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 accessing your Metabase dashboard. Choose the dataset or the specific query result you wish to export. Use the Metabase export functionality to download the data in a CSV format. This can be done by clicking the download button on the query result page and selecting "CSV" as the file format. Save the file locally on your machine.
If you haven't already, set up the AWS Command Line Interface (CLI) on your machine. This tool will allow you to interact with your AWS services from the terminal. Download and install it from the official AWS website. Once installed, configure it by running `aws configure` and inputting your AWS Access Key ID, Secret Access Key, Default region name, and Default output format.
If you don't have an S3 bucket to store your data, you'll need to create one. Use the AWS Management Console to navigate to the S3 service. Click "Create bucket," enter a unique bucket name, select the region, and configure any additional settings as needed. Ensure that the bucket's permissions allow you to upload files.
With the AWS CLI configured and your CSV file ready, you can upload the file to your S3 bucket. Open your terminal and use the following command: ```
aws s3 cp /path/to/your/file.csv s3://your-bucket-name/
```
Replace `/path/to/your/file.csv` with the actual path to your CSV file and `your-bucket-name` with the name of your S3 bucket. This command will upload your CSV file to the specified bucket.
To ensure that your file has been successfully uploaded, you can list the contents of your S3 bucket using AWS CLI. Run the command:
```
aws s3 ls s3://your-bucket-name/
```
This will display a list of files in the bucket, allowing you to verify the presence of your uploaded CSV file.
Depending on your use case, you may need to adjust the permissions of the uploaded file. Use the AWS Management Console or the CLI to set the object permissions. For example, if you need the file to be publicly accessible, modify the bucket policy or the object ACL accordingly.
If this is a recurring task, consider automating the process using a script. Write a shell script that executes the data export from Metabase (if possible via API or command line), and then uses the AWS CLI to upload the file to S3. Schedule the script using a task scheduler like cron (on Unix-based systems) to run at desired intervals.
By following these steps, you can effectively move data from Metabase to Amazon S3 without relying on third-party connectors or integrations.
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.
Metabase is accessible to all. Metabase is a self-service business intelligence software and it is a BI tool with a friendly UX and integrated tooling to let your company explore data on its own. Metabase is the easy, open-source way for everyone in your company to ask questions and learn from data. Metabase is an open-source business intelligence tool that lets you create charts and dashboards using data from a variety of databases and data sources. It generally assists users to create charts and dashboards from their databases.
Metabase's API provides access to a wide range of data types, including:
1. Metrics: These are numerical values that can be used to measure performance or track progress over time. Examples include revenue, website traffic, and customer satisfaction scores.
2. Dimensions: These are attributes that can be used to group or filter data. Examples include date, location, and product category.
3. Filters: These are criteria that can be used to limit the data returned by a query. Examples include date ranges, customer segments, and product types.
4. Joins: These are used to combine data from multiple tables or sources. Examples include joining customer data with sales data to analyze customer behavior.
5. Aggregations: These are used to summarize data by grouping it into categories and calculating metrics for each category. Examples include calculating average revenue per customer or total sales by product category.
6. Custom SQL: This allows users to write their own SQL queries to access and manipulate data in any way they choose.
Overall, Metabase's API provides a powerful tool for accessing and analyzing data from a wide range of sources, making it an ideal choice for businesses and organizations of all sizes.
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:
