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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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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.
White-glove enterprise-level support
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.
Advertised as the “First and only revenue acceleration platform,” Drift provides an array of conversational tools in one place. Live chat, email, video, virtual selling assistants, Drift intel and prospector, and more are all smoothly integrated for a seamless and frictionless communication experience. Putting the personal touch back in marketing, Drift’s Conversational Marketing and Conversational Sales helps companies personalize business/client encounters and grow revenue faster.
Redis is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. It supports a wide range of data structures such as strings, hashes, lists, sets, and sorted sets. Redis is known for its high performance, scalability, and flexibility. It can handle millions of requests per second and can be used in a variety of applications such as real-time analytics, messaging, and session management. Redis also provides advanced features such as pub/sub messaging, Lua scripting, and transactions. It is widely used by companies such as Twitter, GitHub, and StackOverflow.
Drift's API provides access to a wide range of data related to customer interactions and conversations. The following are the categories of data that can be accessed through Drift's API:
1. Conversations: This includes data related to all conversations between customers and agents, including conversation history, transcripts, and metadata.
2. Contacts: This includes data related to customer profiles, such as contact information, company details, and activity history.
3. Events: This includes data related to customer behavior, such as page views, clicks, and other actions taken on the website.
4. Campaigns: This includes data related to marketing campaigns, such as email campaigns, chat campaigns, and other promotional activities.
5. Integrations: This includes data related to third-party integrations, such as CRM systems, marketing automation tools, and other business applications.
6. Analytics: This includes data related to performance metrics, such as conversion rates, engagement rates, and other key performance indicators.
Overall, Drift's API provides a comprehensive set of data that can be used to gain insights into customer behavior, improve customer engagement, and optimize business processes.
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.
