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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
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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.
Mixpanel helps companies leverage metrics to make better decisions, faster. An analytic platform, Mixpanel enables companies to measure meaningful attributes and use the data to create better products/experiences. Mixpanel’s analytics solution enables teams to improve the website visitor experience by providing analytical data—in real time and across devices—on how (and why) visitors engage, convert, and retain.
A fully managed data warehouse service in the Amazon Web Services (AWS) cloud, Amazon Redshift is designed for storage and analysis of large-scale datasets. Redshift allows businesses to scale from a few hundred gigabytes to more than a petabyte (a million gigabytes), and utilizes ML techniques to analyze queries, offering businesses new insights from their data. Users can query and combine exabytes of data using standard SQL, and easily save their query results to their S3 data lake.
Mixpanel's API provides access to a wide range of data related to user behavior and engagement on digital platforms. The following are the categories of data that can be accessed through Mixpanel's API:
1. User data: This includes information about individual users such as their unique identifier, location, device type, and other demographic information.
2. Event data: This includes data related to specific actions taken by users on the platform, such as clicks, page views, and other interactions.
3. Funnel data: This includes data related to the steps users take to complete a specific action or goal on the platform, such as signing up for a service or making a purchase.
4. Retention data: This includes data related to how often users return to the platform and engage with it over time.
5. Revenue data: This includes data related to the financial performance of the platform, such as revenue generated from sales or advertising.
6. Custom data: This includes any additional data that has been collected and stored by the platform, such as user preferences or product usage data.
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.
