AI Automation - Chat with Databases without SQL [No-Code]

start by understanding databases, tables, views, and relations before sql; learn to reduce complexity with views and extract table schemas with distinct values into google sheets for nal sql.

Set up the MySQL Northwind database on AWS RDS, connect with MySQL Workbench, test public access, import the schema, and explore the Northwind tables to run a no-code SQL workflow.

Explore the database schema and ER diagram, showing how customers, orders, order details, products, and suppliers relate. Learn to create a view combining orders and details to manage price data.

Create a database view to simplify data by joining orders, order_details, and products, calculating sales amount and year for KPI insights, while addressing case sensitivity and setup steps.

Set up Google Sheets for each table schema by importing CSV data, naming sheets after tables, and removing duplicates to train the NLP model with clean text data.

Format numeric columns like price, quantity, and sales amount, and set date formats in Google Sheets to support arithmetic and SQL data types, while IDs stay for relationships.

Import all Northwind tables into the SQL portal by adding tables with spreadsheet links, DB schema, and table names, then set up relationships for KPI search and NLP model training.

Set up joins in the order view to link customers, employees, shippers, and products with suppliers and categories, establishing primary and foreign key relationships in the NoSQL portal.

Configure the main KPIs in the analytics portal by adding orders, sales, and sales quantity; set up arguments and columns to compute totals by customer, product, or category in USD.

Deploy and configure the Nlsql app on Azure via marketplace, including resource group, api token, and NoSQL app name, then test web chat and note arm templates for students.

Explore the Azure resource group for the NoSQL application, review the web app, managed identity, App Service, and deployment configurations, and learn how to update database credentials and data source.

Explore no-code web chat integration with SQL data to generate KPIs for orders, sales, and quantity, and view analytics through reports without writing queries.

Attach the sql bot to Microsoft Teams via the NLSQL channel chat, configure the direct line rest api, and run sample queries like orders and sales by supplier.

Use the no-code sql chat bot to fetch orders by column name, generating csv reports for customers, products, shippers, and suppliers, and explore order sales and sales quantity kpis.

Learn to fetch sale quantity by various table column names, such as customer name, employee first name, shipper, supplier, and product, using no-code database queries.

Explore using sales kpi with different column names to measure sales by customer names, first names, supplier names, and shipper names, and discuss discounts and performance recognition.

learn how to fetch all three kpis—orders, sales, and sale quantity—in one prompt and generate a report by customer name, including cost in usd, to compare and celebrate performance.

Learn how to handle identical data across multiple tables, such as country in both customer and supplier tables, when querying sales by UK.

Learn to handle the same column name across multiple tables, with examples like country in customers and suppliers. A forthcoming option will let you specify the source table.

Define and display text-based KPIs such as contact name, address, city, postal code, and country from customer data, then fetch results without writing any query in a no-code workflow.

Learn how synonyms in the no-code SQL portal make database queries human-friendly by mapping spaces to column names like customer name, first name, and product name.

Configure top and bottom arguments on each table to generate instant KPI reports, covering customers, products, employees, and suppliers, with csv exports for larger results.

Configure complex arguments to compute the average received by customer, employee, or product by dividing total amount by total orders.

Update older order dates by adding a 26-year interval to align with the current year, then alter the order view to reflect the changes, demonstrating a simple approach.

Configure the complex growth argument by year to filter order data by 2023 and 2022 and calculate growth percentage using the sales amount with total sum division.

Configure growth by quarter with order date and date quarter filters, selecting sales amount and total sum. Compare this year to previous year and generate KPIs in a no-code workflow.

Configure the date column to unlock date-based insights, enabling sales by last 50 days, specific months, and KPI reports such as top customers and top employees.

Configure the country column for the customers table map by selecting the full country name format and saving, laying groundwork for the upcoming map chart and the next section.

Explore pie, line, bar charts, maps, bubble, and scatter visuals to help management make informed decisions; note stacked bar charts aren’t compatible with MySQL.

Visualize data with interactive pie charts, filtering sales by date and category such as employee name, customer name, supplier name, and shipper name, then download or embed charts for reports.

Discover how bar and pie charts visualize sales, orders, and sales quantity by shipper, employee, customer, and product names for October and November 2022.

Explore line charts to visualize sales by shipper name, employee name, and product name, with dates from July 2022 to Feb 2023 and features like zoom and auto scale.

Explore how to create and interpret scatter charts for single-kpi sales data by employee, product, and customer names, and learn how to embed these visuals in reports and presentations.

Plot a single KPI by country on a map chart, such as sales, with zoomable regional insights for management and interactive reports using pie, line, bar, scatter, and map charts.

Use the analytics page to view prompts' generated SQL queries, verify results, and collaborate with the SQL team to fix issues or run queries in MySQL Workbench.

During production, migrate from Google Sheets to an API-driven Cosmos DB update. Use the NoSQL API for new columns and distinct values.