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
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
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
