Summarize
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."
Begin by exporting the dataset from BigQuery to Google Cloud Storage. Use the BigQuery console or a SQL query with the `EXPORT DATA` statement to export tables. Ensure the GCS bucket you export to is accessible and has the appropriate permissions set.
### 2. Set Up Google Cloud Storage to Amazon S3 Transfer
To move data from GCS to S3, you need to download the data from GCS and then upload it to S3. Use Google Cloud CLI (`gsutil`) to download the data from GCS to a local environment. Ensure you have `gsutil` installed and configured with the necessary credentials.
### 3. Install and Configure AWS CLI
Install the AWS Command Line Interface (CLI) on the same local machine where you have access to the downloaded files. Configure the AWS CLI with the necessary credentials to access your S3 bucket. Use `aws configure` to set up your access key, secret key, region, and output format.
### 4. Transfer Data from Local Environment to Amazon S3
Use the AWS CLI to upload the files from your local environment to your Amazon S3 bucket. Use the command `aws s3 cp` or `aws s3 sync` to ensure all files are transferred correctly. Ensure the S3 bucket has the appropriate permissions for the upload.
### 5. Set Up AWS Glue Environment
In the AWS Management Console, navigate to AWS Glue. Set up an AWS Glue job by creating a Glue ETL script or using the Glue Console. The job will read data from the S3 bucket and process it as needed. Define the data source as the S3 location where you uploaded your files.
### 6. Create an AWS Glue Crawler
Create a Glue Crawler to catalog the data stored in S3. This step is crucial for defining the schema and making the data queryable using AWS Glue. Run the crawler to populate the Glue Data Catalog with the metadata of the S3 data.
### 7. Execute the AWS Glue Job
Run the Glue job to process and transform the data as needed. Monitor the job execution via the AWS Glue console to ensure it completes successfully. The results can then be stored back in S3 or further processed as required.
By following these steps, you can effectively transfer and process data from BigQuery to Amazon S3 using AWS Glue, while leveraging in-built cloud services without third-party connectors.
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.
BigQuery is a cloud-based data warehousing and analytics platform that allows users to store, manage, and analyze large amounts of data in real-time. It is a fully managed service that eliminates the need for users to manage their own infrastructure, and it offers a range of features such as SQL querying, machine learning, and data visualization. BigQuery is designed to handle petabyte-scale datasets and can be used for a variety of use cases, including business intelligence, data exploration, and predictive analytics. It is a powerful tool for organizations looking to gain insights from their data and make data-driven decisions.
BigQuery provides access to a wide range of data types, including:
1. Structured data: This includes data that is organized into tables with defined columns and data types, such as CSV, JSON, and Avro files.
2. Semi-structured data: This includes data that has some structure, but not necessarily a fixed schema, such as XML and JSON files.
3. Unstructured data: This includes data that has no predefined structure, such as text, images, and videos.
4. Time-series data: This includes data that is organized by time, such as stock prices, weather data, and sensor readings.
5. Geospatial data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and spatial databases.
6. Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets and feature vectors.
7. Streaming data: This includes data that is generated in real-time, such as social media feeds, IoT sensor data, and log files.
Overall, BigQuery's API provides access to a wide range of data types, making it a powerful tool for data analysis and machine learning.
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:
