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As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
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- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
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- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
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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.
Fauna merges the flexibility of NoSQL with the relational querying capabilities and ACID consistency of SQL systems. Fauna implements a semi-structured, schema-free, object-relational data model, strict superset of relational, document, object-oriented, and graph. Fauna is a tool in Databases category of tech stack. Inventory of fauna as a tool for sustainable use of economically important mammal species. This is used by animals is a phenomenon in which an animal uses any kind of tool to attain a goal such as acquiring food and water, grooming, defense.
Google Firestore is a cloud-based NoSQL document database that allows developers to store, sync, and query data for their web, mobile, and IoT applications. It is designed to provide real-time updates and offline support, making it ideal for applications that require fast and responsive data access. Firestore offers a flexible data model, allowing developers to store data in collections and documents, and supports complex queries and transactions. It also integrates with other Google Cloud services, such as Cloud Functions and Cloud Storage, to provide a complete backend solution for building scalable and reliable applications.
Fauna's API gives access to various types of data, including:
1. Documents: This includes JSON documents that can be stored, retrieved, and queried using Fauna's API.
2. Collections: Collections are groups of documents that share a common schema. They can be used to organize data and make it easier to query.
3. Indexes: Indexes are used to speed up queries by precomputing results. They can be created on any field in a collection.
4. Functions: Functions are reusable blocks of code that can be called from within queries. They can be used to perform complex calculations or manipulate data.
5. Roles: Roles are used to control access to data. They can be used to define permissions for different types of users or applications.
6. Keys: Keys are used to authenticate requests to Fauna's API. They can be used to control access to data and to track usage.
Overall, Fauna's API provides a flexible and powerful way to store, retrieve, and manipulate data. It can be used for a wide range of applications, from simple data storage to complex data analysis and processing.
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.
