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

Elasticsearch is a distributed search and analytics engine for all types of data. Elasticsearch is the central component of the ELK Stack (Elasticsearch, Logstash, and Kibana).

What is MongoDB

MongoDB is a database that powers crucial applications and systems for global businesses. Designed for developers and specializing in the areas of open source, software development, and databases, it offers functionality such as horizontal scaling, automatic failover, and the capability to assign data to a location.
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Prerequisites

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

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

Step 1: Set up Elasticsearch as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Create Connection" button and select "Elasticsearch" as the source.
3. Enter the required information such as the name of the connection and the Elasticsearch URL.
4. Provide the Elasticsearch credentials such as the username and password.
5. Specify the index or indices that you want to replicate.
6. Choose the replication mode, either full or incremental.
7. Set the replication schedule according to your needs.
8. Test the connection to ensure that the Elasticsearch source connector is working correctly.
9. Save the connection and start the replication process.

It is important to note that the Elasticsearch source connector on Airbyte.com requires a valid Elasticsearch URL and credentials to establish a connection. The connector also allows you to specify the index or indices that you want to replicate and choose the replication mode and schedule. Once the connection is established, Airbyte will replicate the data from Elasticsearch to your destination of choice.

Step 2: Set up MongoDB as a destination connector

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

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

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

Use Cases to transfer your Elasticsearch data to MongoDB

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

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

Now that you have a clear understanding of the use cases & the benefits of data transfer from Elasticsearch to MongoDB, you can head over to check out another amazing article about MongoDB Elasticsearch Data Sync.

Wrapping Up

To summarize, this tutorial has shown you how to:

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