Top companies trust Airbyte to centralize their Data
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Select your source
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Select your destination
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Sync your Data
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
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Cover your custom needs with our extensibility
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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
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Reliability at every level
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Airbyte Open Source
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Airbyte Cloud
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Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
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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.
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White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
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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.
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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.
TikTok for Business provides a rich analytics data source for companies seeking to understand consumer behavior and trends. With billions of daily video views and interactions, TikTok offers invaluable insights into audience preferences, content resonance, and engagement patterns. Businesses can leverage TikTok's built-in analytics tools to access granular data on video performance metrics, audience demographics, content categorizations, and more. This data can fuel advanced analytics initiatives, machine learning models, and data-driven decision-making processes. TikTok's APIs enable developers to integrate the platform's data with their existing analytics infrastructures, facilitating custom analyses and data blending with other sources.
TikTok for Business Marketing's API provides access to a wide range of data that can be used to optimize marketing campaigns and improve audience engagement. The types of data that can be accessed through the API can be categorized as follows:
1. User data: This includes information about TikTok users, such as their age, gender, location, interests, and behavior on the platform.
2. Content data: This includes information about the content that is being shared on TikTok, such as the number of views, likes, comments, and shares.
3. Ad performance data: This includes information about the performance of ads on TikTok, such as the number of impressions, clicks, and conversions.
4. Campaign data: This includes information about the performance of marketing campaigns on TikTok, such as the number of impressions, clicks, and conversions.
5. Trend data: This includes information about the latest trends on TikTok, such as popular hashtags, challenges, and music.
Overall, the TikTok for Business Marketing API provides a wealth of data that can be used to create more effective marketing campaigns and engage with audiences in a more meaningful way.
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.