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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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Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
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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.
Amazon S3 (Simple Storage Service) is a cloud-based object storage service provided by Amazon Web Services (AWS). It is designed to store and retrieve any amount of data from anywhere on the web. S3 is highly scalable, secure, and durable, making it an ideal solution for businesses of all sizes. S3 allows users to store and retrieve data in the form of objects, which can be up to 5 terabytes in size. These objects can be accessed through a web interface or through APIs, making it easy to integrate with other AWS services or third-party applications. S3 also offers a range of features, including versioning, lifecycle policies, and access control, which allow users to manage their data effectively. It also provides high availability and durability, ensuring that data is always accessible and protected against data loss. Overall, S3 is a powerful and flexible tool that enables businesses to store and manage their data in a secure and scalable way, making it an essential component of many cloud-based applications and services.
1. Customer information: Gainsight's API allows you to extract data related to customer information such as their name, email address, phone number, and other contact details.
2. Customer health score: You can extract data related to the health score of your customers, which is a metric that measures the overall health of your customer relationships.
3. Customer feedback: Gainsight's API allows you to extract data related to customer feedback, including survey responses, comments, and other feedback.
4. Customer usage data: You can extract data related to how your customers are using your product or service, including usage patterns, feature adoption, and other usage metrics.
5. Customer engagement data: Gainsight's API allows you to extract data related to customer engagement, including email opens, clicks, and other engagement metrics.
6. Customer support data: You can extract data related to customer support interactions, including tickets, chat logs, and other support-related metrics.
7. Customer revenue data: Gainsight's API allows you to extract data related to customer revenue, including subscription details, contract information, and other revenue-related metrics.
8. Customer churn data: You can extract data related to customer churn, including churn rates, reasons for churn, and other churn-related metrics.
9. Customer segmentation data: Gainsight's API allows you to extract data related to customer segmentation, including how customers are grouped based on various criteria such as industry, company size, and other segmentation metrics.
10. Customer success data: You can extract data related to customer success, including how successful your customers are in achieving their goals and how your product or service is helping them achieve those goals.
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.
