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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.
Kustomer is an omnichannel SaaS CRM platform reimagining enterprise customer service to deliver standout experiences. It scales to meet the needs of any contact center and business by unifying data from multiple sources, enabling companies to deliver service and support through a single timeline view.
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.
Kustomer's API provides access to a wide range of data related to customer interactions and behavior. The following are the categories of data that can be accessed through Kustomer's API:
1. Customer data: This includes information about customers such as their name, email address, phone number, and other contact details.
2. Conversation data: This includes data related to customer conversations such as chat transcripts, email threads, and social media interactions.
3. Order data: This includes information about customer orders such as order number, order status, and order history.
4. Ticket data: This includes information about customer support tickets such as ticket number, ticket status, and ticket history.
5. Agent data: This includes information about agents such as their name, email address, and performance metrics.
6. Analytics data: This includes data related to customer behavior such as customer satisfaction scores, response times, and other key performance indicators.
Overall, Kustomer's API provides access to a comprehensive set of data that can be used to gain insights into customer behavior and improve customer support and engagement.
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.