Trusted by data-driven companies
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."
Before you begin, ensure that you have the Google Cloud SDK installed and configured on your local machine. This tool allows you to interact with Google Cloud services from the command line. Download and install it from the [Google Cloud SDK page](https://cloud.google.com/sdk/docs/install), and then initialize it by running `gcloud init` to set up authentication and configuration.
Log into your Google Cloud Console, navigate to BigQuery, and create a new dataset if you don�t already have one. Within this dataset, define a table where your data will be stored. Specify the schema for the table, which includes defining the fields and their data types that match the structure of your data.
Convert your iterable data into a format that BigQuery can accept, such as CSV or JSON. This can be done programmatically. For example, if you have a list of dictionaries in Python, you can convert it to a JSON Lines file. Each line in the file represents a record in JSON format.
Before you can load data into BigQuery, upload the prepared file to Google Cloud Storage (GCS), which acts as an intermediary storage. Use the `gsutil` command-line tool (included with the Google Cloud SDK) to upload your file. For example:
```bash
gsutil cp your_data_file.json gs://your-bucket-name/
```
Ensure that you have created a Google Cloud Storage bucket prior to this step.
Use the `bq` command-line tool to load data from GCS into your BigQuery table. You need to specify the dataset, table, and the path to your data file in GCS. Here is an example command:
```bash
bq load --source_format=NEWLINE_DELIMITED_JSON your_dataset.your_table gs://your-bucket-name/your_data_file.json
```
Adjust the `--source_format` flag based on the format of your data file.
After loading the data, confirm that the data has been transferred successfully by querying the table in the BigQuery web interface or using the `bq` command-line tool. You can run simple SQL queries to check if the records are correctly inserted.
To streamline future data uploads, consider writing a script that automates the entire process. This script should handle data preparation, upload to GCS, and loading into BigQuery. Use a language like Python or Bash and make use of the Google Cloud SDK command-line tools to execute each step programmatically.
By following these steps, you can efficiently transfer data from an iterable to BigQuery without relying on third-party connectors or integrations, leveraging only Google Cloud's native tools.
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.
Iterable is a marketing platform designed to help businesses grow. Its automated platform enables businesses to measure and optimize customer interactions, with the ability to easily create and execute cross-channel campaigns. Through in-app notifications, email, SMS, web and mobile push, and social media integrations, Iterable powers the entire customer engagement lifecycle, throughout all stages of the customer journey.
Iterable's API provides access to a wide range of data related to customer engagement and marketing campaigns. The following are the categories of data that can be accessed through Iterable's API:
1. User data: This includes information about individual users such as their email address, name, location, and other demographic information.
2. Campaign data: This includes information about marketing campaigns such as email campaigns, push notifications, and SMS campaigns. It includes data on the number of messages sent, open rates, click-through rates, and conversion rates.
3. Event data: This includes data on user behavior such as website visits, product purchases, and other actions taken by users.
4. List data: This includes information about the lists of users that have been created in Iterable, including the number of users in each list and their engagement history.
5. Template data: This includes information about the email templates and other marketing materials used in campaigns, including their design, content, and performance metrics.
6. Analytics data: This includes data on the performance of marketing campaigns, including metrics such as revenue generated, customer lifetime value, and return on investment.
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:
