How to load data from Azure Blob Storage to Firebolt

Begin by ensuring you have the necessary access to your Azure Blob Storage account. This includes having the storage account name and either a shared access signature (SAS) token or the storage account key. This information will be used to authenticate and access the data in your blob storage.

Make sure you have Python installed on your local machine, as it will be used to script the data transfer. Additionally, install the Azure SDK for Python to interact with Azure Blob Storage and the Firebolt Python SDK for interacting with Firebolt. You can use pip to install these packages:
```bash
pip install azure-storage-blob firebolt-sdk
```

Create a Python script to download the data from Azure Blob Storage. Use the Azure SDK to list the blobs in the container and download them to a local temporary directory. Here's a basic example:
```python
from azure.storage.blob import BlobServiceClient
import os

connect_str = ""
container_name = ""
local_path = "./temp_data"

blob_service_client = BlobServiceClient.from_connection_string(connect_str)
container_client = blob_service_client.get_container_client(container_name)

os.makedirs(local_path, exist_ok=True)

blobs = container_client.list_blobs()
for blob in blobs:
blob_client = container_client.get_blob_client(blob)
download_file_path = os.path.join(local_path, blob.name)
with open(download_file_path, "wb") as download_file:
download_file.write(blob_client.download_blob().readall())
```

Ensure that the data downloaded is in a format that Firebolt can ingest, such as CSV or Parquet. If necessary, convert or transform the data locally using Python libraries like Pandas. This may involve cleaning the data or structuring it to match the schema of your Firebolt database.

Log into your Firebolt account and create a new database and table(s) to store the data. Use the Firebolt console or SQL commands to define the schema that matches your data requirements. Ensure that the tables are optimized for the type of queries you plan to run.

Use the Firebolt Python SDK to connect to your Firebolt database and upload the prepared data files. Here's a simple example of how to execute an upload:
```python
from firebolt.client import Client
from firebolt.db import connect

client = Client("", "")
conn = connect(client=client, database="")
cursor = conn.cursor()

# Assuming data is in CSV format
for file_name in os.listdir(local_path):
file_path = os.path.join(local_path, file_name)
with open(file_path, 'r') as file:
cursor.execute(f"COPY INTO FROM '{file_path}' FILE_FORMAT = (type = CSV)")
```

After uploading, verify that the data has been successfully transferred to Firebolt. Run a few sample queries to check the integrity and accuracy of the imported data. Compare counts and sample records with the original data in Azure Blob Storage to ensure consistency.

By following these steps, you can efficiently move data from Azure Blob Storage to Firebolt without relying on third-party connectors or integrations. Adjust the scripts as needed to fit your specific data structures and schemas.