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Holistics Documentation

Welcome to the Holistics Documentation page. You'll find comprehensive guides and documentation to help you start working with Holistics as quickly as possible, as well as support if you get stuck. Let's jump right in!

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Data Modeling

Data modeling refers to the process of applying business logic to make sense of physical data. This process aims to:

  • Consolidate data from multiple data sources
  • Transform data, produce business-friendly data units, in other words, easier to query and to interpret
  • Give business meaning to raw data generated from your system with metadata
  • Promote data self-service and collaboration

Holistics's Data Modeling Layer

Holistics's propriety Data Modeling layer is an extension of this concept. It is a part of the semantic layer that sits on top of your physical database/data warehouse. It acts as the single source of truth containing all data definitions, including metrics, dimensions, metadata... of your organization.

Holistics's Data Modeling can seamlessly connect to your data warehouse, map your data with your business logic, expose enriched datasets to relevant end-users for self-exploration or push reports directly to various channels:

Some of the benefits that you can reap using the Data Modeling layer:

  • It helps Analysts prepare reusable data models and datasets for exploration and access control.
  • It makes the works of different Analysts in the team visible, which encourages collaboration and analytics quality assurance.
  • It helps Business users perform self-service analytics with minimal help from data team.
  • It decouples business logic and physical data, which enables everyone in your organization to understand internal business & data logic just by looking at the modeling layer.

Key Concepts

  • Data Model is the core component of Holistics's approach. It is the "building block" for your Reports, Dashboards or Dataset.
  • Data Models contain Fields & Measures.
  • Data Models are linked together by Relationships. Models that have relationships with one another can form a Dataset.
  • Data Models also contains other Metadata like:
    • Model & Field descriptions, to give more context to your data
    • Data unit test for the models, or model fields
    • Dependency between models (specified by the Data Modeling Syntax)

The Holistics Workflow

How the workflows look like with Holistics:

Updated 7 months ago

What's Next

Next, let's get familiar with Holistics's core features.

Data Model
Data Modeling Syntax

Data Modeling

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