BackendlessDataCollection is a new class in the Backendless Swift SDK that conforms to the Collection protocol and enables you to retrieve and iterate over a collection of objects stored in a Backendless data table. This tutorial will show you how.

In this article, we’ll consider some practical examples of how to use the Backendless Data Collection library to its fullest extent. You can find code documentation and sources for the library here.

In another post, we describe how data tables in Backendless map to the client-side classes whose instances contain persisted data objects. However, there are scenarios when the default mapping is undesirable. In that case, Backendless client libraries provide an API to override the mapping.

In another article, we wrote how to load the first object from a data table using API. For the purpose of symmetry (and out of common sense), there is also an API to load the last object from a data table in your Backendless Database. The last object is determined by the time when it is…

Data objects in Backendless can be connected to related properties not only in other tables, but also with Geopoints. These relationships may be declared programmatically or using Backendless Console. In this post, we will review the process of declaring a Data-to-Geo relationship in a data table schema.

Backendless database provides a very simple, but powerful API for storing, searching, updating or deleting application objects. The “feature 1” post demonstrates how to save objects with relations in Backendless using the APIs.

As data objects are being saved or updated with the API requests some properties of the objects may not have a value assigned to them. It may be necessary that for those properties a default value is assigned. This is identical to how relational databases may have a default value for a column.

Backendless provides an easy-to-use API to introspect data tables. Given a table name, the API provides information about table columns, their names, data types, default values, etc. If a column represents a relationship, it is properly denoted as such in the provided information.

When saving or updating a data object in a persistent storage, it is often necessary to make sure a property follows a particular data format. For instance, properties like email address, a US phone number, a bank account number, a URL and many others may be validated with regex (a regular expression). Backendless makes it…