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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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Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
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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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Including for your Airbyte Open Source instance with our premium support.
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.
Coda is a comprehensive solution that combines documents, spreadsheets, and building tools into a single platform. With this tool, project managers can track OKRs while also brainstorming with their teams.
Elasticsearch is a powerful search and analytics engine that is designed to handle large amounts of data in real-time. It is an open-source, distributed, and scalable search engine that is built on top of the Apache Lucene search library. Elasticsearch is used to search, analyze, and visualize data in real-time, making it an ideal tool for businesses and organizations that need to process large amounts of data quickly. Elasticsearch is designed to be highly scalable and can be used to index and search data across multiple servers. It is also highly customizable, allowing users to configure it to meet their specific needs. Elasticsearch is commonly used for log analysis, full-text search, and business analytics. One of the key features of Elasticsearch is its ability to handle unstructured data, such as text, images, and videos. It uses a powerful search algorithm to analyze and index this data, making it easy to search and retrieve information quickly. Elasticsearch also supports a wide range of data formats, including JSON, CSV, and XML, making it easy to integrate with other data sources. Overall, Elasticsearch is a powerful tool that can help businesses and organizations to process and analyze large amounts of data quickly and efficiently.
Coda's API provides access to a wide range of data types, including:
1. Documents: Access to all the documents in a user's Coda account, including their metadata and content.
2. Tables: Access to the tables within a document, including their columns, rows, and cell values.
3. Rows: Access to individual rows within a table, including their cell values and metadata.
4. Columns: Access to individual columns within a table, including their cell values and metadata.
5. Formulas: Access to the formulas within a table, including their syntax and results.
6. Views: Access to the views within a table, including their filters, sorts, and groupings.
7. Users: Access to the users within a Coda account, including their metadata and permissions.
8. Groups: Access to the groups within a Coda account, including their metadata and membership.
9. Integrations: Access to the integrations within a Coda account, including their metadata and configuration.
10. Webhooks: Access to the webhooks within a Coda account, including their metadata and configuration.
Overall, Coda's API provides a comprehensive set of data types that developers can use to build powerful integrations and applications.
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.
