Author: Prashanth Mally

Data is the lifeblood of the digital age. Algorithms collect, store, process, and analyze it to create new insights and value.

The data life cycle is the process by which data is created, used, and disposed of. It typically includes the following stages:

In the context of inventory management, integrating AI analytics involves leveraging advanced algorithms and models to gain insights, make predictions, or automate decision-making. Let's enhance the example with an illustrative AI analytics scenario.

Enhanced Step 1: Setup

Ensure that your Databricks environment is configured to support machine learning libraries and tools.

This document outlines the process of extracting table structures from SQL Server databases, converting them to JSON format, keeping them in Azure Studio, and then loading them into BigQuery using Cloud Data Fusion. The data types of the SQL Server data are converted to their corresponding BigQuery data types to ensure compatibility and accurate data analysis.

The process involves creating a JSON file with the converted data types and adding additional metadata columns. This file is then used in the Terraform code to define the infrastructure resources. Azure DevOps is integrated into the project to automate the infrastructure provisioning and data pipeline deployment.

The article describes a process that uses a GCP Cloud Function to connect to the Open Weather API, fetch weather data for a specific location, and insert the data into BigQuery. The Cloud Function is triggered by a Pub/Sub topic, which is a messaging service that allows you to decouple different components of your application.

Once the Cloud Function is triggered, it fetches the weather data from the Open Weather API. The Cloud Function then uses the BigQuery API to insert the data into a BigQuery table.