How to load data from Enterprise to Firebolt

Learn how to use Airbyte to synchronize your Enterprise data into Firebolt within minutes.

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Enterprise connector in Airbyte

Connect to Enterprise or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Firebolt for your extracted Enterprise data

Select Firebolt where you want to import data from your Enterprise source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Enterprise to Firebolt in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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How to Sync Enterprise to Firebolt Manually

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.

How to Sync Enterprise to Firebolt Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

Azure Blob Storage is a cloud-based storage solution provided by Microsoft Azure. It is designed to store large amounts of unstructured data such as text, images, videos, and audio files. Blob Storage is highly scalable and can store data of any size, from a few bytes to terabytes. It provides a cost-effective way to store and access data from anywhere in the world. Blob Storage also offers features such as data encryption, access control, and data redundancy to ensure data security and availability. It can be used for a variety of applications such as backup and disaster recovery, media storage, and data archiving.

Azure Blob Storage's API provides access to various types of data, including:
1. Unstructured data: This includes any type of data that does not have a predefined data model or structure, such as text, images, videos, and audio files.
2. Structured data: This includes data that has a predefined data model or structure, such as tables, columns, and rows.
3. Semi-structured data: This includes data that has some structure, but not enough to fit into a traditional relational database, such as JSON, XML, and CSV files.
4. Metadata: This includes information about the data stored in Azure Blob Storage, such as file size, creation date, and last modified date.
5. Access control data: This includes information about who has access to the data stored in Azure Blob Storage and what level of access they have.
6. Logging data: This includes information about the activities performed on the data stored in Azure Blob Storage, such as read and write operations, and access attempts.Overall, Azure Blob Storage's API provides access to a wide range of data types, making it a versatile and flexible storage solution for various types of applications and use cases.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Azure Blob storage to Firebolt as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Azure Blob storage to Firebolt and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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