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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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- 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.
Pinterest Ads is a platform that allows businesses to promote their products and services to a highly engaged audience on Pinterest. With over 400 million monthly active users, Pinterest is a visual discovery engine that helps people find inspiration and ideas for their interests and hobbies. Pinterest Ads allows businesses to create and display ads in the form of Promoted Pins, Promoted Video Pins, and Promoted Carousel Pins. These ads can be targeted to specific audiences based on their interests, behaviors, and demographics. Pinterest Ads also provides analytics and insights to help businesses measure the performance of their ads and optimize their campaigns for better results.
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 webs, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.
Pinterest Ads API provides access to a wide range of data that can be used to optimize ad campaigns and improve targeting. The following are the categories of data that can be accessed through the Pinterest Ads API: 1. Ad performance data: This includes data on impressions, clicks, conversions, and other metrics related to ad performance.
2. Audience data: This includes data on the demographics, interests, and behaviors of the audience that engages with your ads.
3. Pin data: This includes data on the pins that users engage with, such as the type of content, the category, and the keywords associated with the pin.
4. Board data: This includes data on the boards that users engage with, such as the type of content, the category, and the keywords associated with the board.
5. Campaign data: This includes data on the campaigns that you run on Pinterest, such as the budget, targeting options, and ad formats.
6. Conversion data: This includes data on the actions that users take after clicking on your ads, such as purchases, sign-ups, and downloads.
Overall, the Pinterest Ads API provides a wealth of data that can be used to optimize ad campaigns and improve targeting, ultimately leading to better results and higher ROI.
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.
