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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
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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.
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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.
The Firebase Real-time Database allows you to build rich, collaborative applications by allowing secure access to the database directly from client-side code. The Firebase Real-time Database is a NoSQL database from which we can store and sync the data between our users in real-time. Firebase Real-time Database is a solution that stores data in the cloud and offers an easy way to sync your data among various devices, and it is a cloud-hosted database. Data is stored as JSON and synchronized in real-time to every connected client.
Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation.
Firebase's API gives access to a wide range of data types, including:
1. Real-time database: This includes data that is stored in real-time and can be accessed and updated in real-time.
2. Cloud Firestore: This is a NoSQL document database that stores data in documents and collections.
3. Authentication: This includes user data such as email, password, and authentication tokens.
4. Cloud Storage: This includes data such as images, videos, and other files that are stored in the cloud.
5. Cloud Functions: This includes data that is processed by serverless functions in the cloud.
6. Cloud Messaging: This includes data related to push notifications and messaging.
7. Analytics: This includes data related to user behavior and app usage.
8. Performance Monitoring: This includes data related to app performance and user experience.
9. Remote Config: This includes data related to app configuration and feature flags.
Overall, Firebase's API provides access to a wide range of data types that are essential for building modern web and mobile applications.
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.
