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Microservices Expo: Blog Post

The Advantages of a Model-Based Approach

A Safer, Faster, and More Transparent Method of Managing Schema

The vast majority of schema management today is handled through the generation, review, and execution of SQL scripts.  These scripts can be tiny or huge; they can encapsulate the creation and relationships of several objects or they can describe a one-time alteration to a single object.  Once executed they generally leave no history of their passing other than the presence of the pieces they create, delete or modify; you can be dependent on hundreds of small scripts or on one giant script to build out new environments or evaluate existing ones.  You’re left with a schema that is a massive collection of individual parts applied in an order you can’t reproduce.

Assessing the compatibility of the schema you develop against in relation to what’s in production is a time consuming and error prone process. When you craft new changes, ferreting out the possible impact on other objects in your schema is not intuitive. The specific business needs associated with the individual parts of your schema get murky over time and it’s harder for you to design to your future instead of accommodating your past. There is no traceable history of who did what and why from environment to environment.  Application issues caused by database errors become hard to troubleshoot because there is no easily digestible standard to use as measuring stick in evaluating a malfunctioning environment.

At Datical, we talk a lot about the model-based approach we take to database schema management and how it’s superior to the scenario described above.  When you use Datical DB to employ a model of your database schema, everyone in your organization is working with a transparent and comprehensive representation of your entire schema. There’s no more wandering off into the weeds of individual changes with no sense of history or relationship when trying to make sense of your schema. The model enables a faster, safer, and more transparent method of managing your schema.  Here are some of the perks:

No need for SQL
Instead of writing SQL, the model powers a series of graphical form-based wizards that take the guess work out of change authoring.

Reliable change design based on what your database is, not what you think it should be.
When you need to update the schema, relationships between objects are clearly mapped out in the model and their purposes and history are fully documented and easily accessible.

Forecast: Impact Assessment on a Representative Model
Prior to execution the model can be used to simulate proposed changes in memory without touching your database allowing you to deploy with confidence in sensitive environments.

Traceable History of the Evolution of your Schema
Managing schema change with the model becomes an exercise in incrementally updating a single historical document. Changes are described in a simple, readable format.  The application features and business initiatives the changes support are tied to the changes themselves giving data architects and developers insight into why a change was made. The reliance on tribal knowledge disappears because everything you need to know about the “why” of a database change is tied to the change itself.

Easy Comparison of Models
Every database instance is now an instance of the same model.  The model provides the structure you need to quickly ascertain what’s missing, what’s wrong, and what needs to be done to bring your disparate environments into synchronization.

Clear View of Completeness
The operations personnel that deploy and monitor your applications can easily establish the database schema is everything it should be and detect drift more easily than they could previously by eyeballing diagrams or reviewing batches of deployment script.  If reality doesn’t match the model, the path to remediation is clear.

More Stories By Pete Pickerill

Pete Pickerill is Vice President of Products and Co-founder of Datical. Pete is a software industry veteran who has built his career in Austin’s technology sector. Prior to co-founding Datical, he was employee number one at Phurnace Software and helped lead the company to a high profile acquisition by BMC Software, Inc. Pete has spent the majority of his career in successful startups and the companies that acquired them including Loop One (acquired by NeoPost Solutions), WholeSecurity (acquired by Symantec, Inc.) and Phurnace Software.

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