Querying Climate Datasets

The true power of Eratos is the ability to access and query petabytes of data in your local environment without the need to store and manage it locally.

Data can be queried in a number of ways, our guides walk you through how to query data available on the Eratos Community or private Spaces you have been granted access to. The three specific query examples that can be viewed in detail within our general onboarding documentation are:

The variety and veracity of data and the cleaning that it requires before use is a pain point for data professionals globally. Each new data source can mean spending hours if not days wrangling it to be fit for purpose. This is especially true for gridded data, coming in a large variety of shapes, sizes, and types. Eratos alleviates that pain point for your data specialist by providing easy access to complete datasets that maintain data output consistency. Once a gridded dataset has been ingested into Eratos, you can extract the underlying variable through the powerful query functions housed in the Eratos SDK.

Interacting with data through Eratos typically happens in three steps: Input, Operator, and Output. The Input is a dataset. The Operator is a specific function you want to perform on your chosen dataset, there are multiple different functions that can act as your operator however for this guide we'll only be focusing on Query Functions. The Output is your end result, in the case of a query function, it would be the answer to your query.

Due to the output consistency of Eratos, datasets can be substituted in and out at will, enabling rapid innovation in some of the world's most complex domains. This can save hours of time-bending one dataset to fit the needs of a developed operator. Modularity is core to Eratos both conceptually and technically which is key to making complex data solutions understandable, available, and scalable.

Above and below illustrate the output consistency of Eratos where the same Operator, in this case a Query Function, can be applied simply by substituting the Input dataset therefore achieving a different Output.

Let's get started with some practical applications of querying data with Eratos.