For many data analysts, performing ad-hoc data analysis is a common task that often leads to friction when interacting with data administrators. Data users struggle with data requests and cumbersome distribution methods, while administrators face the challenge of maintaining data integrity and efficiently distributing data.
But there’s a solution: Sigma Input Tables integrated with Databricks is the key to overcoming these challenges. Once set up, data users can conduct independent ad-hoc analysis on data from Databricks, while data administrators can efficiently control access and integrity.
That’s what we call a win-win. Let’s dive into how it works.
Linked Input Table Example: Harmonizing Existing and New Data
The Linked Input Table is a feature that bridges the gap between existing Databricks data and new, editable columns in Sigma.
Key Features:
- Dynamic Data Referencing: The Linked Input Table can seamlessly reference existing data from Databricks, fostering cohesion between different datasets.
- Editable Columns and Writeback to Databricks: Users can edit and manipulate the data in new columns defined within the Sigma UI, providing a layer of flexibility without compromising the integrity of the original data. New columns are persisted in a new table in Databricks.
Some Tips to Remember:
- This is perfect for scenario/what-if analyses, based on your existing data [you don’t have to create a whole new table].
- Live calculations can be performed on the input data.
In Sigma, we added a new column, ‘Average Price’ to our data that was sourced from Databricks, and a live ad-hoc calculation was performed that updated our bar chart
- Selective data availability: Data administrators can choose a subset of columns from the dataset for users to perform ad-hoc analysis on.
Once configured, only Date and Sales will be available to users for ad-hoc analysis
This example highlights the potential of combining the robust capabilities of Databricks with the user-friendly interface of Sigma. It harmonizes data collaboration, enabling users to augment existing datasets with additional insights, all within the Sigma environment.
Databricks Connection: Preserving Data Integrity
Input tables facilitate collaborative workflows between Databricks users and non-technical individuals, bridging the gap between technical and non-technical expertise. This enables bi-directional interactions, empowering non-technical users unfamiliar with programming languages such as Python or SQL to actively engage in data-centric processes alongside their technically adept counterparts.
One key feature we want to highlight is the preservation of data integrity ensured by Sigma and Databricks. While new columns can be edited within Sigma, the existing data remains uneditable.
By ensuring data integrity, Sigma and Databricks empower users to augment existing datasets with additional insights while maintaining the trust and consistency of the underlying data.
Integrating Sigma Input Tables with Databricks offers a solution to the traditional challenges data users and administrators face. It enables speculative analysis of data without manual or toilsome processes. Best of all, it allows data users and administrators to focus on the most rewarding aspects of their job—and reduces the friction between them.
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