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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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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
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Keep your data engineering costs in check
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- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
- Airbyte Enterprise: Have it hosted within your own infrastructure, so your data and secrets never leave it.
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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.
GlassFrog is the official software to support and advance your Holacracy practice that is a cloud-based software that helps businesses implement, support, and manage Holacracy practice. GlassFrog makes Holacracy transparent and accessible, end-to-end. Glassfrog is the software that helps organizations using Holacracy record their structure, methodology and outcomes. GlassFrog is a vital piece of software for tactical meetings, plain and simple.
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.
Glassfrog's API provides access to a variety of data related to the management and organization of a company. The following are the categories of data that can be accessed through Glassfrog's API:
1. Circle data: This includes information about the circles within an organization, such as their names, purpose, and members.
2. Role data: This includes information about the roles within each circle, such as their names, purpose, and accountabilities.
3. Governance data: This includes information about the governance structure of the organization, such as the policies and procedures that govern decision-making.
4. Metrics data: This includes information about the performance metrics that are used to measure the success of the organization.
5. Meeting data: This includes information about the meetings that are held within the organization, such as their dates, times, and agendas.
6. User data: This includes information about the users who have access to the Glassfrog platform, such as their names, email addresses, and roles within the organization.
Overall, Glassfrog's API provides a comprehensive set of data that can be used to manage and optimize the performance of an organization.
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.
