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- Automated schema change handling, data normalization and more
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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.
Genesys is a cloud-based customer experience platform that helps businesses improve their customer interactions across all channels, including voice, email, chat, and social media. The platform provides a range of tools and features, including intelligent routing, self-service options, and real-time analytics, to help businesses deliver personalized and efficient customer experiences. Genesys also offers integrations with popular CRM and marketing automation systems, as well as AI-powered chatbots and virtual assistants to automate routine tasks and improve customer engagement. With Genesys, businesses can streamline their customer service operations, reduce costs, and increase customer satisfaction.
An object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many web, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.
Genesys's API provides access to a wide range of data related to customer interactions and contact center operations. The following are the categories of data that can be accessed through Genesys's API:
1. Customer data: This includes information about customers such as their name, contact details, and previous interactions with the contact center.
2. Interaction data: This includes data related to customer interactions such as call recordings, chat transcripts, and email conversations.
3. Agent data: This includes information about agents such as their availability, skills, and performance metrics.
4. Queue data: This includes data related to the queues in the contact center such as the number of calls waiting, average wait time, and service level.
5. Routing data: This includes data related to the routing of interactions such as the routing strategy, routing rules, and routing statistics.
6. Reporting data: This includes data related to contact center performance such as call volume, handle time, and customer satisfaction scores.
7. Configuration data: This includes data related to the configuration of the contact center such as the IVR menu, agent groups, and business hours.
Overall, Genesys's API provides access to a comprehensive set of data that can be used to improve customer experience, optimize contact center operations, and drive business outcomes.
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.
