Top companies trust Airbyte to centralize their Data
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Set up a source connector to extract data from in Airbyte
Choose from one of 300+ sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code connector builder.
Configure the connection in Airbyte
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
Airbyte Open Source
Airbyte Cloud
Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
Get Airbyte hosted where you need it to be
- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
- Airbyte Enterprise: Have it hosted within your own infrastructure, so your data and secrets never leave it.
White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
Fnatic, based out of London, is the world's leading esports organization, with a winning legacy of 16 years and counting in over 28 different titles, generating over 13m USD in prize money. Fnatic has an engaged follower base of 14m across their social media platforms and hundreds of millions of people watch their teams compete in League of Legends, CS:GO, Dota 2, Rainbow Six Siege, and many more titles every year.
Ready to get started?
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 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 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.
1. First, you need to create a new CockroachDB source connector in Airbyte. To do this, click on the "Sources" tab on the left-hand side of the dashboard and then click on the "New Source" button.
2. In the list of available sources, select "CockroachDB" and click on the "Create" button.
3. You will be prompted to enter your CockroachDB credentials. Enter your database name, host, port, username, and password.
4. Once you have entered your credentials, click on the "Test" button to ensure that Airbyte can connect to your CockroachDB database.
5. If the test is successful, click on the "Save" button to save your credentials.
6. You can now configure your CockroachDB source connector by selecting the tables you want to replicate and setting up any necessary transformations.
7. Once you have configured your source connector, you can run a sync to start replicating data from your CockroachDB database to your destination.
8. You can monitor the progress of your sync in the "Jobs" tab on the left-hand side of the dashboard.
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.
