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 ensuring that your CockroachDB instance is up and running. Identify the specific tables and data you want to export. Depending on your data needs, you may need to perform a full database export or target specific tables. Use SQL queries to filter and organize your data before exporting.
Use the `cockroach sql` command to export your data into CSV format. Run a SELECT query for the desired tables and redirect the output to a CSV file. Example command:
```bash
cockroach sql --insecure --host=your_host --database=your_database -e "COPY your_table TO STDOUT WITH CSV DELIMITER ','" > your_data.csv
```
Ensure you replace placeholders with your actual database information.
Set up an Amazon EC2 instance to act as a temporary staging area. Use SCP (Secure Copy Protocol) to transfer your CSV files from your local machine to the EC2 instance. Example SCP command:
```bash
scp -i your_key.pem your_data.csv ec2-user@your_instance_ip:/path/to/destination/
```
Replace the placeholders with your actual key file, instance IP, and destination path.
SSH into your EC2 instance and install the AWS Command Line Interface (CLI) if not already installed. Configure the AWS CLI with your credentials to ensure it has access to your AWS resources:
```bash
aws configure
```
Enter your AWS Access Key, Secret Access Key, region, and output format as prompted.
Use the AWS CLI to upload your CSV files from the EC2 instance to an Amazon S3 bucket. This serves as the intermediate storage for your data lake:
```bash
aws s3 cp /path/to/your_data.csv s3://your-bucket-name/path/in/bucket/
```
Ensure you replace placeholders with your actual file path, bucket name, and desired path within the S3 bucket.
In the AWS Management Console, navigate to AWS Glue. Create a new Glue Crawler that points to the S3 bucket where your CSV files are stored. Set the crawler to identify and catalog the data structure, making the data available for querying in AWS services like Athena.
After the Glue Crawler has completed, verify that the data is correctly cataloged in the AWS Glue Data Catalog. Use AWS Athena to run queries on the data to confirm its integrity and accessibility. This ensures that the data is now part of your AWS Data Lake and can be utilized for analytics and reporting.
By following these steps, you'll successfully transfer data from CockroachDB to AWS Data Lake 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:
