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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
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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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- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
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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.
Todoist is a task management app that helps users organize and prioritize their to-do lists. It allows users to create tasks, set due dates and reminders, and categorize tasks into projects and sub-projects. The app also offers features such as labels, filters, and comments to help users stay on top of their tasks. Todoist can be accessed on multiple devices, including desktop and mobile, and can be integrated with other apps such as Google Calendar and Dropbox. With its simple and intuitive interface, Todoist is a popular choice for individuals and teams looking to increase productivity and manage their workload efficiently.
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.
Todoist's API provides access to a wide range of data related to tasks and projects. The following are the categories of data that can be accessed through Todoist's API:
1. Tasks: This includes all the tasks that are created in Todoist, including their due dates, priorities, labels, and comments.
2. Projects: This includes all the projects that are created in Todoist, including their names, colors, and parent projects.
3. Labels: This includes all the labels that are created in Todoist, including their names and colors.
4. Filters: This includes all the filters that are created in Todoist, including their names, queries, and colors.
5. Comments: This includes all the comments that are added to tasks in Todoist, including their content and authors.
6. Users: This includes all the users who have access to the Todoist account, including their names and email addresses.
7. Collaborators: This includes all the collaborators who have access to specific projects or tasks in Todoist, including their names and email addresses.
Overall, Todoist's API provides access to a comprehensive set of data that can be used to build powerful integrations and 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.
