How to load data from Monday to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Monday data into Databricks Lakehouse within minutes.

Trusted by data-driven companies

Building your pipeline or Using Airbyte

Airbyte is the only open solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Monday connector in Airbyte

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

Set up Databricks Lakehouse for your extracted Monday data

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

Configure the Monday to Databricks Lakehouse 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.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Old Automated Content

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 Monday as a source connector (using Auth, or usually an API key)
  2. set up Databricks Lakehouse 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 Monday

Monday is the first day of the week in most countries and is typically associated with the start of a new work or school week. It is often viewed as a day of productivity and setting goals for the week ahead. Many people may feel a sense of dread or stress on Mondays, commonly referred to as the "Monday blues." However, others may view it as an opportunity to start fresh and tackle new challenges. Some cultures also have specific traditions or superstitions associated with Mondays, such as avoiding certain activities or wearing specific colors. Overall, Monday represents a new beginning and a chance to make the most of the week ahead.

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate Monday with Databricks Lakehouse in minutes

Try for free now

Prerequisites

  1. A Monday account to transfer your customer data automatically from.
  2. A Databricks Lakehouse 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 Monday and Databricks Lakehouse, for seamless data migration.

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

Step 1: Set up Monday as a source connector

1. First, navigate to the Airbyte website and create an account.
2. Once you have logged in, click on the ""Sources"" tab on the left-hand side of the screen.
3. Scroll down until you find the Monday source connector and click on it.
4. You will be prompted to enter your Monday API key. If you do not have one, you can generate one by following the instructions provided.
5. Once you have entered your API key, click on the ""Test"" button to ensure that the connection is successful.
6. If the test is successful, click on the ""Save & Continue"" button to proceed.
7. You will then be prompted to select the data you want to sync from Monday. You can choose from a variety of options, including boards, tasks, and users.
8. Once you have selected the data you want to sync, click on the ""Save & Continue"" button to proceed.
9. You will then be prompted to set up your destination connector. This will depend on where you want to sync your data to.
10. Follow the instructions provided to set up your destination connector and complete the syncing process.

Step 2: Set up Databricks Lakehouse 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 "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.

Step 3: Set up a connection to sync your Monday data to Databricks Lakehouse

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

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

Use Cases to transfer your Monday data to Databricks Lakehouse

Integrating data from Monday to Databricks Lakehouse provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Monday account as an Airbyte data source connector.
  2. Configure Databricks Lakehouse as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Monday to Databricks Lakehouse 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 sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that supports both incremental and full refreshes, for databases of any size.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Jean-Mathieu Saponaro
Data & Analytics Senior Eng Manager

"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"

Learn more
Chase Zieman headshot
Chase Zieman
Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Learn more
Alexis Weill
Data Lead

“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria.
The value of being able to scale and execute at a high level by maximizing resources is immense”

Learn more

Sync with Airbyte

How to Sync Monday to Databricks Lakehouse 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.

Monday is the first day of the week in most countries and is typically associated with the start of a new work or school week. It is often viewed as a day of productivity and setting goals for the week ahead. Many people may feel a sense of dread or stress on Mondays, commonly referred to as the "Monday blues." However, others may view it as an opportunity to start fresh and tackle new challenges. Some cultures also have specific traditions or superstitions associated with Mondays, such as avoiding certain activities or wearing specific colors. Overall, Monday represents a new beginning and a chance to make the most of the week ahead.

Monday's API provides access to a wide range of data related to project management and team collaboration. The following are the categories of data that can be accessed through Monday's API:

1. Boards: This category includes data related to the boards created in Monday, such as board name, description, and status.

2. Items: This category includes data related to the items created within a board, such as item name, description, and status.

3. Users: This category includes data related to the users who have access to a board, such as user name, email address, and role.

4. Groups: This category includes data related to the groups created within a board, such as group name, description, and members.

5. Columns: This category includes data related to the columns created within a board, such as column name, type, and settings.

6. Updates: This category includes data related to the updates made to a board or item, such as update text, creator, and timestamp.

7. Notifications: This category includes data related to the notifications sent to users, such as notification type, recipient, and timestamp.

Overall, Monday's API provides access to a comprehensive set of data that can be used to build custom integrations and applications to enhance project management and team collaboration.

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 Monday to Databricks Lakehouse 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 Monday to Databricks Lakehouse 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.

Warehouses and Lakes
Finance & Ops Analytics

How to load data from Monday to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Monday data into Databricks Lakehouse 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 Monday as a source connector (using Auth, or usually an API key)
  2. set up Databricks Lakehouse 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 Monday

Monday is the first day of the week in most countries and is typically associated with the start of a new work or school week. It is often viewed as a day of productivity and setting goals for the week ahead. Many people may feel a sense of dread or stress on Mondays, commonly referred to as the "Monday blues." However, others may view it as an opportunity to start fresh and tackle new challenges. Some cultures also have specific traditions or superstitions associated with Mondays, such as avoiding certain activities or wearing specific colors. Overall, Monday represents a new beginning and a chance to make the most of the week ahead.

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate Monday with Databricks Lakehouse in minutes

Try for free now

Prerequisites

  1. A Monday account to transfer your customer data automatically from.
  2. A Databricks Lakehouse 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 Monday and Databricks Lakehouse, for seamless data migration.

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

Step 1: Set up Monday as a source connector

1. First, navigate to the Airbyte website and create an account.
2. Once you have logged in, click on the ""Sources"" tab on the left-hand side of the screen.
3. Scroll down until you find the Monday source connector and click on it.
4. You will be prompted to enter your Monday API key. If you do not have one, you can generate one by following the instructions provided.
5. Once you have entered your API key, click on the ""Test"" button to ensure that the connection is successful.
6. If the test is successful, click on the ""Save & Continue"" button to proceed.
7. You will then be prompted to select the data you want to sync from Monday. You can choose from a variety of options, including boards, tasks, and users.
8. Once you have selected the data you want to sync, click on the ""Save & Continue"" button to proceed.
9. You will then be prompted to set up your destination connector. This will depend on where you want to sync your data to.
10. Follow the instructions provided to set up your destination connector and complete the syncing process.

Step 2: Set up Databricks Lakehouse 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 "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.

Step 3: Set up a connection to sync your Monday data to Databricks Lakehouse

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

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

Use Cases to transfer your Monday data to Databricks Lakehouse

Integrating data from Monday to Databricks Lakehouse provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Monday account as an Airbyte data source connector.
  2. Configure Databricks Lakehouse as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Monday to Databricks Lakehouse 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 Monday?

Monday's API provides access to a wide range of data related to project management and team collaboration. The following are the categories of data that can be accessed through Monday's API:

1. Boards: This category includes data related to the boards created in Monday, such as board name, description, and status.

2. Items: This category includes data related to the items created within a board, such as item name, description, and status.

3. Users: This category includes data related to the users who have access to a board, such as user name, email address, and role.

4. Groups: This category includes data related to the groups created within a board, such as group name, description, and members.

5. Columns: This category includes data related to the columns created within a board, such as column name, type, and settings.

6. Updates: This category includes data related to the updates made to a board or item, such as update text, creator, and timestamp.

7. Notifications: This category includes data related to the notifications sent to users, such as notification type, recipient, and timestamp.

Overall, Monday's API provides access to a comprehensive set of data that can be used to build custom integrations and applications to enhance project management and team collaboration.

What data can you transfer to Databricks Lakehouse?

You can transfer a wide variety of data to Databricks Lakehouse. 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 Monday to Databricks Lakehouse?

The most prominent ETL tools to transfer data from Monday to Databricks Lakehouse include:

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

These tools help in extracting data from Monday and various sources (APIs, databases, and more), transforming it efficiently, and loading it into Databricks Lakehouse 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:

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