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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).
- 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.
Pardot is a marketing automation platform that helps businesses streamline their marketing efforts and generate more leads. It offers a range of tools and features, including email marketing, lead scoring, lead nurturing, and analytics. With Pardot, businesses can create targeted campaigns that reach the right audience at the right time, and track their performance to optimize their marketing strategies. The platform also integrates with Salesforce, allowing businesses to seamlessly manage their sales and marketing efforts in one place. Overall, Pardot is designed to help businesses improve their marketing ROI and drive growth.
Microsoft SQL Server is a relational database management (RDBMS) built by Microsoft. As a database server, its primary function is to store and retrieve data upon the request of other software applications, either from the same computer or a different computer across a network—including the internet. To serve the needs of different audiences and workload sizes, Microsoft offers multiple editions (at least 12) of its Microsoft SQL Server.
Pardot's API provides access to a wide range of data related to marketing automation and lead management. The following are the categories of data that can be accessed through Pardot's API:
1. Prospects: Information about individual leads, including their contact details, activity history, and lead score.
2. Campaigns: Details about marketing campaigns, including their status, performance metrics, and associated assets.
3. Lists: Information about lists of prospects, including their size, membership criteria, and segmentation rules.
4. Emails: Details about email campaigns, including their content, delivery status, and engagement metrics.
5. Forms: Information about web forms used to capture lead data, including their design, submission data, and conversion rates.
6. Landing Pages: Details about landing pages used to drive lead generation, including their design, traffic sources, and conversion rates.
7. Tags: Information about tags used to categorize prospects, campaigns, and other marketing assets.
8. Users: Details about Pardot users, including their roles, permissions, and activity history.
9. Custom Objects: Information about custom objects created in Pardot, including their fields, records, and relationships with other objects.
Overall, Pardot's API provides a comprehensive set of data that can be used to optimize marketing campaigns, improve lead management, and drive business growth.
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.
