Top companies trust Airbyte to centralize their Data
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Select your source
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Select your destination
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Sync your 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
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Cover your custom needs with our extensibility
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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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Reliability at every level
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Airbyte Open Source
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Airbyte Cloud
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Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
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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.
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White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
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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.
A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.
1. Data from various sources: Apache Spark's API allows you to extract data from various sources such as Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, and Amazon S3.
2. Structured and unstructured data: You can extract both structured and unstructured data using Apache Spark's API. Structured data can be extracted using Spark SQL, while unstructured data can be extracted using Spark Streaming.
3. Real-time data: Apache Spark's API allows you to extract real-time data using Spark Streaming. This feature is particularly useful for applications that require real-time data processing.
4. Machine learning data: Apache Spark's API provides support for machine learning algorithms. You can extract data for machine learning applications using Spark MLlib.
5. Graph data: Apache Spark's API provides support for graph processing. You can extract graph data using Spark GraphX.
6. Data transformation: Apache Spark's API allows you to transform data using various operations such as filtering, mapping, and reducing.
7. Data aggregation: You can extract aggregated data using Apache Spark's API. This feature is particularly useful for applications that require data summarization.
8. Data visualization: Apache Spark's API provides support for data visualization. You can extract data and visualize it using various tools such as Apache Zeppelin and Jupyter Notebook.
9. Data storage: Apache Spark's API allows you to store data in various formats such as Parquet, Avro, and ORC. You can extract data and store it in a format that is suitable for your application.
10. Data analysis: Apache Spark's API provides support for data analysis. You can extract data and perform various analysis operations such as statistical analysis, time series analysis, and predictive analysis.
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.