links about using NoSQL databases

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awesome awesome-list brewer-theorem databases graph-databases learning-nosql nosql nosql-databases nosql-guides relational-databases

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Hi there, I'm Eric! 👋

Instructions for living a life. Pay attention. Be astonished. Tell about it.

— Mary Oliver, American poet

🧑‍💻 Currently: Data Scientist (full-time), Engineer (at heart), and Educator (in practice)
🔭 I’m currently working on writing math articles for freeCodeCamp, and fun data side projects.
🌱 I’m currently learning about Emacs Lisp to build my own packages, marketing, Econometrics with R, Bayesian statistics, and Causal inference as personal/professional endeavors for the future.
📚 Currently reading: The Diamond Age: Or, a Young Lady's Illustrated Primer by Neal Stephenson.
👯 I’m looking to collaborate on open source tools that empower individuals to solve problems and learn.
💬 Ask me about open science, open-source culture, data science, healthcare reform, and education reform.
😄 Pronouns: he/him
⚡ Fun fact: I like writing with fountain pens on good stationery and notebooks.
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Last updated: 2024-01-30

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NoSQL systems have gained their popularity for many reasons, including the flexibility they provide in organizing data, as they relax the rigidity provided by the relational model and by the other structured models. This flexibility and the heterogeneity that has emerged in the area have led to a little use of traditional modeling techniques, as opposed to what has happened with databases for decades.

In this paper, we argue how traditional notions related to data modeling can be useful in this context as well. Specifically, we propose NoAM (NoSQL Abstract Model), a novel abstract data model for NoSQL databases, which exploits the commonalities of various NoSQL systems. We also propose a database design methodology for NoSQL systems based on NoAM, with initial activities that are independent of the specific target system. NoAM is used to specify a system-independent representation of the application data and, then, this intermediate representation can be implemented in target NoSQL databases, taking into account their specific features. Overall, the methodology aims at supporting scalability, performance, and consistency, as needed by next-generation web applications.

"Difference between scaling horizontally and vertically for databases"

https://stackoverflow.com/a/11715598/

"NoSQL Databases: Yearning for Disambiguation"

https://arxiv.org/abs/2003.04074

The demanding requirements of the new Big Data intensive era raised the need for flexible storage systems capable of handling huge volumes of unstructured data and of tackling the challenges that traditional databases were facing. NoSQL Databases, in their heterogeneity, are a powerful and diverse set of databases tailored to specific industrial and business needs. However, the lack of theoretical background creates a lack of consensus even among experts about many NoSQL concepts, leading to ambiguity and confusion. In this paper, we present a survey of NoSQL databases and their classification by data model type. We also conduct a benchmark in order to compare different NoSQL databases and distinguish their characteristics. Additionally, we present the major areas of ambiguity and confusion around NoSQL databases and their related concepts, and attempt to disambiguate them.

"NoSQL Databases"

https://doi.org/10.31219/osf.io/u5q3y

NoSQL databases are open-source, schema-less, horizontally scalable and high-performance databases. These characteristics make them very different from relational databases, the traditional choice for spatial data. The four types of data stores in NoSQL databases (key-value store, document store, column store, and graph store) contribute to significant flexibility for a range of applications. NoSQL databases are well suited to handle typical challenges of big data, including volume, variety, and velocity. For these reasons, they are increasingly adopted by private industries and used in research. They have gained tremendous popularity in the last decade due to their ability to manage unstructured data (e.g. social media data).