The Landscape of Open Source Databases
Data management has been always at the heart of IT applications, as most of them base their operation on the processing of input data and the subsequent production of output data. For several years, transactional databases based on the relational model and the Structured Query Language (SQL) have dominated the data management landscape and helped supporting the most prominent enterprise applications like ERP, CRM, Business Intelligence (BI), knowledge management and reporting systems. During the past decade, this landscape has radically changed, due to the advent of Big Data applications and the introduction of new types of data management infrastructures such as Data Lakes and NoSQL databases. Nevertheless, both the old and the new data management landscapes share a common characteristic: The availability and popularity of open source databases.
Open source databases provide a compelling option for data management in the Big Data era: They come at an affordable cost and incorporate a wide range of novel data management capabilities that enable innovative applications. The landscape of open source databases provides a wide array of possible choices, which could make the selection of a proper open source database challenging. In this context, it’s always good to understand the main features of open source databases and when it’s a good idea to use each one of them. Thus a high-level classification of open source databases in SQL, noSQL and special-purpose databases is useful. Databases falling in each of these categories serve a different purpose at the first place.
Despite the emergence of Big Data that include various sources of unstructured and semi-structured data, a large number of companies are still in-need of handling conventional, structured, relational datasets. That’s for example the case for all traditional enterprise applications that need to offer structured reporting functionalities. Luckily, the open source community has made available several open source relational databases, which offer features comparable to the large-scale enterprise databases. Prominent mentions include:
In the Big Data era, noSQL databases have been gaining momentum, as they offer virtually infinite scalability. However, this scalability comes at the expense of sacrificing strict transaction guarantees and lack of support for structured schemas that facilitate reporting. Prominent example of open source noSQL databases include:
Beyond the SQL and noSQL distinction, there are also databases that are designed for specialized purposes and applications, including applications that did not exist few years before. For example:
There are tens of other open source databases that could be added on this list. However, the given list is overall representative of the open source choices that are offered to Big Data developers. The latter have to consider various trade-offs when selecting their databases. They might have to consider other criteria like the level of support for their preferred programming platform, the learning curve associated with each database, as well as the compatibility of the target database with the overall context of the project at hand. Nevertheless, the wealth of available choices is certainly a “pleasant headache” that empowers developers to ultimately identify what suits their needs best.
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