How to load data from Metabase to ElasticSearch

Learn how to use Airbyte to synchronize your Metabase data into ElasticSearch within minutes.

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Set up a Metabase connector in Airbyte

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

Set up ElasticSearch for your extracted Metabase data

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

Configure the Metabase to ElasticSearch 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 Metabase as a source connector (using Auth, or usually an API key)
set up ElasticSearch 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 Metabase

Metabase is accessible to all. Metabase is a self-service business intelligence software and it is a BI tool with a friendly UX and integrated tooling to let your company explore data on its own. Metabase is the easy, open-source way for everyone in your company to ask questions and learn from data. Metabase is an open-source business intelligence tool that lets you create charts and dashboards using data from a variety of databases and data sources. It generally assists users to create charts and dashboards from their databases.

What is ElasticSearch

Elasticsearch is a powerful search and analytics engine that is designed to handle large amounts of data in real-time. It is an open-source, distributed, and scalable search engine that is built on top of the Apache Lucene search library. Elasticsearch is used to search, analyze, and visualize data in real-time, making it an ideal tool for businesses and organizations that need to process large amounts of data quickly. Elasticsearch is designed to be highly scalable and can be used to index and search data across multiple servers. It is also highly customizable, allowing users to configure it to meet their specific needs. Elasticsearch is commonly used for log analysis, full-text search, and business analytics. One of the key features of Elasticsearch is its ability to handle unstructured data, such as text, images, and videos. It uses a powerful search algorithm to analyze and index this data, making it easy to search and retrieve information quickly. Elasticsearch also supports a wide range of data formats, including JSON, CSV, and XML, making it easy to integrate with other data sources. Overall, Elasticsearch is a powerful tool that can help businesses and organizations to process and analyze large amounts of data quickly and efficiently.

Integrate Metabase with ElasticSearch in minutes

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Prerequisites

A Metabase account to transfer your customer data automatically from.
A ElasticSearch 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 Metabase and ElasticSearch, for seamless data migration.

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

Step 1: Set up Metabase as a source connector

1. Open Metabase and navigate to the "Databases" tab.
2. Click on "Add Database" and select "Generic SQL" as the type.
3. In the "Connection Details" section, enter the following information: - Name: A name for your database connection - Host: The hostname or IP address of your Airbyte instance - Port: The port number used by your Airbyte instance (default is 8000) - Database: The name of the database you want to connect to - Username: Your Airbyte username - Password: Your Airbyte password
4. Click on "Save" to save your connection details.
5. Once your connection is saved, you can use it to create queries and visualizations in Metabase. Simply select your Airbyte connection from the list of available databases when creating a new query or visualization.

Step 2: Set up ElasticSearch 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 Elasticsearch destination connector and click on it.
4. You will be prompted to enter your Elasticsearch connection details, including the host URL, port number, and any authentication credentials.
5. Once you have entered your connection details, click on the "Test" button to ensure that your connection is working properly.
6. If the test is successful, click on the "Save" button to save your Elasticsearch destination connector settings.
7. You can now use this connector to send data from your Airbyte sources to your Elasticsearch database.
8. To set up a pipeline, navigate to the "Sources" tab and select the source you want to use.
9. Click on the "Create New Connection" button and select your Elasticsearch destination connector from the list.
10. Follow the prompts to map your source data to your Elasticsearch database fields and save your pipeline.

Step 3: Set up a connection to sync your Metabase data to ElasticSearch

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

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

Use Cases to transfer your Metabase data to ElasticSearch

Integrating data from Metabase to ElasticSearch provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Metabase account as an Airbyte data source connector.
Configure ElasticSearch as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Metabase to ElasticSearch 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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Sync with Airbyte

Step 1: Set up Metabase as a source connector

Step 2: Set up ElasticSearch as a destination connector

Step 3: Set up a connection to sync your Metabase data to ElasticSearch

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

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

How to Sync Metabase to ElasticSearch Manually

Step 1:

Step 2:

Step 3:

Step 4:

Step 5:

Step 6:

Step 7:

Step 8:

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 Metabase?

What data can you extract from Metabase?

Metabase's API provides access to a wide range of data types, including:

1. Metrics: These are numerical values that can be used to measure performance or track progress over time. Examples include revenue, website traffic, and customer satisfaction scores.
2. Dimensions: These are attributes that can be used to group or filter data. Examples include date, location, and product category.
3. Filters: These are criteria that can be used to limit the data returned by a query. Examples include date ranges, customer segments, and product types.
4. Joins: These are used to combine data from multiple tables or sources. Examples include joining customer data with sales data to analyze customer behavior.
5. Aggregations: These are used to summarize data by grouping it into categories and calculating metrics for each category. Examples include calculating average revenue per customer or total sales by product category.
6. Custom SQL: This allows users to write their own SQL queries to access and manipulate data in any way they choose.

Overall, Metabase's API provides a powerful tool for accessing and analyzing data from a wide range of sources, making it an ideal choice for businesses and organizations of all sizes.

How do I transfer data from Metabase?

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 Metabase to Elasticsearch 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 Metabase to Elasticsearch 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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Databases

Product Analytics

How to load data from Metabase to ElasticSearch

Learn how to use Airbyte to synchronize your Metabase data into ElasticSearch within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Metabase

What is ElasticSearch

Prerequisites

Step 1: Setup Metabase

Step 2: Setup ElasticSearch

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Metabase and ElasticSearch

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Metabase as a source connector (using Auth, or usually an API key)
set up ElasticSearch 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 Metabase

What is ElasticSearch

Integrate Metabase with ElasticSearch in minutes

Try for free now

Prerequisites

A Metabase account to transfer your customer data automatically from.
A ElasticSearch 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.

Step 1: Set up Metabase as a source connector

Step 2: Set up ElasticSearch as a destination connector

Step 3: Set up a connection to sync your Metabase data to ElasticSearch

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

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

Use Cases to transfer your Metabase data to ElasticSearch

Integrating data from Metabase to ElasticSearch provides several benefits. Here are a few use cases:

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