How to load data from Firebase Realtime Database to Kafka

Learn how to use Airbyte to synchronize your Firebase Realtime Database data into Kafka within minutes.

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Set up a Firebase Realtime Database connector in Airbyte

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

Set up Kafka for your extracted Firebase Realtime Database data

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

Configure the Firebase Realtime Database to Kafka 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

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 Firebase Realtime Database as a source connector (using Auth, or usually an API key)
  2. set up Kafka as a destination connector
  3. 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 Firebase Realtime Database

The Firebase Real-time Database allows you to build rich, collaborative applications by allowing secure access to the database directly from client-side code. The Firebase Real-time Database is a NoSQL database from which we can store and sync the data between our users in real-time. Firebase Real-time Database is a solution that stores data in the cloud and offers an easy way to sync your data among various devices, and it is a cloud-hosted database. Data is stored as JSON and synchronized in real-time to every connected client.

What is Kafka

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.

Integrate Firebase Realtime Database with Kafka in minutes

Try for free now

Prerequisites

  1. A Firebase Realtime Database account to transfer your customer data automatically from.
  2. A Kafka account.
  3. 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 Firebase Realtime Database and Kafka, for seamless data migration.

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

Step 1: Set up Firebase Realtime Database as a source connector

1. First, you need to create a Firebase project and obtain the necessary credentials. You can do this by going to the Firebase console and selecting your project. Then, navigate to the "Settings" tab and select "Service Accounts." From there, click on "Generate new private key" to download a JSON file containing your credentials.  
2. In Airbyte, navigate to the "Sources" tab and select "Add Source." Choose "Firebase" from the list of available sources.  
3. In the Firebase source configuration page, you will need to enter the following information:  - Name: A unique name for your source - Firebase Project ID: The ID of your Firebase project - Firebase Credentials: Copy and paste the contents of the JSON file you downloaded earlier into this field  
4. Once you have entered all the necessary information, click "Test Connection" to ensure that Airbyte can successfully connect to your Firebase source.  
5. If the connection is successful, you can then configure the specific tables or collections you want to replicate in Airbyte. You can do this by selecting the "Schema" tab and choosing the tables or collections you want to replicate. 6. Finally, click "Create Source" to save your configuration and start replicating data from your Firebase source.

Step 2: Set up Kafka as a destination connector

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.

Step 3: Set up a connection to sync your Firebase Realtime Database data to Kafka

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

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Firebase Realtime Database from the dropdown list of your configured sources.
  3. Select your destination: Choose Kafka from the dropdown list of your configured destinations.
  4. 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.
  5. Select the data to sync: Choose the specific Firebase Realtime Database objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
  6. 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.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Firebase Realtime Database to Kafka according to your settings.

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

Use Cases to transfer your Firebase Realtime Database data to Kafka

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

  1. Advanced Analytics: Kafka’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Firebase Realtime Database data, extracting insights that wouldn't be possible within Firebase Realtime Database alone.
  2. Data Consolidation: If you're using multiple other sources along with Firebase Realtime Database, syncing to Kafka 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.
  3. Historical Data Analysis: Firebase Realtime Database has limits on historical data. Syncing data to Kafka allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Kafka provides robust data security features. Syncing Firebase Realtime Database data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Kafka can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Firebase Realtime Database data.
  6. Data Science and Machine Learning: By having Firebase Realtime Database data in Kafka, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Firebase Realtime Database provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Kafka, providing more advanced business intelligence options. If you have a Firebase Realtime Database table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Firebase Realtime Database account as an Airbyte data source connector.
  2. Configure Kafka as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Firebase Realtime Database to Kafka 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!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Sync with Airbyte

1. First, you need to create a Firebase project and obtain the necessary credentials. You can do this by going to the Firebase console and selecting your project. Then, navigate to the "Settings" tab and select "Service Accounts." From there, click on "Generate new private key" to download a JSON file containing your credentials.  
2. In Airbyte, navigate to the "Sources" tab and select "Add Source." Choose "Firebase" from the list of available sources.  
3. In the Firebase source configuration page, you will need to enter the following information:  - Name: A unique name for your source - Firebase Project ID: The ID of your Firebase project - Firebase Credentials: Copy and paste the contents of the JSON file you downloaded earlier into this field  
4. Once you have entered all the necessary information, click "Test Connection" to ensure that Airbyte can successfully connect to your Firebase source.  
5. If the connection is successful, you can then configure the specific tables or collections you want to replicate in Airbyte. You can do this by selecting the "Schema" tab and choosing the tables or collections you want to replicate. 6. Finally, click "Create Source" to save your configuration and start replicating data from your Firebase source.

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.

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

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Firebase Realtime Database from the dropdown list of your configured sources.
  3. Select your destination: Choose Kafka from the dropdown list of your configured destinations.
  4. 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.
  5. Select the data to sync: Choose the specific Firebase Realtime Database objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
  6. 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.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Firebase Realtime Database to Kafka according to your settings.

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

How to Sync Firebase Realtime Database to Kafka Manually

FAQs

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.

The Firebase Real-time Database allows you to build rich, collaborative applications by allowing secure access to the database directly from client-side code. The Firebase Real-time Database is a NoSQL database from which we can store and sync the data between our users in real-time. Firebase Real-time Database is a solution that stores data in the cloud and offers an easy way to sync your data among various devices, and it is a cloud-hosted database. Data is stored as JSON and synchronized in real-time to every connected client.

Firebase's API gives access to a wide range of data types, including:  

1. Real-time database: This includes data that is stored in real-time and can be accessed and updated in real-time.  
2. Cloud Firestore: This is a NoSQL document database that stores data in documents and collections.  
3. Authentication: This includes user data such as email, password, and authentication tokens.  
4. Cloud Storage: This includes data such as images, videos, and other files that are stored in the cloud.  
5. Cloud Functions: This includes data that is processed by serverless functions in the cloud.  
6. Cloud Messaging: This includes data related to push notifications and messaging.  
7. Analytics: This includes data related to user behavior and app usage.  
8. Performance Monitoring: This includes data related to app performance and user experience.  
9. Remote Config: This includes data related to app configuration and feature flags.  

Overall, Firebase's API provides access to a wide range of data types that are essential for building modern web and mobile applications.

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

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.

Databases
Databases

How to load data from Firebase Realtime Database to Kafka

Learn how to use Airbyte to synchronize your Firebase Realtime Database data into Kafka within minutes.

TL;DR

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 Firebase Realtime Database as a source connector (using Auth, or usually an API key)
  2. set up Kafka as a destination connector
  3. 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 Firebase Realtime Database

The Firebase Real-time Database allows you to build rich, collaborative applications by allowing secure access to the database directly from client-side code. The Firebase Real-time Database is a NoSQL database from which we can store and sync the data between our users in real-time. Firebase Real-time Database is a solution that stores data in the cloud and offers an easy way to sync your data among various devices, and it is a cloud-hosted database. Data is stored as JSON and synchronized in real-time to every connected client.

What is Kafka

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.

Integrate Firebase Realtime Database with Kafka in minutes

Try for free now

Prerequisites

  1. A Firebase Realtime Database account to transfer your customer data automatically from.
  2. A Kafka account.
  3. 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 Firebase Realtime Database and Kafka, for seamless data migration.

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

Step 1: Set up Firebase Realtime Database as a source connector

1. First, you need to create a Firebase project and obtain the necessary credentials. You can do this by going to the Firebase console and selecting your project. Then, navigate to the "Settings" tab and select "Service Accounts." From there, click on "Generate new private key" to download a JSON file containing your credentials.  
2. In Airbyte, navigate to the "Sources" tab and select "Add Source." Choose "Firebase" from the list of available sources.  
3. In the Firebase source configuration page, you will need to enter the following information:  - Name: A unique name for your source - Firebase Project ID: The ID of your Firebase project - Firebase Credentials: Copy and paste the contents of the JSON file you downloaded earlier into this field  
4. Once you have entered all the necessary information, click "Test Connection" to ensure that Airbyte can successfully connect to your Firebase source.  
5. If the connection is successful, you can then configure the specific tables or collections you want to replicate in Airbyte. You can do this by selecting the "Schema" tab and choosing the tables or collections you want to replicate. 6. Finally, click "Create Source" to save your configuration and start replicating data from your Firebase source.

Step 2: Set up Kafka as a destination connector

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.

Step 3: Set up a connection to sync your Firebase Realtime Database data to Kafka

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

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Firebase Realtime Database from the dropdown list of your configured sources.
  3. Select your destination: Choose Kafka from the dropdown list of your configured destinations.
  4. 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.
  5. Select the data to sync: Choose the specific Firebase Realtime Database objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
  6. 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.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Firebase Realtime Database to Kafka according to your settings.

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

Use Cases to transfer your Firebase Realtime Database data to Kafka

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

  1. Advanced Analytics: Kafka’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Firebase Realtime Database data, extracting insights that wouldn't be possible within Firebase Realtime Database alone.
  2. Data Consolidation: If you're using multiple other sources along with Firebase Realtime Database, syncing to Kafka 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.
  3. Historical Data Analysis: Firebase Realtime Database has limits on historical data. Syncing data to Kafka allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Kafka provides robust data security features. Syncing Firebase Realtime Database data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Kafka can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Firebase Realtime Database data.
  6. Data Science and Machine Learning: By having Firebase Realtime Database data in Kafka, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Firebase Realtime Database provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Kafka, providing more advanced business intelligence options. If you have a Firebase Realtime Database table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Firebase Realtime Database account as an Airbyte data source connector.
  2. Configure Kafka as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Firebase Realtime Database to Kafka 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!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Frequently Asked Questions

What data can you extract from Firebase Realtime Database?

Firebase's API gives access to a wide range of data types, including:  

1. Real-time database: This includes data that is stored in real-time and can be accessed and updated in real-time.  
2. Cloud Firestore: This is a NoSQL document database that stores data in documents and collections.  
3. Authentication: This includes user data such as email, password, and authentication tokens.  
4. Cloud Storage: This includes data such as images, videos, and other files that are stored in the cloud.  
5. Cloud Functions: This includes data that is processed by serverless functions in the cloud.  
6. Cloud Messaging: This includes data related to push notifications and messaging.  
7. Analytics: This includes data related to user behavior and app usage.  
8. Performance Monitoring: This includes data related to app performance and user experience.  
9. Remote Config: This includes data related to app configuration and feature flags.  

Overall, Firebase's API provides access to a wide range of data types that are essential for building modern web and mobile applications.

What data can you transfer to Kafka?

You can transfer a wide variety of data to Kafka. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Firebase Realtime Database to Kafka?

The most prominent ETL tools to transfer data from Firebase Realtime Database to Kafka include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Firebase Realtime Database and various sources (APIs, databases, and more), transforming it efficiently, and loading it into Kafka and other databases, data warehouses and data lakes, enhancing data management capabilities.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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