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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
First, ensure you have access to your CockroachDB instance and have installed the CockroachDB client (`cockroach`). You can download the client from the CockroachDB website and configure it by setting up the proper connection parameters (host, port, user, database name) in your environment or using command-line options.
Use the `cockroach sql` command to export data from your CockroachDB database. You can do this by running SQL queries that output data in a format suitable for file storage, such as CSV. For example:
```shell
cockroach sql --execute="COPY (SELECT * FROM your_table) TO STDOUT WITH CSV HEADER" > data.csv
```
This command exports data from `your_table` to a CSV file named `data.csv`.
Install the AWS Command Line Interface (CLI) on your machine if you haven’t already. You can do this by following the instructions on the AWS CLI installation page. The AWS CLI allows you to interact with AWS services directly from your command line.
Configure your AWS CLI with your credentials to gain access to your AWS account. You can do this by running:
```shell
aws configure
```
Enter your AWS Access Key ID, Secret Access Key, region, and output format when prompted. This step is crucial to ensure you have the necessary permissions to upload files to your S3 bucket.
Ensure you have an S3 bucket to store your data. You can create a new S3 bucket using the AWS Management Console or via the AWS CLI:
```shell
aws s3 mb s3://your-bucket-name
```
Replace `your-bucket-name` with a unique name for your S3 bucket. Ensure that your bucket name adheres to AWS naming conventions.
Use the AWS CLI to upload your data file to the S3 bucket. Execute the following command:
```shell
aws s3 cp data.csv s3://your-bucket-name/
```
This command uploads the `data.csv` file to the specified S3 bucket. Ensure that the file path and bucket name are correct.
Finally, verify that your data has been successfully uploaded to S3. You can do this by listing the objects in your S3 bucket using:
```shell
aws s3 ls s3://your-bucket-name/
```
Check that `data.csv` appears in the list of objects. You can also verify via the AWS Management Console by navigating to your S3 bucket and checking for the presence of your file.
By following these steps, you can manually move data from CockroachDB to 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.
Self-proclaimed “The most highly evolved database on the planet,” Cockroachdb helps businesses “scale fast,” “survive anything,” and “thrive anywhere.” Cockroachdb makes it easy for businesses to scale their database quickly and automatically and can be used across multiple cloud platforms or hybridized across clouds and on-prem data centers. They service all sizes of brands, including major companies such as Bose, Comcast and Equifax, providing easy backup, multi-platform deployment, and secure and scalable data storage and retrieval.
CockroachDB gives access to a wide range of data types, including:
1. Structured data: This includes data that is organized into tables and columns, such as customer information, product details, and transaction records.
2. Unstructured data: This includes data that does not have a predefined structure, such as text documents, images, and videos.
3. Time-series data: This includes data that is collected over time and is typically used for analysis and forecasting, such as stock prices, weather data, and sensor readings.
4. Geospatial data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and address information.
5. Machine-generated data: This includes data that is generated by machines and devices, such as log files, system metrics, and IoT sensor data.
6. User-generated data: This includes data that is created by users, such as social media posts, comments, and reviews.
Overall, CockroachDB's API provides access to a wide range of data types, making it a versatile and powerful tool for developers and data analysts.
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:
