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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.
MariaDB Columnstore is a powerful tool designed for big data analytics and business intelligence. It is a columnar storage engine that allows users to store and analyze large amounts of data in real-time. The tool is built on top of the MariaDB database management system and is designed to handle complex queries and data processing tasks. MariaDB Columnstore is designed to provide high performance and scalability, making it ideal for organizations that need to process large amounts of data quickly. It is also highly flexible, allowing users to customize the tool to meet their specific needs. One of the key features of MariaDB Columnstore is its ability to handle both structured and unstructured data. This means that users can analyze data from a wide range of sources, including social media, web logs, and other unstructured data sources. Overall, MariaDB Columnstore is a powerful tool that can help organizations make better decisions by providing them with the insights they need to succeed. Whether you are looking to analyze customer data, track sales trends, or monitor website traffic, MariaDB Columnstore can help you get the job done quickly and efficiently.
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.