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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.
Ashby uses a heavily-optimized infrastructure-as-a-service (IaaS) platform from Heroku and Amazon Web Services. Ashby is SOC2 compliant and Type 2 audited annually. Our SOC2 reports are available upon customer request. Ashby permits authentication from Google Workspace (formerly GSuite), Office 365 corporate accounts, Magic Links (sent via email), and SSO via SAML and OIDC. Ashby does not store any passwords. Ashby app is safe to use and requests are authentic with XSS and CSRF protection, signed and encrypted user authentication cookies, and session expiration.
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.
Ashby's API provides access to a wide range of data related to the UK property market. The data can be categorized into the following categories:
1. Property Listings: Ashby's API provides access to a comprehensive database of property listings across the UK. This includes details such as property type, location, price, and features.
2. Property Valuations: The API also provides access to property valuation data, which can be used to estimate the value of a property based on various factors such as location, size, and condition.
3. Market Trends: Ashby's API provides access to data on market trends, including information on property prices, rental yields, and demand for different types of properties.
4. Demographics: The API also provides access to demographic data, including information on population density, age distribution, and income levels in different areas.
5. Property Ownership: Ashby's API provides access to data on property ownership, including information on the number of properties owned by individuals and companies, as well as details on property transactions.
6. Planning Applications: The API also provides access to data on planning applications, including information on the number of applications submitted, approved, and rejected in different areas.
Overall, Ashby's API provides a wealth of data that can be used by property professionals, investors, and researchers to gain insights into the UK property market.
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.