The End-to-End Cloud Data Warehouse Solution
Headquarters:Madison, WI, USA
Type of business:Private
Pandata Group has assisted many customers in their pursuit of digital transformation through effective data management. Our evolution towards pure cloud solutions such as Snowflake and Thoughtspot reflects the growth of the SaaS approach to data and analytics platforms. We have increased time-to-value by deploying zero-management solutions that support cloud DW and analytics (e.g. Matillion ETL). Our objective has been to deliver a modern end-to-end cloud data experience for scalability, ease-of-access and efficient cost management.
When it came time to choose a data modeling tool to architect cloud data warehouses for our customers, we were faced with a dilemma. Full-client tools that required complex licensing for multiple users and large deployment footprints seemed out of step with the “everything on a browser” approach that we had embraced. This is where SqlDBM came to the rescue. Since it is a cloud-hosted, SaaS solution, it fits perfectly with our “cloud data lifecycle” … from modeling to ETL to data management to analytics. No need to have anything more than a web browser!
Data Modeling Everywhere
One of our first “all cloud data” adopters is a regional health care and assisted-living provider. They were looking to rapidly deploy a data analytics solution to drive critical KPI reporting across numerous source systems. The customer was eager to adopt Snowflake, Matillion and Thoughtspot as part of a “cloud first” approach. After initial requirements-gathering we were able to discern aconceptual data model and followed it up with a logical one. We then knew that in order to produce an effective physical model, a formal modeling tool needed to be employed.
What immediately struck us about SqlDBM was its ease of setup and use (create an account and start modeling!), as well as its collaborative capabilities. Now we were able to create and alter the model anywhere, whether on site in front of the client or back at our home office (or in both places at once). It created great flexibility and agility in our ability to fine-tune the model in response to changes in data profiles and business rules. As a client-facing aid to assist the customer in understanding the model, it was very convenient and productive having access to it any time anywhere.
Export, Print and Document
SqlDBM is very responsive to the wishes of their customers. We experienced this first-hand as it became apparent that including the model as part of the delivery of documentation was going to be impossible without native export and print capabilities. During the course of our development work in SqlDBM, many other users chimed in on their support feedback forum requesting such a feature. By the time the project was completed, this feature had been implemented. We were able to create pixel-perfect PNG files to accompany our standard documentation to provide a visual guide for the customer’s model. Furthermore, we could instantly generate table and schema documentation and export to Excel directly from the model.
DDLs On The Fly!
SqlDBM’s cloud-based deployment really shines when it comes to forward and reverse engineering. If you’re modeling for Snowflake, CREATE TABLE DDL scripts can be generated with the click of a mouse once your model is complete. This has been further enhanced with the recent addition of direct Snowflake connectivity, which allows reverse engineering of an existing Snowflake data configuration. We used this technique to go back and validate the model during testing, ensuring that any “tweaks” to the tables still fit the original plan.
By adding SqlDBM to our toolbox, we now have a complete “soup-to-nuts” data management strategy and set of solutions in the cloud. This gives us great flexibility and agility when working with clients on their digital transformation journey through modern data applications. SqlDBM needs to be included in any “pure cloud play” approach to data and analytics.