

Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes



Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say

Andre Exner

"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."

Chase Zieman

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
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.
FAQs
What is ETL?
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.
What is ELT?
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.
Difference between ETL and ELT?
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: