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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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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.
Klarna offers better shopping with direct payments, pay-later options, and installment plans in a smooth one-click purchase experience. Klarna is the leading global payment and shopping service, providing a smarter and more flexible shopping and purchasing experience to 150 million active customers at over 450,000 merchants in 45 countries. Klarna offers installment plans with direct payment, pay-after-delivery options, and a smooth one-click shopping experience that allows consumers to pay when and how they choose.
DuckDB is an in-process SQL OLAP database management system. It has strong support for SQL. DuckDB is borrowing the SQLite shell implementation. Each database is a single file on disk. It’s analogous to “ SQLite for analytical (OLAP) workloads” (direct comparison on the SQLite vs DuckDB paper here), whereas SQLite is for OLTP ones. But it can handle vast amounts of data locally. It’s the smaller, lighter version of Apache Druid and other OLAP technologies.
Klarna's API provides access to a wide range of data related to online payments and transactions. The following are the categories of data that can be accessed through Klarna's API:
1. Customer data: Klarna's API provides access to customer data such as name, email address, shipping address, and billing address.
2. Transaction data: The API provides information about transactions, including the amount, currency, and status of the transaction.
3. Order data: Klarna's API provides access to order data, including order number, order status, and order details.
4. Payment data: The API provides information about payment methods used, payment status, and payment details.
5. Fraud data: Klarna's API provides access to fraud data, including fraud risk scores and fraud prevention measures.
6. Refund data: The API provides information about refunds, including refund amount, refund status, and refund details.
7. Shipping data: Klarna's API provides access to shipping data, including shipping method, shipping status, and shipping details.
Overall, Klarna's API provides a comprehensive set of data that can be used to manage and analyze online payments and transactions.
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.
