Purpose and Integration with Our SaaS Application
Welcome to the comprehensive guide for our data warehouse, a crucial component of our SaaS application. This data warehouse is designed to provide robust, scalable, and reliable data storage and retrieval capabilities. These features are essential for enhancing the analytical and decision-making processes within our organization.
Data Warehouse Characteristics
Hourly Data Loading: To ensure that our users have access to the most up-to-date information, our data warehouse is updated hourly. This frequent update cycle guarantees that the data is not only current but also reflects the latest trends and changes in user activities and system interactions.
Schema Design Mirroring 'Fat Finger Workflows': The schema of our data warehouse is intricately designed to align with the 'Fat Finger Workflows.' This unique design approach means that the structure of the data within our warehouse closely resembles the format of our Excel exports. Such a familiar structure is aimed at minimizing the learning curve for users who are already accustomed to the layout and format of our Excel-based reports.
Seamless Integration: The data warehouse is seamlessly integrated with our SaaS application, providing a cohesive and unified user experience. This integration allows for streamlined processes, where data flows effortlessly between our application and the warehouse.
Enhanced Data Analysis: With the data warehouse in place, users can delve into more complex and comprehensive data analysis. The hourly updates and the familiar schema enable users to conduct real-time analysis with confidence and precision.
User-Centric Design: The architecture of our data warehouse is crafted with the end-user in mind. By mirroring the Excel export format, users who are familiar with our existing systems will find the transition to using the data warehouse intuitive and straightforward.
Getting Access to FAT FINGER Data Warehouse
Please contact support via intercom chat below to request credentials. Credentials and URL Endpoint will be sent to you directly once approved.
Data Dictionary or Catalog
Understanding the Structure
In our data warehouse, the structure and organization of data are directly influenced by the workflows created in Fat Finger. This section will guide you through understanding how these workflows are represented in our Snowflake data warehouse, ensuring you can effectively navigate and utilize the data.
Workflow to Table Representation
Workflow and Section Representation: Each workflow in Fat Finger is represented as a separate table in Snowflake. The naming convention of these tables is a combination of the workflow name and the section name, ensuring a direct correlation between the workflow structure in Fat Finger and its representation in the data warehouse.
Example: If you have a workflow named "InventoryCheck" in Fat Finger with a section named "ProductDetails," the corresponding table in Snowflake would be named "InventoryCheck_ProductDetails."
Sections and Layout: Sections in a workflow, whether vertical or horizontal, define the structure of the corresponding table in Snowflake. This mirroring of the workflow structure into table format makes it intuitive for users to relate their workflow design with the data stored in the warehouse.
Field Mapping: Each field within a section of a Fat Finger workflow is represented as a column within the corresponding Snowflake table. This direct mapping ensures that all data captured in a workflow is accurately and comprehensively represented in the data warehouse.
Example: A field named "ProductID" in the "ProductDetails" section of the "InventoryCheck" workflow will be a column named "ProductID" in the "InventoryCheck_ProductDetails" table in Snowflake.
Excel Export Similarity
Intuitive Layout: Users familiar with the Excel exports of Fat Finger workflows will find a similar pattern in the data warehouse. The way tables and fields are structured in the data warehouse closely follows the layout and organization of data in the Excel exports, providing a familiar and user-friendly environment for data interaction.
Benefits of This Structure
Readability: This approach provides a highly readable and intuitive way of working with your data. Users can easily draw parallels between their workflow designs and the data representation in Snowflake.
Consistency: Maintaining consistent naming conventions and structures across the platform and data warehouse reduces the learning curve and enhances data accessibility.
Efficiency: The direct mapping of workflows to tables and fields to columns streamlines data analysis, making it easier for users to locate and interpret the data they need.
As we continue to explore the capabilities and features of our data warehouse in the following sections, keep in mind that this tool is more than just a storage solution; it's a pivotal part of our ecosystem that empowers users with timely, accurate, and actionable data insights.