Effektiv mit Data in Snowflake chatten

Table of Contents

1. Introduction
2. Project Overview
3. Prerequisites
4. Setting Up API Keys
5. Setting Up Snowflake User
6. Downloading the Data Set and Data Dictionary
7. Loading the Dimension and Fact Tables
8. Creating the DBT Models
9. Creating the DBT Schema Files
10. Starting the Streamlit App

Introduction

In this article, we will explore a data engineering project that uses Chat GPT, DBT, Snowflake, and Streamlit to Create a chat application that interacts with a Snowflake environment. We will go through the step-by-step process of setting up the project, loading the data, creating DBT models, and running the Streamlit app.

Project Overview

The project aims to build a chat application, called Snow Chat, that allows end users to Interact with a Snowflake environment and obtain data-driven insights. The application utilizes external data sets, passes the attributes to Chat GPT, and requests Chat GPT to create data and DBT models. These models are executed to load target Snowflake tables, and a Streamlit app is created to allow end users to ask questions and receive responses Based on the available data.

Prerequisites

Before starting the project, there are a few prerequisites that need to be met. You will need an Ambition account with Docker installed, a Snowflake account, and an OpenAI account. The article provides instructions on generating an API key for OpenAI and creating a Snowflake user for both DBT and Streamlit to perform the necessary loads and queries.

Setting Up API Keys

To generate an API key for OpenAI, log into your OpenAI account and navigate to the API Keys section. Click on "Create New API Key" and generate one specifically for Streamlit. The generated API key will be required for the integration between Streamlit and OpenAI.

Setting Up Snowflake User

To set up the Snowflake user, the article provides SQL queries to create the DBT user and the Streamlit user. These queries create the necessary user, role, warehouse, database, and schema for the demo. You can customize the queries by setting a strong password and modifying the naming conventions or database name.

Downloading the Data Set and Data Dictionary

For the project demonstration, you will need to download the NYC Yellow Taxi trip data set and the corresponding data dictionary from the NYC government Website. The article provides the necessary links for downloading these files. If you are using the provided Snowflake examples, the data files are already available within the DBT seeds and trips directories.

Loading the Dimension and Fact Tables

To load the dimension and fact tables, you will need to copy the attribute names from the data set and feed them into Chat GPT. The article includes a prompt that provides the rules for creating the Dimensions and fact tables. It guides you through the process of creating seed files using the data dictionary and loading the seed data.

Creating the DBT Models

The article provides Prompts that you can use to request Chat GPT to generate the necessary DBT models for the data set. The generated SQL code can be copied into the DBT models directory, which is then executed to load the target Snowflake tables. The article demonstrates the process of running DBT to validate the models.

Creating the DBT Schema Files

The DBT schema files are used to update the column and table descriptions in the Snowflake environment. The article provides instructions on creating a copy of the secrets.domel.template file and updating it with the OpenAI API Key and the Snowflake connection details. These files are mounted as volumes within the Docker container.

Starting the Streamlit App

To start the Streamlit app, the article provides a Docker Compose file that sets up the necessary dependencies and environment. You will need to create a copy of the secrets.domel.template file and save it as secrets.normel, providing the OpenAI API key and Snowflake connection details. The article demonstrates how to initiate the connection and start the Streamlit app.

Note: The article contains code snippets and visuals to assist with the setup and implementation of the project.

Article

Introduction to Snow Chat

Snow Chat is a powerful chat application that combines the capabilities of Chat GPT, DBT, Snowflake, and Streamlit to create an interactive data-driven environment. This article will guide you through the step-by-step process of setting up the project, loading the data, creating DBT models, and running the Streamlit app. By the end of this article, you will have a fully functional chat application that allows end users to obtain insights from data stored in a Snowflake environment.

Project Overview

The main goal of this project is to build a chat application, named Snow Chat, that enables users to interact with a Snowflake environment and access data-driven information. The application utilizes external data sets, passes the attributes to Chat GPT – a powerful language model developed by OpenAI – and requests Chat GPT to create data and DBT (Data Build Tool) models. These models are then executed to load target Snowflake tables. The Streamlit app allows end users to ask questions and receive responses based on the available data in the Snowflake environment.

Prerequisites

Before getting started with the project, let's ensure we have all the necessary prerequisites in place.

1. Ambition Account with Docker: Ensure you have an active Ambition account and Docker installed on your system. Docker will be used to set up and manage the required containers for this project.

2. Snowflake Account: You will need a Snowflake account to store and access the data for this project.

3. OpenAI Account: Sign up for an OpenAI account to leverage their powerful language model, Chat GPT.

4. Secrets and Environment Setup: We will configure the necessary API keys, credentials, and environment variables to connect and interact with the OpenAI and Snowflake services.

Once you have fulfilled these prerequisites, you are ready to proceed with the project.

Setting Up API Keys

To interact with the OpenAI service, you will need to generate an API key. Follow these steps to generate an API key specific to Streamlit:

1. Log in to your OpenAI account.

2. Navigate to the API Keys section.

3. Click on "Create New API Key."

4. Generate an API key specifically for Streamlit.

Once you have generated the API key, you will use it to connect your Streamlit application with the OpenAI service.

Setting Up Snowflake User

To perform the necessary loads and queries in Snowflake, we need to create a Snowflake user for both DBT and Streamlit. Follow these steps to set up the Snowflake users:

1. Use the provided SQL queries to set up the DBT user and the Streamlit user. These queries will create the required user, role, warehouse, database, and schema for this demo.

2. Customize the queries by setting a strong password and modifying the naming conventions or database name according to your preferences.

Make sure to save the generated Snowflake user credentials, as they will be used during the project setup.

Downloading the Data Set and Data Dictionary

To demonstrate the project, we will use the NYC Yellow Taxi trip data set. Additionally, we will need the corresponding data dictionary to build reference files. Follow these steps to download the required files:

1. Download the NYC Yellow Taxi trip data set from the provided link.

2. Download the corresponding data dictionary from the NYC government website.

If you are using the provided Snowflake examples, the required data files are already available within the DBT seeds and trips directories. You can proceed to the next step.

Loading the Dimension and Fact Tables

For this project, we need to load the dimension and fact tables required for the Snowflake environment. Follow these steps to load the tables:

1. Copy the attribute names from the downloaded data set.

2. Pass the attribute names to Chat GPT using the provided prompt. This will help Chat GPT generate the necessary dimensions and fact tables for the data set.

3. Create seed files using the data dictionary and load the seed data into the Snowflake environment.

Once the seed data is loaded, you will have the required dimension and fact tables ready to use.

Creating the DBT Models

DBT (Data Build Tool) is a powerful tool used for managing and transforming data in data warehouses. In this step, we will create the necessary DBT models for our data set. Follow these steps to create the DBT models:

1. Use the provided prompt to request Chat GPT to generate the DBT models for our data set.

2. Copy the generated SQL code into the DBT models directory.

3. Execute the DBT models to load the target Snowflake tables.

Running DBT will validate the generated models and load the data into the Snowflake environment. This will allow us to utilize the data in our Streamlit app.

Creating the DBT Schema Files

To update the column and table descriptions in the Snowflake environment, we will create DBT schema files. These files will be used to store the metadata and allow our Streamlit app to interpret the data. Follow these steps to create the DBT schema files:

1. Create a copy of the secrets.domel.template file provided in the Source.stream directory.

2. Update the copied file with your OpenAI API key and Snowflake connection details.

These files will be mounted as volumes within the Docker container, allowing the Streamlit app to access the required information.

Starting the Streamlit App

Now it's time to start the Streamlit app and interact with the Snowflake environment. Follow these steps to launch the Streamlit app:

1. Use the provided Docker Compose file to set up and configure the necessary dependencies and environment.

2. Create a copy of the secrets.domel.template file and save it as secrets.normel. Update the copied file with your OpenAI API key and Snowflake connection details.

3. Initiate the connection and start the Streamlit app using the Docker Compose file.

Once the app is up and running, you will be able to interact with the Snowflake environment through the Snow Chat interface. You can ask questions about the data, perform queries, and receive responses based on the available information.

Congratulations! You have successfully set up the Snow Chat project and built a powerful chat application that allows you to interact with data stored in a Snowflake environment. Explore the functionalities of the Snow Chat app and use it to gain insights from your data.

Highlights

• Snow Chat is a chat application that integrates Chat GPT, DBT, Snowflake, and Streamlit.
• The project involves loading data sets, creating DBT models, and running a Streamlit app.
• Prerequisites include an Ambition account with Docker, a Snowflake account, and an OpenAI account.
• API keys are generated for the OpenAI service and Snowflake user accounts are set up.
• The project demonstrates how to download data sets and data dictionaries for loading into Snowflake.
• DBT models are created for loading the dimension and fact tables.
• The Streamlit app is started to interact with the Snowflake environment and obtain insights from the data.

FAQ

Q: What is Snow Chat?

A: Snow Chat is a chat application that combines Chat GPT, DBT, Snowflake, and Streamlit. It allows users to interact with a Snowflake environment and access data-driven insights.

Q: What are the prerequisites for the Snow Chat project?

A: The prerequisites for the Snow Chat project include an Ambition account with Docker, a Snowflake account, and an OpenAI account. Additionally, the necessary API keys and credentials need to be set up.

Q: How can I generate an API key for OpenAI?

A: To generate an API key for OpenAI, log in to your OpenAI account, navigate to the API Keys section, and click on "Create New API Key." Generate an API key specifically for Streamlit.

Q: How do I set up a Snowflake user for DBT and Streamlit?

A: The article provides SQL queries that can be used to set up a Snowflake user for DBT and Streamlit. These queries create the required user, role, warehouse, database, and schema for the project.

Q: Where can I download the data set and data dictionary for the project?

A: The article provides the necessary links for downloading the NYC Yellow Taxi trip data set and the corresponding data dictionary. If you are using the provided Snowflake examples, the data files are already available within the DBT seeds and trips directories.

Q: How do I load the dimension and fact tables?

A: To load the dimension and fact tables, you need to copy the attribute names from the data set. Then, using the provided prompt, pass the attribute names to Chat GPT. The generated SQL code can be used to load the tables into the Snowflake environment.

Q: What are DBT models?

A: DBT models are SQL code that define how data should be transformed and loaded into a data warehouse. In the Context of this project, the DBT models are used to load the target Snowflake tables.

Q: How do I start the Streamlit app?

A: The article provides a Docker Compose file that sets up the necessary dependencies and environment for the Streamlit app. Additionally, you need to create a copy of the secrets.domel.template file, update it with the required information, and save it as secrets.normel. Finally, you can initiate the connection and start the Streamlit app using the Docker Compose file.

Note: The answers provided are a summary of the information covered in the article. Please refer to the respective sections in the article for detailed instructions.