How to load data from Kafka to Weaviate

Learn how to use Airbyte to synchronize your Kafka data into Weaviate within minutes.

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Set up a Kafka connector in Airbyte

Connect to Kafka or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Weaviate for your extracted Kafka data

Select Weaviate where you want to import data from your Kafka source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Kafka to Weaviate in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Kafka as a source connector (using Auth, or usually an API key)
set up Weaviate as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is 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.

What is Weaviate

Weaviate is an open-source, cloud-native, real-time vector search engine that allows developers to build intelligent applications with natural language processing (NLP) capabilities. It uses machine learning algorithms to understand the meaning of unstructured data and provides a semantic search engine that can retrieve relevant information from large datasets. Weaviate can be used to build chatbots, recommendation systems, and other intelligent applications that require NLP capabilities. It is designed to be scalable, flexible, and easy to use, with a RESTful API that allows developers to integrate it into their applications quickly. Weaviate is built on top of Kubernetes and can be deployed on-premises or in the cloud.

Integrate Kafka with Weaviate in minutes

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Prerequisites

A Kafka account to transfer your customer data automatically from.
A Weaviate account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Kafka and Weaviate, for seamless data migration.

When using Airbyte to move data from Kafka to Weaviate, it extracts data from Kafka using the source connector, converts it into a format Weaviate can ingest using the provided schema, and then loads it into Weaviate via the destination connector. This allows businesses to leverage their Kafka data for advanced analytics and insights within Weaviate, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Kafka as a source connector

1. First, you need to have a Kafka source connector that you want to connect to Airbyte. You can download the connector from the Apache Kafka website or any other reliable source.

2. Once you have the Kafka source connector, you need to configure it with the necessary settings such as the Kafka broker URL, topic name, and other relevant parameters.

3. Next, you need to create a new connection in Airbyte by clicking on the ""New Connection"" button on the dashboard.

4. Select the Kafka source connector from the list of available connectors and provide the necessary details such as the connector name, version, and configuration settings.

5. After providing the required details, click on the ""Test Connection"" button to ensure that the connection is established successfully.

6. If the connection is successful, you can proceed to create a new pipeline by clicking on the ""New Pipeline"" button on the dashboard.

7. Select the Kafka source connector as the source and choose the destination connector where you want to send the data.

8. Configure the pipeline settings such as the data mapping, transformation, and other relevant parameters.

9. Once you have configured the pipeline, click on the ""Run"" button to start the data transfer process.

10. Monitor the pipeline progress and ensure that the data is transferred successfully from the Kafka source connector to the destination connector.

Step 2: Set up Weaviate as a destination connector

1. First, navigate to the Weaviate destination connector on Airbyte's website.
2. Click on the "Get Started" button to begin the setup process.
3. Enter the required credentials for your Weaviate instance, including the URL, API key, and schema name.
4. Test the connection to ensure that the credentials are correct and the connection is successful.
5. Choose the tables or collections that you want to sync from your source connector to Weaviate.
6. Map the fields from your source connector to the corresponding fields in Weaviate.
7. Set up any necessary transformations or filters to ensure that the data is formatted correctly for Weaviate.
8. Schedule the sync to run at regular intervals or manually trigger it as needed.
9. Monitor the sync to ensure that the data is being transferred correctly and troubleshoot any issues that arise.
10. Once the sync is complete, verify that the data has been successfully transferred to Weaviate.

Step 3: Set up a connection to sync your Kafka data to Weaviate

Once you've successfully connected Kafka as a data source and Weaviate as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Kafka from the dropdown list of your configured sources.
Select your destination: Choose Weaviate from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Kafka objects you want to import data from towards Weaviate. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Kafka to Weaviate according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Weaviate data warehouse is always up-to-date with your Kafka data.

Use Cases to transfer your Kafka data to Weaviate

Integrating data from Kafka to Weaviate provides several benefits. Here are a few use cases:

Advanced Analytics: Weaviate’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Kafka data, extracting insights that wouldn't be possible within Kafka alone.
Data Consolidation: If you're using multiple other sources along with Kafka, syncing to Weaviate allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Kafka has limits on historical data. Syncing data to Weaviate allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Weaviate provides robust data security features. Syncing Kafka data to Weaviate ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Weaviate can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Kafka data.
Data Science and Machine Learning: By having Kafka data in Weaviate, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Kafka provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Weaviate, providing more advanced business intelligence options. If you have a Kafka table that needs to be converted to a Weaviate table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Kafka account as an Airbyte data source connector.
Configure Weaviate as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Kafka to Weaviate after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Kafka to Weaviate Manually

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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.

What is Kafka?

What data can you extract from Kafka?

Kafka's API gives access to various types of data, including:

1. Event data: Kafka is primarily used for streaming event data, such as user actions, sensor readings, and log data.

2. Metadata: Kafka provides metadata about the topics, partitions, and brokers in a cluster.

3. Consumer offsets: Kafka tracks the offset of each message consumed by a consumer, allowing for reliable message delivery.

4. Producer metrics: Kafka provides metrics on the performance of producers, such as message send rate and error rate.

5. Consumer metrics: Kafka provides metrics on the performance of consumers, such as message consumption rate and lag.

6. Log data: Kafka stores log data for a configurable amount of time, allowing for historical analysis and debugging.

7. Administrative data: Kafka provides APIs for managing topics, partitions, and consumer groups.

Overall, Kafka's API gives access to a wide range of data related to event streaming, metadata, performance metrics, and administrative tasks.

How do I transfer data from Kafka?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Kafka to Weaviate as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Kafka to Weaviate and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

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Engineering Analytics

How to load data from Kafka to Weaviate

Learn how to use Airbyte to synchronize your Kafka data into Weaviate within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 2: Setup Weaviate

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Kafka and Weaviate

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Kafka as a source connector (using Auth, or usually an API key)
set up Weaviate as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Kafka