Kafka integrations to save data teams 40 hours a week

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Start using Kafka integrations in three easy steps

Integrate Kafka connector in Airbyte

Choose a source connector to extract data

Connect Kafka as a source in Airbyte to start the data extraction process - without deep technical expertise.

Choose a source connector from 400+ integrations available on Airbyte to start the data extraction process - without deep technical expertise.

Send Kafka data anywhere you need it

Store your data inside Kafka destination

Choose from 50+ Airbyte destinations, including warehouses, databases, and lakes, to store and analyze your Kafka data.

Choose Kafka from 50+ Airbyte destinations, including warehouses, databases, and lakes, to store and analyze the data from the source connector.

Configure your Kafka data synchronization

Configure the integration for data synchronization

Select the Kafka streams you need and define your sync frequency. Airbyte lets you choose exactly which data to load and where it lands for full pipeline control.

Select the streams you need and define your sync frequency. Airbyte lets you choose exactly which data to load and where it lands for full pipeline control.

Kafka integrations let you do all these:

Sync Kafka data to BigQuery for advanced analytics

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Replicate Kafka data into PostgreSQL for structured querying

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Get insights by merging Kafka data with HubSpot

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Export Kafka data to Google Sheets for analysis

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Kafka integrations let you do all these:

Sync Google Analytics data to Kafka for analysis

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Load PostgreSQL data in to Kafka effortlessly

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Keep Notion data fresh in Kafka with automated syncs

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Manage Salesforce data in Kafka BigQuery for analytics

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All about Kafka integrations

What are Kafka integrations?

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Why choose Airbyte for Kafka data integration?

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What data can you extract from Airbyte’s Kafka integration?

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What data can you load to Kafka?

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How often does Airbyte sync my Kafka data?

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Do I need coding experience to use the Kafka integrations?

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About Kafka

Apache Kafka is an open-source distributed event streaming platform that is used to handle real-time data feeds. It is designed to handle high volumes of data and provide real-time processing and analysis of data streams. Kafka is used by many companies for various purposes such as data integration, real-time analytics, and messaging. It is highly scalable and fault-tolerant, making it a popular choice for large-scale data processing. Kafka provides a publish-subscribe model where producers publish data to topics, and consumers subscribe to those topics to receive the data. It also provides features such as data retention, replication, and partitioning to ensure data reliability and availability.