This article focuses on introducing the basics of data sources in Lambda Analytics.
Data Sources are the components that allow you to select data from your database to build Ad Hoc views, reports, and dashboards. Data sources are the fundamental first step in the Lambda Analytics Information Hierarchy.
Databases can contain an overwhelmingly large amount of data, which data sources can help to filter. Since data sources are a read replica of your database, you can manipulate data and delete unnecessary files without tampering with the contents of your database.
Through using a data source, users can see columns that have been joined, filtered, and labelled for their specific business needs. Security files can then further limit the data accessible to users building an Ad Hoc view with a particular data source.
Lambda Analytics comes with over twenty pre-configured data sources that you can use as the basis of your Ad Hoc views. These data sources can cover a variety of different topics, ranging from a general overview of your eLearning system to individual course activities.
If you find that your reporting needs require a custom database, contact our Support team for more information.
Elements of a Data Source
A data source is saved as an object in the Repository. Like other Repository objects, it has a name and an optional description. All Out-of-the-Box data sources will be saved under Public Artifacts, whereas any duplicate or customized data sources will be saved under Private Artifacts.
A data source is composed of:
Data Source Design: This specifies the available tables and columns, queries for derived tables, joins between tables, calculated fields, and labels to display the columns. This is created in the Data Source Designer.
Security File: Security files define the row and column-level access privileges based on users and roles. These files are external, and are uploaded to Lambda Analytics.
Data Source Designer
The Data Source Designer allows you to define all of the components of a particular data source. You can right-click an existing data source in the Repository and click Edit to launch the Data Source Designer.
Along the top of the Data Source Designer are tabs for configuring various aspects of the data source.
Data Management: This allows you to determine which database tables you would like to include in the data source.
Joins: This allows you to define inner and outer joins between all tables and derived tables.
Pre-Filters: This allows you to specify conditions on field values to limit the data accessed through the data source.
Data Presentation: This allows you to organize the visual aspects of a data source and change the display properties of tables, columns, sets, and items exposed to data source users.
Options: This allows you to include any security files or locale bundles into your data source.
The Data Source Designer opens automatically to the Data Management tab. From there, you can navigate to a different tab. Before you can save your data source, you must choose which sets to make visible.
Once you are complete, click OK. This will validate your changes, and save it in your specified location.
When working with databases, you may need to use schemas. A schema is a logical model that determines how data is stored, and is represented in XML design files. For example, the schema in a relational database is a description of the relationships between tables, views, and indexes.
If your database supports schemas, the Select Database Schemas dialog box will appear when you click Create with Data Source Designer.
If you are using a data source that supports database schemas (e.g. Oracle RDBMS), you will need to specify the schema to use in your data source. You will also need to select schemas when you are using a virtual data source.
To help build your data sources, you can use our visual and interactive representation of the Moodle and Totara database schemas. These will give you a graphical means of exploring data structures, and help you better understand which tables to select and how to join them.
Listed here is an ever-growing collection of case studies focused on making use of the many features available in data sources. Each case study has a specified outcome, a set of steps to follow, and an example created in full detail from start to finish.