What is Data Modeling in Qlik?
The Qlik product family offers two methods to model and transform data: (a) using Qlik Compose, a data warehouse automation tool, and (b) using the Data Manager in Qlik Sense. Qlik Compose is a more formal tool for creating and managing data warehouse schemas. The Data Manager in Qlik Sense is an inline interface to load and model data for use within a Qlik app.
Qlik Compose is a tool for data warehouse automation that automates and manages the entire data warehouse lifecycle. With Qlik Compose, data engineers can create new data models, add new sources, and provision new data marts. The tool integrates with the Qlik Data Integration to extract and load the data in the data warehouse and with Qlik Catalog to catalog and govern the data models it creates and manages.
With Qlik Compose, there are three places where data is managed within the data warehouse: a landing area, warehouse, and data marts. With Qlik Compose, you perform the following steps to create and model your data warehouse and data marts:
The user experience for Qlik Compose is very much like a formal data modeling tool that adheres to data vault and star-schema methodologies. Data models for your warehouse must be a data vault, and those for your data marts must be star-schemas (facts and dimensions).
Qlik Compose lets you create all your data models graphically without writing code. If you need to customize fields or enrich the data with additional fields, you do so via a formula builder, which translates into SQL expressions. You can also add data quality rules for cleansing, validation, filters, etc.
Qlik Compose also automates the end-to-end process of data warehouse modeling, creation, and management.
Qlik Sense contains a tool called Data Manager which allows a user (analyst) to define what data to use within a Qlik app, then model and transform it to get the data in the proper use for the app. Within Data Manager and a Qlik app, you form data tables representing the data you want to use.
With Data Manager, you can:
Data Manager generates data loading scripts that are run within the Qlik app. You can also define data transformations by creating or editing data loading scripts. You perform the scripting in the Data Load editor. To perform more than simple data transformations such as JOINs or calculated columns, you will need to use the Data Load editor and write scripts.
The Data Load editor is an inline tool that looks like an IDE with an editor to define your data loading and transformation scripts. Within these scripts, you can do more sophisticated transformations such as dropping or adding new calculated fields, translating coded fields, joining tables, aggregating values, pivoting, and data validation. But the important thing to remember is: to perform more than simple data transformations, you must write scripts.
The Qlik tools for data modeling and transformation are designed for their specific purposes:
The best approach to data modeling and transformation for your Qlik apps is to use a third-party, no-code, in-cloud data warehouse data transformation tool.
Whether for Qlik apps or other analytics applications, there are certain best practices data and analytics teams should adhere to for data modeling and transformation. To learn more about these best practices, read our definitive guides:
Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics. With Datameer, you can:
Datameer provides a number of key benefits for your modern data stack and Qlik apps, including:
In addition, Datameer in-Snowflake data models and transformations would be available to your entire suite of BI apps and tools, not just Qlik apps. Most organizations have multiple BI tools.
The Qlik tools for data modeling and transformation – Qlik Compose and Qlik Data Manager/Load Editor – are purpose-built for highly specific tasks (data warehouse automation and data loading). These tools are not general-purpose no-code data modeling and transformation tools that can meet the needs of your entire set of personas (data engineers, data analysts, and data scientists) and allow them to collaborate on projects.
Datameer’s explicit focus on in-Snowflake data transformation makes it much more applicable across multiple analytics projects and tools. It offers a much more inclusive and easier user experience that supports multiple personas, collaboration among team members, a much deeper set of searchable, catalog-like data documentation, and transforms directly in Snowflake, using its powerful engine and keeping data and models secure.
Are you interested in seeing Datameer in action? Contact our team to request a personalized product demonstration.