Engineering Analytics

How to load data from MongoDB to Typesense

Learn how to use Airbyte to synchronize your MongoDB data into Typesense within minutes.


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 MongoDB as a source connector (using Auth, or usually an API key)
  2. set up Typesense 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 MongoDB

MongoDB is a popular open-source NoSQL database that stores data in a flexible, document-based format. It is designed to handle large volumes of unstructured data and is highly scalable, making it a popular choice for modern web applications. MongoDB uses a JSON-like format to store data, which allows for easy integration with web applications and APIs. It also supports dynamic queries, indexing, and aggregation, making it a powerful tool for data analysis. MongoDB is widely used in industries such as finance, healthcare, and e-commerce, and is known for its ease of use and flexibility.

What is Typesense

Typesense is an open-source, typo-tolerant search engine optimized for an instant (typically sub-50ms) search-like-up-type experience and developer productivity. If you've heard of Elasticsearch or Algolia, a good way to think about Typesense is that it's an open source alternative to Algolia, with some key issues fixed and an easy-to-use battery-powered alternative to Elasticsearch.It works like a CDN, but for Search. Deploy nodes around the world, closest to your users, to provide them an ultra-fast search experience.


  1. A MongoDB account to transfer your customer data automatically from.
  2. A Typesense 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 MongoDB and Typesense, for seamless data migration.

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

Step 1: Set up MongoDB as a source connector

1. First, you need to have a MongoDB instance running and accessible from the internet. You will also need to have the necessary credentials to access the database.

2. In the Airbyte dashboard, click on "Sources" and then click on "New Source."

3. Select "MongoDB" from the list of available sources.

4. In the "Connection Configuration" section, enter the following information:
- Host: The hostname or IP address of your MongoDB instance.
- Port: The port number on which your MongoDB instance is running.
- Username: The username you use to access your MongoDB instance.
- Password: The password you use to access your MongoDB instance.
- Authentication Database: The name of the database where your authentication credentials are stored.

5. Click on "Test Connection" to ensure that Airbyte can connect to your MongoDB instance.

6. If the connection is successful, click on "Save" to save your MongoDB source configuration.

7. You can now create a new pipeline and select your MongoDB source as the input. You can then configure the pipeline to transform and load your data into your desired destination.

Step 2: Set up Typesense as a destination connector

1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the Typesense destination connector and click on it.
4. You will be prompted to enter your Typesense API key. Enter this information and click "Test Connection" to ensure that the connection is successful.
5. If the connection is successful, click "Save" to save your Typesense destination connector settings.
6. Next, navigate to the "Sources" tab on the left-hand side of the screen and select the source that you want to connect to your Typesense destination.
7. Follow the prompts to enter the necessary information for your source connector, such as the API key or database credentials.
8. Once you have entered all of the necessary information, click "Test Connection" to ensure that the connection is successful.
9. If the connection is successful, click "Save" to save your source connector settings.
10. Finally, click on the "Sync" tab on the left-hand side of the screen and select the source and destination connectors that you want to use for your data sync.
11. Follow the prompts to set up your data sync, such as selecting the tables or data types that you want to sync.
12. Once you have completed all of the necessary steps, click "Start Sync" to begin syncing your data between your source and Typesense destination connectors.

Step 3: Set up a connection to sync your MongoDB data to Typesense

Once you've successfully connected MongoDB as a data source and Typesense 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 MongoDB from the dropdown list of your configured sources.
  3. Select your destination: Choose Typesense 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 MongoDB objects you want to import data from towards Typesense. 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 MongoDB to Typesense according to your settings.

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

Use Cases to transfer your MongoDB data to Typesense

Integrating data from MongoDB to Typesense provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

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

Frequently Asked Questions

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 data can you extract from MongoDB?

MongoDB gives access to a wide range of data types, including:

1. Documents: MongoDB stores data in the form of documents, which are similar to JSON objects. Each document contains a set of key-value pairs that represent the data.
2. Collections: A collection is a group of related documents that are stored together in MongoDB. Collections can be thought of as tables in a relational database.
3. Indexes: MongoDB supports various types of indexes, including single-field, compound, and geospatial indexes. Indexes are used to improve query performance.
4. GridFS: MongoDB's GridFS is a specification for storing and retrieving large files, such as images and videos, in MongoDB.
5. Aggregation: MongoDB's aggregation framework provides a way to perform complex data analysis operations, such as grouping, filtering, and sorting, on large datasets.
6. Transactions: MongoDB supports multi-document transactions, which allow multiple operations to be performed atomically.
7. Change streams: MongoDB's change streams provide a way to monitor changes to data in real-time, allowing applications to react to changes as they occur.

Overall, MongoDB provides access to a flexible and powerful data model that can handle a wide range of data types and use cases.

What data can you transfer to Typesense?

You can transfer a wide variety of data to Typesense. 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 MongoDB to Typesense?

The most prominent ETL tools to transfer data from MongoDB to Typesense include:

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

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

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.