AI Gets to Work

Automated Wealth: Build Passive Income Using AI Systems

4.9 - Lab: Extracting (Unload) Data from Snowflake to S3 📤🧪

4.8 - Snowpipe: Billing Estimation & Key Considerations for Data Ingestion 💰📊

4.7 - Lab: Create and Implement Snowpipe in Snowflake 🧪📤

4.6 - Introduction to Continuous Data Ingestion in Snowflake 🔄🚀

4.5 - Lab: Ingest JSON from S3 to Snowflake 🧪📥

004 - Lab: Ingest CSV from S3 to Snowflake 🧪🗂️

003 - Lab: Create an Integration Object to Connect Snowflake with AWS S3

001 - Section Overview: Snowflake - Data Loading, Ingestion, and Extraction

5.2 - Data Ingestion Section Overview: Snowflake - Data Loading, Ingestion, and Extraction

4.9 - Snowflake Search Optimization Feature: Improve Query Performance

4.6 - Lab: Selecting Your Clustering Keys in Snowflake

4.8 - Lab: Query Processing and Caching in Snowflake

5.1 - Data Ingestion Section Overview: Snowflake - Data Loading, Ingestion, and Extraction

4.4 - Lab: Micro-partitions and Clustering Keys in Snowflake

4.7 - Lab: Check Query Profile and History in Snowflake

4.5 - Understanding Clustering Depth and Cluster Overlap in Snowflake

4.3 - Benefits of Micro-partitions and Clustering in Snowflake 🌐

3.2 - Lab: Create Tables in Snowflake

3.3 - Snowflake: Views, Materialized Views, and Secure Views

3.5 - Lab: Create Secure Views in Snowflake

3.4 - Lab: Create Views in Snowflake

3.1 - Introduction: Different Types of Tables in Snowflake

3.6 - More About Views in Snowflake

4.2 - Introduction to Partitions and Clustering Keys in Snowflake

4.1 - Section Overview: Data Warehousing and Engineering with Snowflake

2.9 - Snowflake: Resource Monitors

2.8 - Snowflake: Track Your Consumption

2.7 - Snowflake: Understanding Different Billing Components

2.6 - Snowflake Virtual Warehouses: Powering Scalable Compute

2.5 - Snowflake Object Hierarchy: Understanding the Structure

2.4 - Snowflake Architecture: A Comprehensive Overview

2.3 - Lab: Setup Snowflake Trial Account

2.2 - Two Aspects of a Data Ecosystem

2.1 - What is a data warehouse

Topic 02 - Prerequisites and How to Succeed in This Course

Topic 01: Data Warehousing and Engineering Using Snowflake and AWS Cloud - Course Roadmap

4.5 Building AI agents with CrewAI with LLMs running locally on your computer

4.4 - Setting Up CrewAI: Step-by-Step Guide

4.3 - Installation and Setup: Open-Source LLMs (Meta’s LLaMA II)

4.2 - Installation and Setup: Python for Crew AI

4.1 - Introduction to Crew AI: Building Smarter Workflows

3.2 Setting Up Social Media Publishing Agent: AI Agent

3.3 - Testing and Going Live: AI Agent 1 - Social Media Publishing Agent

3.1 - AI Agent 1: Social Media Publishing Agent

2.5 - Zapier Actions: How to Configure and Use Them

2.4 - Zapier Introduction: Configuring AI Agents

2.3 - CustomGPTs: How to Create and Use Them

2.2 - AI Agents vs. Large Language Models: Understanding the Difference

2.1 - Automation of Tasks with AI Agents: Introduction to AI Agents

1 🤖 AI Agents - Build with ChatGPT, Zapier, CrewAI

40 ❄️ Creating Standalone and Dependent Trees of Tasks in Snowflake

38 ❄️ Demo: Drop and Undrop Table, Schema, and Database in Snowflake

39 ❄️ Demo: Clone with Time Travel in Snowflake - Step-by-Step Guide

37 ❄️ Demo: How to Enable Time Travel in Snowflake

36 ❄️ Time Travel vs Fail-safe in Snowflake: Understanding the Differences

35 ❄️ Time Travel in Snowflake: A Comprehensive Guide

34 ❄️ Creating a Dashboard for a COVID-19 Dataset in Snowsight: Step-by-Step Guide

33 ❄️ Dashboard Filters in Snowsight: Step-by-Step Guide