Top companies trust Airbyte to centralize their Data
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
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
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.
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.
LinkedIn Pages are a great platform for organizations to post industry updates, job opportunities, information about life at their organization, and much more. LinkedIn Pages can be used by admins and followers when signed in to LinkedIn.com on desktop and mobile devices. A LinkedIn Page permits you to represent your organization on LinkedIn. LinkedIn Pages offer a platform for companies, universities, and high schools to share information about their brand with visitors and followers. A LinkedIn Page assists.
Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.
LinkedIn Pages API provides access to a wide range of data related to LinkedIn Pages. The API allows developers to retrieve and manage data related to company pages, including company information, updates, and followers. Here are the categories of data that LinkedIn Pages API provides access to:
1. Company information: This includes basic information about the company, such as name, logo, description, and website URL.
2. Updates: This includes all the updates posted on the company page, including text, images, and videos.
3. Followers: This includes information about the followers of the company page, such as their names, job titles, and locations.
4. Analytics: This includes data related to the performance of the company page, such as engagement metrics, follower growth, and demographics.
5. Employee information: This includes information about the employees of the company, such as their names, job titles, and LinkedIn profiles.
6. Content recommendations: This includes recommendations for content that is likely to perform well on the company page based on LinkedIn's algorithm.
Overall, LinkedIn Pages API provides developers with a comprehensive set of data that can be used to build powerful applications and tools for managing LinkedIn Pages.
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.