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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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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.
Talkdesk Explore is an leading business brilliance tool, supported with Talkdesk IQ and built with various customizable reports. Talkdesk provides a full portfolio of contact center automation tools as part of its and reporting builder. Talkdesk Explore is one kinds of reporting tool that permits you more performance and flexibility to manage your historical data. It is also a business analytics tool that features flexible filtering, scheduling, and customization options for a 360-degree view.
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables decoupling and scaling of microservices, distributed systems, and serverless applications. It allows applications to communicate asynchronously by sending and receiving messages between components. SQS provides a reliable, highly scalable, and durable messaging infrastructure that eliminates the need for developers to build and maintain their own messaging systems. It supports both standard and FIFO (first-in, first-out) queues, and integrates with other AWS services such as Lambda, EC2, and S3. SQS also provides features such as message encryption, dead-letter queues, and message filtering to enhance message delivery and processing.
TalkDesk Explore's API provides access to a wide range of data related to customer interactions and contact center performance. The following are the categories of data that can be accessed through the API:
1. Agent Performance: This category includes data related to the performance of individual agents, such as their call volume, average handle time, and customer satisfaction ratings.
2. Call Metrics: This category includes data related to the calls made and received by the contact center, such as call volume, call duration, and call outcome.
3. Customer Experience: This category includes data related to the customer experience, such as customer satisfaction ratings, customer feedback, and customer demographics.
4. Queue Metrics: This category includes data related to the performance of the contact center queues, such as queue volume, wait time, and abandonment rate.
5. Service Level: This category includes data related to the contact center's service level, such as the percentage of calls answered within a certain time frame.
6. Team Performance: This category includes data related to the performance of teams within the contact center, such as team call volume, team handle time, and team customer satisfaction ratings.
Overall, TalkDesk Explore's API provides a comprehensive set of data that can be used to analyze and optimize contact center performance and improve the customer experience.
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.
