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DATA Act Pilot: Simplicity is Key


  • Select an MVP target; ignore tasks which don’t meet that
  • Design for the least capable of your users
  • Validation logic should be maintainable outside the source code


18F has the pleasure of employing a plethora of truly great software developers. Unfortunately, our project-focus tends to silo our engineers from each other. Rather than wait for knowledge to naturally diffuse through team changes, we can kick-start transfer by highlighting some of the more interesting design decisions from existing projects. Today, we’ll focus on the (completed) DATA Act pilot. Importantly, though that project has finished, this is not meant to be a full retrospective or post-mortem; we’ll be focusing on technical decisions.

As context, the recently minted DATA Act requires agencies generate better, public records around money appropriated from Congress. Treasury has only recently (as of April, 2016) released the final, XBRL (a schema of XML) -based data standard. However, they did not want delays in creating the standard–which caused policy concerns, not technical concerns–to delay the start of agencies’ implementation work. The real hard work for agencies would be around connecting data sources which haven’t been joined before, not around formatting and syntax. Unfortunately, when the agencies hear “XBRL”, they think “contractors”. Rather than planning out how to combine data in house, they plan how to pay someone to generate the appropriate file format. This pilot aimed to remove this excuse by providing a simple interface for agencies to verify that they could combine the data.

Minimum viable product

While some agencies debated how to make a “data lake” and vendors assembled sales teams to sell DATA Act solutions, the engineers on this pilot focused on a minimal viable product. It would contain two endpoints: one to display a form and one to accept input and report validation errors. There would be no file storage, no authentication (aside from minimal Basic Auth), and no front end build process. They would deploy on and selected a familiar, Flask stack. The team anticipated writing “throwaway” code, a simple demonstration that such an application could exist; they wanted to create that demonstration quickly with few resources. Ultimately, they were quite successful in achieving this goal. The application has been a routine reference point during DATA Act conversations long after the pilot ended.

The proof-of-concept focus had its drawbacks, however. While the team argues that actively ignoring certain best practices was the right choice for many of their decisions, the lack of test coverage would be painful as the project wound down. For example, during this period, they realized that the “happy path” (with no validation errors) caused the application to explode; they had only (manually) run through a handful of scenarios while testing. The team described viewing code as “not real” to be a trap. Their small code base quickly grew larger than anticipated and their early technical decisions would have long lasting ramifications.

Diagram matching above description

Simplicity avoids scary

One of the earliest decisions the team grappled with centered around the data format they would receive from agencies. XML/XBRL was off the table as even a simpler schema would surely trigger the CIO search light. After an initial attempt using Protocol Buffers (“a terrible decision”), they realized they needed something that the folks responsible for the data would understand. Comma Separated Values fit the bill; they aren’t intimidating and are easy to export from existing spreadsheet software and database applications. With them, agencies could focus on finding the data, not encoding it.

Relevant data would be submitted as four separate CSVs, each listing entries for one type of data. Think of it as a very rudimentary (and therefore approachable) set of four RDMS tables, complete with foreign keys. Rather than defining a schema (or waiting for the official schema to be complete), the team generated example CSVs to serve as templates. These were made available, along with the codebase, on GitHub, and would be referenced long after the pilot completed. By defining a set of validations over these files rather than the too-difficult-to-generate-and-not-completely-defined XML schema, agencies could receive feedback about their data now. Further, in the final sprints of the pilot project, the team implemented a simple conversion between the CSV files and the “final” XML/XBRL format, meaning that agencies could continue to work with a format they understood.

Validation rules

Two basic schools of thought dominate discussions of data standards. One holds that data validation should be defined along with the standard. Database schemas include constraints, XML files can be validated against an XSD; by using the sophisticated rules of these and similar languages, one can prevent bad data from entering the system. The second camp argues that the standards are constantly evolving and need to remain flexible, that the validation rules are akin to unit tests for the schema. Further, the language of validation isn’t rich enough to support all of the rules needed. As we will be writing validation rules outside of the data format, why not keep the two separate from the beginning?

With experience of similar problems in industry and knowledge that the XML/XBRL schema was still in flux, the team aligned itself with the second camp. The rules shouldn’t be part of the schema (and couldn’t easily, given CSV input), but they also didn’t want validation logic to live entirely in Python. Requiring developer intervention for all edits would be quite costly, particularly as the rules would be changing over the course of the pilot. If CSVs were a good common denominator for accepting data, perhaps they would be a good format for defining simple validation rules?

fieldname required data_type field_length unique
AwardandModificationEntryID False int 25 False
PlaceOfPerformanceEntryNumber False int 25 False

This approach proved to be quite handy. It enforced a very clean separation between “business logic” and the rest of the application. It also meant that maintaining these rules as the formal specification changed would be very easy. Editing rules was so easy, in fact, that our agency partner could maintain the rules themselves; they’ve modified these rules over time to fit new expectations. Additional fields could be added and rules applied to them all by simply editing a spreadsheet.

Of course, this scheme doesn’t scale forever. Certain requirements can’t be easily described this way, but these requirements were not encountered over the course of the pilot. The team says that they would have tried to tackle more complex validations in a similar manner, defining the new rule via CSV descriptions. They gained so much value from the approach that they’d work hard to extend it to new problems. When asked how these types of problems are handled in industry, the team pointed to Google’s use of Prolog as a rule engine.


Though this pilot had a very tight focus, it offers several interesting technical decisions which are worth sharing. Determining, at the outset, what a minimum viable product looked like and avoiding “production-ready” concerns like server load, malicious users, etc. allowed the team to exceed their client’s expectations in short order. Building for a least common denominator (CSVs) gave the project reach (more users could participate) and reduced code complexity. Pulling out validation rules into a separate, easy-to-modify format made the product flexible and simple to maintain. Do any of these principles make sense for your project?

This document is the distillation of an architecture discussion between Aaron Borden, Jacob Kaplan-Moss, CM Lubinski, Micah Saul, Marco Segreto, and Becky Sweger. For more information about the DATA Act pilot, see, particularly their screencasts.