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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.
Specializing in the development and maintenance of Android, iOS, and Web applications, DB2’s AI technology offers fast insights, flexible data management, and secure data movement to businesses globally through its IBM Cloud Pak for Data platform. Companies rely on DB2’s AI-powered insights and secure platform and save money with its multimodal capability, which eliminates the need for unnecessary replication and migration of data. Additionally, DB2 is convenient and will run on any cloud vendor.
Starburst Data is a data access and analytics company that offers a cloud-native, SQL-based query engine called Presto. Their mission is to enable organizations to access and analyze data across various sources efficiently and at scale. Starburst Data provides an enterprise-grade platform that leverages the power of Presto to query data residing in different databases, data lakes, and cloud storage systems, eliminating data silos and accelerating insights. With a focus on performance, security, and ease of use, Starburst Data empowers businesses to unlock the value of their data, enabling faster decision-making and advanced analytics capabilities.
IBM Db2 provides access to a wide range of data types, including:
1. Relational data: This includes tables, views, and indexes that are organized in a relational database management system (RDBMS).
2. Non-relational data: This includes data that is not organized in a traditional RDBMS, such as NoSQL databases, JSON documents, and XML files.
3. Time-series data: This includes data that is collected over time and is typically used for analysis and forecasting, such as sensor data, financial data, and weather data.
4. Geospatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
5. Graph data: This includes data that is organized in a graph structure, such as social networks, recommendation engines, and knowledge graphs.
6. Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets, feature vectors, and model parameters.
Overall, IBM Db2's API provides access to a diverse range of data types, making it a powerful tool for data management and analysis.
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.