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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
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).
- 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.
Google Analytics is a web service that provides integrated analytical tools and essential statistics to aid companies in understanding data and performing search engine optimization (SEO) to improve marketing. Google Analytics provides a range of free marketing tools that help businesses track their website performance and gather insights from visitors. Analytics’ integration with other Google solutions like Google’s advertising and publishing products promotes a seamless workflow that saves time and increases efficiency and insights.
Typesense is an open-source, typo-tolerant search engine optimized for an instant (typically sub-50ms) search-like-up-type experience and developer productivity. If you've heard of Elasticsearch or Algolia, a good way to think about Typesense is that it's an open source alternative to Algolia, with some key issues fixed and an easy-to-use battery-powered alternative to Elasticsearch.It works like a CDN, but for Search. Deploy nodes around the world, closest to your users, to provide them an ultra-fast search experience.
Google Analytics API provides access to a wide range of data related to website traffic and user behavior. The following are the categories of data that can be accessed through the API:
1. Audience data: This includes information about the demographics, interests, and behavior of website visitors.
2. Acquisition data: This includes data related to how visitors are finding the website, such as through search engines, social media, or referral links.
3. Behavior data: This includes data related to how visitors are interacting with the website, such as which pages they are visiting, how long they are staying on the site, and which actions they are taking.
4. Conversion data: This includes data related to the goals and conversions on the website, such as the number of purchases, form submissions, or other desired actions.
5. E-commerce data: This includes data related to online sales, such as revenue, average order value, and product performance.
6. Real-time data: This includes data related to the current activity on the website, such as the number of active users and the pages they are currently viewing.
Overall, the Google Analytics API provides a wealth of data that can be used to gain insights into website performance and user behavior.
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.
