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nefertem
is an exetensible framework for monitoring and managing data quality processes. With nefertem
you can define your own data quality process, run them and get the results. You can also create specific plugins that enable the use of your favourite data quality frameworks.
nefertem
adopt a run execution model. A user defines an execution run with a client that handles also the I/O storages. Every run is executed under an experiment, an organizational unit.
Running nefertem
produces in-memory objects, deriving from the execution frameworks plugged-in (e.g. frictionless, ydata_profiling, etc.), a bunch of process descriptive metadata and a series of artifacts that can be persisted on various backend storage.
The typical workflow involves the configuration of the resources, of the input storages in which the resources are saved (local or remote filesystems, databases and datalakes) and the configuration of the run itself, where the user specifies the desired operations and the frameworks to be used.
Out-of-the-box nefertem
supports the following data quality operation:
import nefertem
# Set configurations
output_path = "./nefertem_run"
store = {"name": "local", "store_type": "local"}
data_resource = {
"name": "resource_name",
"path": "path/to/resource",
"store": "local",
}
run_config = {
"operation": "inference",
"exec_config": [{"framework": "frictionless"}]
}
# Create a client and run
client = nefertem.create_client(output_path=output_path, store=[store])
with client.create_run([data_resource], run_config) as nt_run:
nt_run.infer()
nt_run.log_schema()
nt_run.persist_schema()
FAQs
Python data validation library
We found that nefertem demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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