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.
Slack is an enterprise software platform that facilitates global communication between all sizes of businesses and teams. Slack enables collaborative work to be more efficient and more productive, making it possible for businesses to connect with immediacy from half a world apart. It allows teams to work together in concert, almost as if they were in the same room. Slack transforms the process of communication, bringing it into the 21st century with powerful style.
Slack's API provides access to a wide range of data, including:
1. Conversations: This includes information about channels, direct messages, and group messages.
2. Users: This includes information about individual users, such as their name, email address, and profile picture.
3. Files: This includes information about files uploaded to Slack, such as their name, size, and type.
4. Apps: This includes information about the apps installed in Slack, such as their name, description, and permissions.
5. Messages: This includes information about individual messages, such as their text, timestamp, and author.
6. Events: This includes information about events that occur in Slack, such as when a user joins or leaves a channel.
7. Workflows: This includes information about workflows created in Slack, such as their name, description, and status.
8. Analytics: This includes information about how users are interacting with Slack, such as the number of messages sent and received, and the most active channels.
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.
Slack is an enterprise software platform that facilitates global communication between all sizes of businesses and teams. Slack enables collaborative work to be more efficient and more productive, making it possible for businesses to connect with immediacy from half a world apart. It allows teams to work together in concert, almost as if they were in the same room. Slack transforms the process of communication, bringing it into the 21st century with powerful style.
Slack's API provides access to a wide range of data, including:
1. Conversations: This includes information about channels, direct messages, and group messages.
2. Users: This includes information about individual users, such as their name, email address, and profile picture.
3. Files: This includes information about files uploaded to Slack, such as their name, size, and type.
4. Apps: This includes information about the apps installed in Slack, such as their name, description, and permissions.
5. Messages: This includes information about individual messages, such as their text, timestamp, and author.
6. Events: This includes information about events that occur in Slack, such as when a user joins or leaves a channel.
7. Workflows: This includes information about workflows created in Slack, such as their name, description, and status.
8. Analytics: This includes information about how users are interacting with Slack, such as the number of messages sent and received, and the most active channels.
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.
Slack is an enterprise software platform that facilitates global communication between all sizes of businesses and teams. Slack enables collaborative work to be more efficient and more productive, making it possible for businesses to connect with immediacy from half a world apart. It allows teams to work together in concert, almost as if they were in the same room. Slack transforms the process of communication, bringing it into the 21st century with powerful style.
Slack's API provides access to a wide range of data, including:
1. Conversations: This includes information about channels, direct messages, and group messages.
2. Users: This includes information about individual users, such as their name, email address, and profile picture.
3. Files: This includes information about files uploaded to Slack, such as their name, size, and type.
4. Apps: This includes information about the apps installed in Slack, such as their name, description, and permissions.
5. Messages: This includes information about individual messages, such as their text, timestamp, and author.
6. Events: This includes information about events that occur in Slack, such as when a user joins or leaves a channel.
7. Workflows: This includes information about workflows created in Slack, such as their name, description, and status.
8. Analytics: This includes information about how users are interacting with Slack, such as the number of messages sent and received, and the most active channels.
1. First, navigate to the Slack source connector page on Airbyte.com.
2. Click on the "Add Source" button to begin the process of adding your Slack credentials.
3. In the "Connection Configuration" section, enter a name for your Slack connection.
4. Next, enter your Slack workspace's API token in the "API Token" field. You can generate an API token by following the instructions in the Airbyte documentation.
5. In the "Channels" field, enter the names of the Slack channels you want to sync data from. You can enter multiple channels by separating them with commas.
6. If you want to filter the data that is synced from Slack, you can enter a date range in the "Start Date" and "End Date" fields.
7. Once you have entered all the necessary information, click on the "Test" button to ensure that your credentials are valid and that Airbyte can connect to your Slack workspace.
8. If the test is successful, click on the "Save & Continue" button to save your Slack connection.
9. You can now use your Slack source connector to sync data from your Slack workspace to your destination of choice.
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.
