Dataiku code recipes, datasets, environments and extension boundaries
Model the Flow dependency
A Dataiku recipe represents a declared transformation between relevant inputs and outputs. An exploratory notebook and a reproducible Flow step serve different purposes. Use actual declared dependencies, appropriate schema and supported execution settings so another authorized run can reproduce the intended transformation. A successful notebook cell does not prove that scheduled production data will be built correctly. Reading a dataset into a dataframe also does not establish that it fits safely in memory.
Use the actual API contract
The dataiku package supplies relevant in-platform dataset, folder and context functions, while dataikuapi exposes public administration/client interfaces. They are not interchangeable classes with identical methods. Dataset reads can use dataframe, chunked or row-oriented interfaces according to the actual source and memory needs. A chunk size limits one returned batch rather than guaranteeing that every downstream operation has bounded memory. Writing with schema inference can modify the output schema; that behavior requires deliberate compatibility and permission review.
Manage code and its dependencies
A code environment controls relevant language/package dependencies. It is not a complete isolation boundary against malicious code or permission misuse. Supported container execution, installed packages and plugin requirements depend on actual DSS version, administration and platform. Shared project libraries can reduce duplicated code, but imports and updates still need version control, testing and authorized distribution. Treat credentials and connection permissions separately from ordinary configurable values.
Extend without inventing universal behavior
A plugin packages supported extension components with metadata and component-specific configuration. Recipe roles and parameter definitions help map configured inputs and settings to execution, while connector/writer interfaces have their own contracts. Validate configuration, input data, failure behavior and supported deployment dependencies rather than assume that packaging proves correctness. Webapp frontend authorization and backend execution authority can differ. These written examples run no Dataiku recipe, install no plugin, access no credentials and change no schema. Current Developer Certification requirements and actual DSS release documentation govern their respective scopes; related visual-ML and Python foundations here are prerequisites, not a claim that the practice reproduces a complete official hands-on assessment.