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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 400 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 no-code connector builder.
Configure the connection in Airbyte
The Airbyte Open Data Movement Platform
The only open solution empowering data teams to meet growing business demands in the new AI era.
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.
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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.
1. Real-time and historical pricing data for various cryptocurrencies, including Bitcoin, Ethereum, and Litecoin.
2. Market capitalization and trading volume data for each cryptocurrency.
3. Information on the top gainers and losers in the cryptocurrency market.
4. Data on the number of active addresses and transactions for each cryptocurrency.
5. News articles and social media sentiment analysis related to the cryptocurrency market.
6. Information on the top exchanges and trading pairs for each cryptocurrency.
7. Historical data on the performance of various cryptocurrencies over time.
8. Data on the supply and circulating supply of each cryptocurrency.
9. Information on the development activity and community engagement for each cryptocurrency.
10. Data on the correlation between different cryptocurrencies and traditional financial assets.
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.
1. Real-time and historical pricing data for various cryptocurrencies, including Bitcoin, Ethereum, and Litecoin.
2. Market capitalization and trading volume data for each cryptocurrency.
3. Information on the top gainers and losers in the cryptocurrency market.
4. Data on the number of active addresses and transactions for each cryptocurrency.
5. News articles and social media sentiment analysis related to the cryptocurrency market.
6. Information on the top exchanges and trading pairs for each cryptocurrency.
7. Historical data on the performance of various cryptocurrencies over time.
8. Data on the supply and circulating supply of each cryptocurrency.
9. Information on the development activity and community engagement for each cryptocurrency.
10. Data on the correlation between different cryptocurrencies and traditional financial assets.
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.
1. Real-time and historical pricing data for various cryptocurrencies, including Bitcoin, Ethereum, and Litecoin.
2. Market capitalization and trading volume data for each cryptocurrency.
3. Information on the top gainers and losers in the cryptocurrency market.
4. Data on the number of active addresses and transactions for each cryptocurrency.
5. News articles and social media sentiment analysis related to the cryptocurrency market.
6. Information on the top exchanges and trading pairs for each cryptocurrency.
7. Historical data on the performance of various cryptocurrencies over time.
8. Data on the supply and circulating supply of each cryptocurrency.
9. Information on the development activity and community engagement for each cryptocurrency.
10. Data on the correlation between different cryptocurrencies and traditional financial assets.
1. First, navigate to the Swell source connector page on Airbyte's website.
2. Click on the "Create a new connection" button.
3. Enter a name for your connection and click "Next".
4. Enter your Swell API credentials, including your API key and API secret.
5. Click "Test connection" to ensure that your credentials are correct and that Airbyte can connect to your Swell account.
6. Once the connection is successful, select the data you want to replicate from Swell.
7. Choose the frequency at which you want Airbyte to replicate your Swell data.
8. Click "Create connection" to finalize the setup process.
9. Your Swell data will now be replicated to your destination of choice through Airbyte's platform. It is important to note that Swell's API requires a paid subscription to access. Additionally, Airbyte offers a variety of destination connectors to choose from, allowing you to replicate your Swell data to a variety of different platforms.
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.