The industry-leading BI (business intelligence) tools, such as Tableau, predate NoSQL databases. We’ll focus on those that integrate well with MongoDB, the most popular NoSQL database according to DB-Engines. In this article, we’ll learn what are the leading tools for visualizing NoSQL data. The rise of modern non-relational databases led to the development of new data visualization tools that allow you to pull data from various sources - both relation and non-relational. Historically, business intelligence tools were created to work with tabular data using relational databases as a data source. These visuals help you discover hidden patterns and derive business insights. NoSQL databases also have scale-out architecture which allows the load to be redistributed by adding new nodes to the system.ĭata visualization tools translate large amounts of data into visuals, such as charts, graphs, and infographics. They provide flexible schemas allowing the storage of semi-structured and unstructured data. NoSQL databases, such as MongoDB, were developed. This variability of the data required modern database solutions.

The data gathered by applications for big data analysis and business intelligence analysis could be structured, unstructured, or a combination of the two. The traditional and tedious data models focused on reducing data duplication and therefore, storage costs, were no longer relevant. The rapid growth of data has been counteracted by the decreased cost of storing it. In the past few decades, the amount of data produced by users and processed by applications has increased tremendously.

But where did NoSQL, or non-relational, databases come from?

Throughout the article, we’ll use the terms NoSQL and non-relational interchangeably. NoSQL (aka “not only SQL”) is an umbrella term describing databases that store data in a format different from relational tables.

Sql tools for data analysis