How Holistics fits in your data stack

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If you're new to the data space and interested in learning how to build a modern data stack, check out our Analytics Setup Guidebook

Building a modern data stack usually require stitching multiple data components together. This page explains where Holistics fits in the overall value chain.

Components of a modern data stack

A modern data stack is built on the foundation of four main components:

  1. Extraction & Loading (EL): Extracting data from source systems and replicating it to a central repository, usually a data warehouse.
  2. Data Warehouse (DW): The central analytics database where all the data now gets stored.
  3. Transformation (T): Where the raw data is turned into clean data and eventually insights.
  4. Visualization (BI): Where transformed data is turned into charts and dashboards to enable business decision-making.

Modern Data Stack

How Holistics fits in

Being a BI tool, Holistics handles (4) in the above diagram.

Holistics also provides certain capabilities of (3) (Transformation) in case you don't use a dedicated Transformation tool (like dbt or Dataform). Holistics also support strong integration with these tools.

In short, your data stack will contain:

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

How Holistics fits in your Data Stack