Top companies trust Airbyte to centralize their Data
Ship more quickly with the only solution that fits ALL your needs.
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
Leverage the largest catalog of connectors
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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Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
Get Airbyte hosted where you need it to be
- 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.
Jira is an issue tracking software by Atlassian that assists developers in bug tracking and agile project management. With software support throughout the entire development process, from planning to tracking, to the final release, and reports based on real-time data to improve team performance, Jira is the go-to software development tool for agile teams.
Typesense is an open-source, typo-tolerant search engine optimized for an instant (typically sub-50ms) search-like-up-type experience and developer productivity. If you've heard of Elasticsearch or Algolia, a good way to think about Typesense is that it's an open source alternative to Algolia, with some key issues fixed and an easy-to-use battery-powered alternative to Elasticsearch.It works like a CDN, but for Search. Deploy nodes around the world, closest to your users, to provide them an ultra-fast search experience.
Jira's API provides access to a wide range of data related to project management and issue tracking. The following are the categories of data that can be accessed through Jira's API:
1. Issues: This includes all the information related to the issues such as issue type, status, priority, description, comments, attachments, and more.
2. Projects: This includes information about the projects such as project name, description, project lead, and more.
3. Users: This includes information about the users such as user name, email address, and more.
4. Workflows: This includes information about the workflows such as workflow name, workflow steps, and more.
5. Custom fields: This includes information about the custom fields such as custom field name, type, and more.
6. Dashboards: This includes information about the dashboards such as dashboard name, description, and more.
7. Reports: This includes information about the reports such as report name, description, and more.
8. Agile boards: This includes information about the agile boards such as board name, board type, and more.
Overall, Jira's API provides access to a vast amount of data that can be used to improve project management and issue tracking.
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.
