Inzata Labs

Python’s Utilization In Inzata BI Platform

Nikol H
Nikol H
A passionate tech enthusiast and seasoned tech blogger, Nikol's writing style is characterized by its clarity and accessibility. Whether demystifying the intricacies of artificial intelligence, or guiding readers through the world of data modeling, her articles are a beacon for those navigating the ever-evolving tech landscape.

Python is widely used and one of the top programming languages for data science, data ingestion, and transformation – technology areas belonging to the Business Intelligence IT vertical. Inzata has been leveraging Python in the following modules and components:

  1. InFlow – is a NoSQL fully interactive module for data ingestion and transformation within the comprehensive Inzata BI platform. There are two ingestion/transformation functions that allow a user to leverage fully:
    1. Row-based transformation method – allowing direct to define Python transformation code typically used for transforming streamed data (e.g.IoT)
    2. Complex transformation method – utilizing a Docker container wrapper within an InFlow function typically used in the situation, where there is a need to access the whole data set to code transformation logic.
  2. AI/ML Module– The whole AI/ML module is designed in Python and it allows either to use of a predefined set of AI/ML methods (see the list below) or the use of an interactive Python dev environment (e.g. Anaconda) to code its own AI/MM method (in such an interactive dev environment) is based on the large Python AI/NN library of functions. The greatest benefit is that the Python dev environment is integrated with Inzata, which significantly simplifies data sample preparation efforts, which are typically time-consuming. In terms of predefined, out-of-the-box available AI/ML,  function following methods are available:
    1. AI Neural Net model  with both regression and classification module
    2. SARIMA forecasting modules for time series forecasting
    3. ANOVA variance analysis
    4. Entity Matching –  for deduplication and record linkage at scale using an unsupervised learning approach.
    5. Text classification with the ability to parametrize it for  Nearest Neighbors, SVM, Gaussian Process, Decision Tree, Random Forest, etc.

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