Deductive Pipeline Python Client

Deductive Pipeline Python Client

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The deductive tools library provides functionality to extend other Python libraries, typically boto3 and pandas, to make it easier to process, cleanse, and load large datasets at scale.

you can install it using pip as follows:

pip install dativatools

This includes the PipelineClient class, provide api key, source s3 location, destination s3 location, rules, and get source file cleaned and posted to destination.

Refer for more details.

  • param api_key - The individual key provided by the pipeline api
  • type api_key - str
  • param source_s3_url - The s3 source where the csv files are present
  • type source_s3_url - str
  • param destination_s3_url - The destination where the files are to be posted after cleansing
  • type destination_s3_url - str
  • param rules - Rules by which to clean the file
  • type rules - list, str specifying location of the rules file
  • param url - The url of the pipeline api, defaults to
  • type url - str
  • param status_url - the url to query for to check status of the api call, defaults to *{0}
  • type status_url - url
  • param source_delimiter - the delimiter of the source file, defaults to ,
  • type source_delimiter - str
  • param destination_delimiter - the delimiter of the destination file, defaults to ,
  • type destination_delimiter - str
  • param source_encoding - the encoding of the source file, defaults to utf-8
  • type source_encoding - str
  • param destination_encoding - the encoding of the destination file, defaults to utf-8
  • type destination_encoding - str
from import PipelineClient

obj = PipelineClient(api_key=api_key,

Previous: Overview | Next: Sample Data


Related documentation

  • Deductive Pipeline API on AWS - The Deductive Pipeline API is available through the AWS marketplace (more)
  • Deductive Pipeline API: Sample Data - Sample files to demonstrate usage of the Deductive Pipeline API (more)
  • Deductive Pipeline API: Validating basic data types - Validating incoming datasets for basic string, number, and date type formatting and range checks using the Deductive Data Pipeline API (more)
  • Deductive Pipeline API: Anonymizing data - The Deductive Pipeline API support tokenization, hashing, and encyrption of incoming datasets for anonymisation and pseudonymization (more)
  • Deductive Pipeline API: Referential Integrity - Using the Deductive Pipeline API to validate data against other known good datasets to ensure referential integrity (more)
  • Deductive Pipeline API: Handling invalid data - Invalid data can be quarantined or automatically fixed by the Deductive Data Pipeline API (more)
  • Deductive Pipeline API: Working with session data - The Deductive Pipeline API can check for gaps and overlaps in session data and automatically fix them (more)
  • Deductive Pipeline API: Reporting and monitoring data quality - The Deductive Pipeline API logs data that does not meet the defined rules and quarantines bad data (more)
  • Deductive Pipeline API: Full API reference - A field by field breakdown of the full functionality of the Deductive Data Pipeline API (more)

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Deductive is a global consulting firm providing data consulting and engineering services to companies that want to build and implement strategies to put data to work. We work with primary data generators, businesses harvesting their own internal data, data-centric service providers, data brokers, agencies, media buyers and media sellers.

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