Package: datacraft
DatacraftDatacraft: Your Synthetic Data Engine 🚀Ever felt trapped in the endless cycle of creating mock datasets for testing or bogged down by bulky CSV files?
Enter Datacraft – a sleek, JSON-driven engine that crafts synthetic data tailored to your needs. With our unique
domain-specific language (DSL), designing and populating complex data structures becomes a breeze. Whether you're
targeting JSON, XML, CSV, or database rows, Datacraft provides the blueprint and the toolkit.Key Features:🛠️ Versatile Data Design: Use our Data Spec and Field Spec paradigms to decouple data values from their structure.🔧 Customizable: Out-of-the-box field types not cutting it? Easily define your own with ourCustom Code Loading🌐 Jinja2 Templating: Seamlessly integrate templating using the popular Jinja2 engine.💡 Python API: Integrate directly with your test suites or applications. No need for third-party tools.⚙️Command Line Magic: With just a few commands, generate millions—or even billions—of records on the fly.Switch to Datacraft today and redefine how you generate and manage synthetic data. Dive in below to get started!OverviewDatacraft is a tool for generating synthetic data. We do this by providing a JSON based domain specific language
(DSL) for specifying the fields present in a record apart from what form the record takes. The goal is to separate
the structure of the data from the values that populate it. We do this by defining two core concepts: the Data Spec
and the Field Spec. A Data Spec is used to define all the fields that should be generated for a record. The Data
Spec does not care about the structure of the records it will populate. A single Data Spec could be used to generate
JSON, XML, a csv file, or rows in a Database. Each field in the Data Spec is described by a Field Spec. A Field Spec
defines how the values for a field should be generated. There are a variety of built-in field types that can be used
to describe the data structure and format for fields. Where the built-in types are not sufficient, there is an easy
way to create custom types and handlers for them usingCustom Code Loading. Thedatacrafttool
supports templating using theJinja2templating engine format.Data is a key part of any application. Synthetic data can be used to test and exercise a system while it is under
development or modification. By using a Data Spec to generate this synthetic data, it is more compact and easier to
modify, update, and manage. It also lends itself to sharing and reuse. Instead of hosting large data files full of
synthetic test data, you can build Data Specs that encapsulate the information needed to generate the data. If
well-designed, these can be easier to inspect and reason through compared with scanning thousands of lines of a csv
file.datacraftmakes it easy to generate millions or billions of records to use for development and testing of
new or existing systems. Datacraft also has a python API so that you can generate your synthetic data as part of your
test suite or application without have to use online tools or external services.DocsFind the latest documentation and detailed usage information here:datacraft.readthedocs.ioInstallation$pipinstalldatacraft

$datacraft-h# for full command line usageBasic UsageCommand Line$datacrafttype-list# list all available field spec types ...$datacraft--type-helpcombine
INFO[05-Jun-205005:52:59PM]StartingLoadingConfigurations...
INFO[05-Jun-205005:52:59PM]Loadingcustomtypeloader:core
INFO[05-Jun-205005:52:59PM]Loadingcustomtypeloader:xeger
-------------------------------------
combine|ExampleSpec:{"name":{"type":"combine","refs":["first","last"],"config":{"join_with":" "}},"refs":{"first":{"type":"values","data":["zebra","hedgehog","llama","flamingo"]},"last":{"type":"values","data":["jones","smith","williams"]}}}datacraft-sspec.json-i3--formatjson-x-loff[{"name":"zebra jones"},{"name":"hedgehog smith"},{"name":"llama williams"}]Python APIimportdatacraftspec={"id":{"type":"uuid"},"timestamp":{"type":"date.iso.millis"},"handle":{"type":"cc-word","config":{"min":4,"max":8,"prefix":"@"}}}print(*datacraft.entries(spec,3),sep='\n'){'id':'40bf8be1-23d2-4e93-9b8b-b37103c4b18c','timestamp':'2050-12-03T20:40:03.709','handle':'@WPNn'}{'id':'3bb5789e-10d1-4ae3-ae61-e0682dad8ecf','timestamp':'2050-11-20T02:57:48.131','handle':'@kl1KUdtT'}{'id':'474a439a-8582-46a2-84d6-58bfbfa10bca','timestamp':'2050-11-29T18:08:44.971','handle':'@XDvquPI'}Type Helpimportdatacraft# List all registered types:datacraft.registered_types()['calculate','char_class','cc-ascii','cc-lower','...','uuid','values','replace','regex_replace']# Print API usage for a specific type or typesprint(datacraft.type_usage('char_class','replace','...'))# Example Output"""-------------------------------------replace | API Example:import datacraftspec = {"field": {"type": "values","data": ["foo", "bar", "baz"]},"replacement": {"type": "replace","data": {"ba": "fi"},"ref": "field"}}print(*datacraft.entries(spec, 3), sep='\n'){'field': 'foo', 'replacement': 'foo'}{'field': 'bar', 'replacement': 'fir'}{'field': 'baz', 'replacement': 'fiz'}"""For more detailed documentation please see:datacraft.readthedocs.io