How to load data from CallRail to Databricks Lakehouse

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

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Set up a CallRail connector in Airbyte

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

Set up Databricks Lakehouse for your extracted CallRail data

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

Configure the CallRail 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.

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TL;DR

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

CallRail is a cloud-based call tracking and analytics platform that helps businesses of all sizes to track and analyze their phone calls. It provides businesses with a unique phone number for each marketing campaign, which allows them to track the source of their calls and measure the effectiveness of their marketing efforts. CallRail also offers features such as call recording, call routing, and call analytics, which help businesses to improve their customer service and sales performance. With CallRail, businesses can gain valuable insights into their phone calls and make data-driven decisions to optimize their marketing and sales strategies.

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 CallRail with Databricks Lakehouse in minutes

Try for free now

Prerequisites

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

When using Airbyte to move data from CallRail to Databricks Lakehouse, it extracts data from CallRail 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 CallRail data for advanced analytics and insights within Databricks Lakehouse, simplifying the ETL process and saving significant time and resources.

Step 1: Set up CallRail as a source connector

1. First, log in to your Airbyte account and navigate to the "Sources" tab.
2. Click on the "Add Source" button and select "CallRail" from the list of available connectors.
3. Enter your CallRail API key and click on the "Test Connection" button to ensure that the connection is successful.
4. Once the connection is verified, you can configure the settings for your CallRail source connector, such as selecting the specific data you want to sync and setting up a schedule for automatic syncing.
5. After configuring your settings, click on the "Create Source" button to finalize the setup of your CallRail source connector.
6. You can now use your CallRail source connector to sync data from your CallRail account to your Airbyte destination connectors, such as Google Sheets or Snowflake.
7. To monitor the status of your CallRail source connector, you can check the "Jobs" tab in your Airbyte account to see if any syncs are currently running or if there are any errors that need to be addressed.
8. You can also edit or delete your CallRail source connector at any time by navigating to the "Sources" tab in your Airbyte account and selecting the appropriate option.

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 CallRail data to Databricks Lakehouse

Once you've successfully connected CallRail 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:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select CallRail from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse 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 CallRail objects you want to import data from towards Databricks Lakehouse. 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 CallRail 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 CallRail data.

Use Cases to transfer your CallRail data to Databricks Lakehouse

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

Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your CallRail data, extracting insights that wouldn't be possible within CallRail alone.
Data Consolidation: If you're using multiple other sources along with CallRail, 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.
Historical Data Analysis: CallRail has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing CallRail data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding CallRail data.
Data Science and Machine Learning: By having CallRail data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While CallRail 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 CallRail 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:

Configure a CallRail account as an Airbyte data source connector.
Configure Databricks Lakehouse as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from CallRail 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!

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Sync with Airbyte

Step 1: Set up CallRail as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

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

Once you've successfully connected CallRail 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:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select CallRail from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse 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 CallRail objects you want to import data from towards Databricks Lakehouse. 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 CallRail 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 CallRail data.

How to Sync CallRail to Databricks Lakehouse 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 CallRail?

What data can you extract from CallRail?

CallRail's API provides access to a wide range of data related to call tracking and analytics. The following are the categories of data that can be accessed through CallRail's API:
1. Call data: This includes information about incoming and outgoing calls, such as call duration, call recording, caller ID, call source, and call outcome.
2. Lead data: This includes information about leads generated through calls, such as lead source, lead status, lead score, and lead contact information.
3. Keyword data: This includes information about the keywords that triggered calls, such as keyword source, keyword match type, and keyword performance.
4. Form data: This includes information about form submissions generated through calls, such as form source, form status, and form contact information.
5. Account data: This includes information about the CallRail account, such as account settings, user information, and billing information.
6. Integration data: This includes information about integrations with other platforms, such as Google Analytics, Salesforce, and HubSpot.
Overall, CallRail's API provides a comprehensive set of data that can be used to analyze call tracking and optimize marketing campaigns.

How do I transfer data from CallRail?

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 CallRail 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 CallRail 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.

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.

Back to all tutorials

Warehouses and Lakes

Marketing Analytics

How to load data from CallRail to Databricks Lakehouse

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

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is CallRail

What is Databricks Lakehouse

Prerequisites

Step 1: Setup CallRail

Step 2: Setup Databricks Lakehouse

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between CallRail and Databricks Lakehouse

Get your data syncing in minutes

Try Airbyte free

TL;DR

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

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 CallRail with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A CallRail account to transfer your customer data automatically from.
A Databricks Lakehouse 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 CallRail as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

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

Once you've successfully connected CallRail 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:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select CallRail from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse 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 CallRail objects you want to import data from towards Databricks Lakehouse. 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 CallRail 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 CallRail data.

Use Cases to transfer your CallRail data to Databricks Lakehouse

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

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