Connectors

/

Kafka

Data replication

Connect Kafka with any data sources.

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.

  • Standard
  • Alpha
Load data into KafkaRead the docs
Kafka

One connector

Everything Kafka can do in Airbyte

  • Load from any source

    Move data into Kafka from 600+ Airbyte sources on a schedule you control.

    Load from any source

    Move data into Kafka from 600+ Airbyte sources on a schedule you control.

  • Incremental syncs

    Pull only the records that changed since the last run instead of reloading everything.

    Incremental syncs

    Pull only the records that changed since the last run instead of reloading everything.

  • Cloud or self-hosted

    Run the Kafka connector on Self-Managed Enterprise.

    Cloud or self-hosted

    Run the Kafka connector on Self-Managed Enterprise.

Specifications

What to know before you load into Kafka

  • Support levelStandard
  • Available onSelf-Managed Enterprise
  • Connector version0.1.11
  • Release stageAlpha

Sync capabilities

  • Full Refresh SyncNot supported
  • Incremental SyncSupported
  • Sources600+ Airbyte connectors

Set up in 9 steps

  1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.
  2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button.
  3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.
  4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.
  5. If the connection test is successful, click on the "Save" button to save the connection.
  6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.
  7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.
  8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.
  9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

Explore

Related connectors

Local SQLiteDatabasesStandard Destinations self-managed-enterprise
MS SQL ServerDatabasesCertified Destinations cloudself-managed-enterprise
MS SQL Server V2DatabasesStandard Destinations cloudself-managed-enterprise
MilvusDatabasesStandard Destinations cloudself-managed-enterprise
MongoDBDatabasesStandard Destinations cloudself-managed-enterprise
MySQL DestinationDatabasesStandard Destinations cloudself-managed-enterprise
All connectors

Common questions

Didn't find your answer?
Please don't hesitate to reach out.

Talk to sales

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.

Kafka provides access to a wide range of data types, including: Structured data (organized into tables with defined columns and data types, such as CSV, JSON, and Avro files); Semi-structured data (some structure, but not necessarily a fixed schema, such as XML and JSON files); Unstructured data (no predefined structure, such as text, images, and videos); Time-series data (organized by time, such as stock prices, weather data, and sensor readings); Geospatial data (related to geographic locations, such as maps, GPS coordinates, and spatial databases); Machine learning data (used to train machine learning models, such as labeled datasets and feature vectors); and Streaming data (generated in real-time, such as social media feeds, IoT sensor data, and log files). Overall, Kafka's API provides access to a wide range of data types, making it a powerful tool for data analysis and machine learning.

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button. 3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.
5. If the connection test is successful, click on the "Save" button to save the connection.
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

The most prominent ETL tools to transfer data to include: Airbyte, Fivetran, StitchData, Matillion, Talend Data Integration. These tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into and other databases, data warehouses and data lakes, enhancing data management capabilities.

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.

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.

Start moving Kafka data today

Free for 14 days on Airbyte Cloud. Set up the Kafka connector once and let Airbyte keep it in sync.

Try it freeTalk to sales