How Holistics works

This page gives a quick high-level understanding of how Holistics works, what are different components and how they interact with each other.

How Holistics works

Here's a quick concept on how Holistics works:

  1. Holistics connect directly to your SQL database
  2. Data Teams build and maintain data models and datasets. These are reusable data components with predefined definitions of business logic.
  3. Non-technical Teams can build reports off datasets without the need to write code.
  4. Dashboards are shared with other users internally and externally.

Holistics Workflow

Key Concepts

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.

Dataset - A selected collection of data models and their relationships. Dataset is needed to explore data, and creating charts/dashboards.

Data Source - a data source is a connection to your SQL database. This is the first thing you need to set up in Holistics.

Chart - A visualization that's displayed to the end user. Chart is created by exploring a dataset.

Dashboard - A collection of charts put together. Dashboard can be shared to business users.

Dashboard Filter - A component to add to dashboard to let viewers control/restrict the data being displayed based on certain conditions.

User Types

There are 4 user roles in Holistics:

  • Admin: Has all privileges of other roles, plus the ability to manage users, data sources, billing,... and impersonate other users.
  • Analyst: Can create & edit reports, dashboards, data models and datasets on the data sources shared with them. Analysts cannot manage users, or connect new data sources.
  • Explorer: Can explore datasets & reports, and can save their exploration to their private workspace, but not the public workspace.
  • Viewer (Business User): Can only view reports and dashboards that are shared with them.


  1. Connect: You connect Holistics to an existing SQL data warehouse.
  2. Model & Transform: You use Holistics Data Modeling to model and transform analytics data.
  3. Dataset Building: You build datasets (a combination of data models)
  4. Self-service Analytics: Non-technical users can self-service explore data based on datasets prepared by data teams, or build interactive reporting dashboards.
  5. Sharing: Dashboards can be shared with others, or pushed to other platforms (email, Slack, webhooks, etc)

How Holistics Fits In Your Data Stack

A modern data stack will follow these steps:

  1. Data Loading: Loading data from various sources into a central repository, usually a data-warehouse.
  2. Data Warehouse: a powerful analytics database that hosts all your data.
  3. Data Modeling & Transformation: Raw data is being transformed and modelled inside your data-warehouse to be ready for consumptions.
  4. BI/Visualization: Transformed data is visualized and presented to business decision-makers.

Holistics handles (3) and (4) in the list above. Namely the transformation, modeling and BI layer.

In short, your data stack will contain:

  1. A SQL database - This can be your existing production database (not recommended), or a common data-warehouse (Snowflake, BigQuery, Redshift, etc).
  2. Holistics - the modeling & BI layer
  3. An ETL/EL tool - Optional, needed when you have data from multiple application sources and need to move into your data-warehouse.

Holistics Workflow