The Collect, Store, Validate Pattern
What is the best flat-file management solution for the CsvPath Language?
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What is the best flat-file management solution for the CsvPath Language?
Last updated
The CsvPath Language is a declarative validation language. It provides strong validation for delimited and tabular data using structural schemas and data validation rules. As the most capable and advanced such tool, it is very focused on its task.
The CsvPath Framework implements the CsvPath Language, and goes far beyond it to provide a prebuilt preboarding process that acts as the trusted publisher to the data lake and applications. You can use CsvPath Language in all sorts of ways. The Framework, however, is opinionated. It is all about the Collect, Store, Validate Pattern.
Design patterns make it easy to reuse proven approaches and communicate about designs. The Collect, Store, Validate Pattern is a narrowly focused architecture for landing data files, identifying, validating, and upgrading them, and publishing known-good data for downstream consumers. It fills the gap between MFT (managed file transfer) and the typical data lake architecture. CsvPath Language validation is core to the pattern, but there is so much more.
Onboarding flat files may feel like a simple thing; too simple to focus on and pattern. That would be a mistake. Flat files are widespread, drive large-scale revenue and value-delivered, and are trickier and more expensive than you might think. 4 of this author's last 6 companies derived more than half a billion USD a year in revenue based on flat-file data exchanges. The 5th provided tools that, in part, enabled companies to do many billions of dollars more of flat-file-dependent business. The 6th exchanged vital research data, a substantial amount in flat-files, that impacts essentially everyone alive on the planet. These were all leading-edge companies.
In short, flat files may be ugly and old, but they are complex and vital and an omnipresent win/lose component of economic activity.
So, back to Collect, Store, Validate. The CSV Pattern structures the interstitial space where files enter the organization. It is also applicable to where and how flat-files leave the organization, but let's set that slightly simpler problem aside for now. The space between where a file stops being data in-flight in an on-the-wire protocol and where it is loaded into a data lake is the CSV Pattern's focus. In some cases there is a soft-focus distinction between the CSV Pattern and the data lake, but let's set that gray area aside for the moment as well.
The activities within the interstitial space that any pattern would need to address are:
File landing
File and data registration
Preliminary validation
Data shaping
Business rules validation
State tracking
Archiving
File presentment or loading
Each of these has needs, decisions, and strategies. Let's break them down.
How are files received and initially stored.
What protocol(s) do they arrive on?
Are all files kept together or separated?
What are the file naming conventions?
Files and their data require a clear identity, filecycle, and point on the lifecycle. A birth certificate and a social security number. This is the beginning of the data's lineage. It is the first step towards the file's and the data's respective senescence.
How are files identified?
How do we know what data a file contains?
For any given source, what is the unit of data within a file or files?
What lifecycle is the file on?
How can a user see the lifecycle states a file has been in?
How can another system ask for the file?
When is the file no longer important?
This stage is the recognition and fitness test.
Can we recognize that this file and its data are a certain type of file and data, regardless of its ultimate correctness? (Is it well-formed, from an identifiable namespace, in a charset expected, etc.)
Does the data conform to a structure we determine in advance?
The data needs to be adjusted to eliminate any minor issues and conformed to broad-brush expectations.
Are there variations loose upstream requirements that can be automatically corrected to upgrade fidelity?
Do we need to add timestamps, IDs, or other metadata?
Are there modest adjustments that would harmonize different sources?
Detailed rules exist in any organization. They come in both precise and/or technical form and impressionistic or statistical heuristics. Some of these constraints can be captured as structure. Many of them must be declared in terms of logical rules.
Does the data fit or map to a structural definition
Are there co-ocurances, statistical tests, precedence relationships, arithmetical limits, or other requirements?
Validation and shaping are best done in small increments. Small increments save development time, are more provably correct, and enable rewind/replay and forensic inspection. Managing small increments increases the importance of state tracking.
How do we know what state a unit of data is in?
Can we go back to a known-good state and push a unit of data through the remaining lifecycle steps again?
Is it easy to inspect data in each state it evolves into?
Files are part of a business process record. As such they are controlled by data retention rules, explicit or in-practice, and cannot be deleted or handled in a way that obscures the part they play.
How long are files kept?
Where are they stored long-term and in what format?
Are the intermediate state files kept after processing completes or only the final-form version?
How can we find and inspect files potentially years after the fact?
The data that is released into a data lake should be trustworthy and conform to expectations. CsvPath Framework is that trusted publisher to the data lake or other data systems.
Are files pushed, pulled, or both?
How do we identify data to downstream systems?
What happens if data is rejected by downstream?
The Collect, Store, Validate Pattern answers most of the questions above. There is, of course, flexibility in any pattern-based design that allows for variability. At a high level, the CSV Pattern looks like this picture.
The main thrust answers are these:
Capture data as files and maintain as files until they are known-valid data in a conformed format ready for downstream cataloging and/or loading
Process files in a strictly linear CSV-only lifecycle
Define processing steps narrowly—more steps is better than fewer, all else being equal
Separate the types of validations—file format, data structure, and business rules—as much as practical
Follow a level-of-engagement progression: format check -> structure check -> fidelity-upgrade -> business rules check
Progress the file lifecycle with copy-on-write semantics so that data is never lost, confused, or untrace-backable
Enable lifecycle rewind and stage replay to allow for data fixes without rerunning from scratch
Err on the side of over-capturing stage metadata for lineage control and understanding point-in-time goodness
End the CSV Pattern lifecycle with immutable data archived in a trustworthy state and made available to downstream consumers as the trusted publisher
Automate all of the above stages, decision points, and integrations
If you read this and think: how else would you do it? that is good. In practice, though, in many companies the pattern is not this clear and intentional. In fact, in many companies the pattern isn't consistently apparent across the operation(s). And in a some more technically savvy companies CSV coexists or competes with other approaches with their own merits. Those approaches may be API oriented, focus on user-self-service, center on streaming parallel processes, etc.—or combinations of any of these. That is fine too. No pattern can cover every possible situation. We believe the CSV Pattern covers the majority of trasaction-oriented, many-party, tabular data, loose-integration situations—particularly those where one or more of the parties is technically weak for any reason.
CsvPath Framework provides the core CSV Pattern features prebuilt, preintegrated, and ready to run.
The Framework stores input data, CsvPath Language, and results files in three separate repositories
It manages the lifecycle as a strictly linear process implemented by the source data files repository, any number of well-structured groups of CsvPath Language statements, and a consistent flow of data from one stage to the next
Data file changes have copy-on-write semantics
A substantial volume of metadata is captured for each stage of the lifecycle
Lifecycle rewind and stage replay are easily available
CsvPath Framework enables a variety of validation strategies, of course, including narrow definitions, structural and rules-based validation, logical operations choices, types of stage-chaining, and user-defined extension functions. And it provides tooling around output, errors, and asset identities.
At a high level, it looks like this:
This is admitedly barely a sketch. It shows that files arrive at the left (green) and progress into the file storage area (blue) before CsvPath Language files are applied to generate or pass through results (red).
The three asset management areas are for source data files, CsvPath Language files, and results, which are the combination of data, metadata, printouts, and errors. Those three asset management areas are on the filesystem. Their structures look like these:
None of these areas is off-limits to CsvPath Framework users. However, you should not need to do any work in these areas, other than to collect results data or triage issues. Once you register your assets with CsvPath, the Framework makes a copy, assigns an identity, assembles metadata, and executes runs on command.
As you can see in the screenshot below, a new CsvPath project will have both the inputs and archive directories. The Framework creates those the first time you run the CLI or create a CsvPath or CsvPaths instance. inputs is the root of the named-file and named-paths areas. Named-files are data files that have been received for processing. Named-paths are CsvPath Language validation and data shaping statements. The archive directory is where the results of processing steps live. Copy-on-write is implemented in this area.
For more details on the asset locations please read and , and . In addition, gives more information about the runtime, configuration, and user-defined metadata collected in results. To learn about how CsvPath Framework implements Rewind and Replay read . And talks about how CsvPath Language can be used for both structural and rules-based validation, allowing for stronger, more fine-grained, and more logically separated validations.
