Key Concepts
This page defines frequently used terms and concepts in Holistics. The diagram below illustrates the main concepts in Holistics.
Data Source
A data source is a connection to your SQL database. This is the first thing you need to set up in Holistics.
Data Modeling
The core of Holistics, an abstract/semantic layer where mapping between business logic and underlying data table sits. Data Teams will spend most of their time in this layer.
Data Model
An abstract concept that sits on top of a database table, where different business logic are added in the modeling layer. Part of the modeling layer. There are 3 types of data models: Table, Transform and Import.
Relationship
The linking between data models. Similar to joins/foreign-key relationship in database.
Dimension and Measure
Dimensions are non-aggregate model fields referencing the underlying table's columns, or created by using non-aggregate functions to transform one or multiple dimensions. On the other hand, Measures are aggregating fields created with aggregate functions (SUM, COUNT, etc).
As-code (Holistics 4.0)
In general, As-code or Infrastructure as Code paradigm refers to the process of managing and provisioning computational infrastructures through code instead of manual processes.
In Holistics's context, As-code refers to our modeling mechanism which allows users to model their business data with code beside the usual point-and-click experience.
AMQL
AMQL is Holistics's modeling and querying language. It consists of 2 inter-connected components:
- AML: A declarative language used to describe data semantic model and business metrics.
- AQL: A query language that leverages models defined in AML, to query SQL databases in a higher abstraction manner.
Git Integration (Holistics 4.0)
Your Holistics code base is now powered by Git, a version control system that allows users to track every change, create separate branches for development, and conduct code reviews.
Data Exploration
This concept provides a user-friendly interface for exploring and visualizing data through drag-and-drop interface or SQL Editors, enabling faster insights and analysis.
Dataset
A selected collection of data models and their relationships. Dataset is needed to explore data, and creating charts/dashboards.
Metric
Metric is a full-fledged aggregation query written in Holistics's Analytics Query Language (AQL) to calculate measurements that a business is interested in. Typically, Metrics are defined within Datasets.
In the Business Intelligence (BI) world, the words “metric” and “measure” are often used interchangeably. Some BI tools use the term “metric”, while others use “measure”. However, in general, these terms are intended to convey the same meaning. This makes sense, as the word “metric” is derived from the Greek word “metron”, which translates to “a means of measure”.
In Holistics, we have both “metric” and “measure”, where a “measure” is defined in a model, while a “metric” is defined at the dataset level.
Dashboard
A Dashboard is a collection of charts to visually present data to business users. User's immediate interactions with dashboards are streamlined with Dashboard Filters, while power users can still dive deeper using the Explore action at the individual charts.