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Overview

The platform has 2 sections:

  • Analytical (Workbench)
  • Production (Execution)

The Analytical layer is for analysts to test and validate their logic and get the required approvals and compliance checks. It connects to the existing historical data available in the Data Lake and provides a space for free form analytics and workflow-driven analytics. The end-goal of using the analytical layer is understanding the data and using it to finalize the decision logic as an artifact-bundle.

The Production layer can be completely isolated from the Analytical layer and is meant to execute artifact-bundles created by the Analytical layer. If realtime production is required - it can be run as a simple API or a Python function call. If batch production is required - it can be run as a Spark job.

Most of the discussions in the documentation are regarding the Analytical layer. The production layer setup will be like a connection to an existing Production system and hence depends on how the Production system is currently set up.