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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.
Gocardless is an online tool that collects direct bank payments on behalf of other businesses and It was founded in January 2011. GoCardless is an online Direct Debit supplier with a secure set-up process that permits the customer to pay both easily and safely. We ask all our customers to sign up to gain a streamlined payment procedure whereby the amount is automatically debited from the account provided every month. GoCardless is aims at becoming the world's bank payment network.
Firebolt is a high-performance cloud-native data warehouse platform designed for massive-scale data analytics. It enables organizations to harness the power of big data with lightning-fast query speeds and unlimited scalability. Firebolt.io utilizes a unique indexing technology and a highly parallelized architecture to optimize data processing and reduce query latency. With its cloud-native approach, users can easily integrate and analyze diverse data sources while benefiting from automatic scalability and cost optimization. Firebolt.io empowers businesses to derive actionable insights from their data at unprecedented speed and efficiency, accelerating data-driven decision-making and unlocking the full potential of big data analytics.
GoCardless's API provides access to a wide range of data related to payments and customers. The following are the categories of data that can be accessed through the API:
1. Payment data: This includes information about payments made by customers, such as the amount, currency, status, and date of payment.
2. Customer data: This includes information about customers, such as their name, email address, phone number, and billing address.
3. Subscription data: This includes information about subscriptions, such as the amount, frequency, and start and end dates.
4. Mandate data: This includes information about mandates, which are the authorizations given by customers to allow GoCardless to collect payments from their bank accounts.
5. Bank account data: This includes information about the bank accounts used by customers to make payments, such as the account number, sort code, and bank name.
6. Refund data: This includes information about refunds issued to customers, such as the amount, currency, and date of refund.
7. Dispute data: This includes information about disputes raised by customers, such as the reason for the dispute and the status of the dispute resolution process.
Overall, GoCardless's API provides comprehensive access to data related to payments and customers, enabling businesses to manage their payment processes more efficiently and effectively.
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.
