How to load data from Gridly to Databricks Lakehouse

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

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Set up a Gridly connector in Airbyte

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

Set up Databricks Lakehouse for your extracted Gridly data

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

Configure the Gridly 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 Gridly 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 Gridly

Gridly is a cloud-based headless CMS for multilingual game-as-a-service projects with an open API, browser-based spreadsheet UI, and built-in functions to handle localization and frequent updates. It is a collaborative system for users of any technical ability. Gridly is spreadsheet for multi-language content tailor-made for games and digital products. By connecting development, design, and localization teams and their tools, Gridly serves as a single source of truth for faster content updates. Gridly improves collaboration and streamlines content management and localization for your games or apps.

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 Gridly with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A Gridly 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 Gridly and Databricks Lakehouse, for seamless data migration.

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

Step 1: Set up Gridly as a source connector

1. First, navigate to the Gridly source connector page on Airbyte.com.
2. Click on the "Create new connection" button.
3. In the "Configuration" tab, enter a name for your connection.
4. In the "Source" section, select "Gridly" from the dropdown menu.
5. In the "API Key" field, enter your Gridly API key.
6. In the "Project ID" field, enter the ID of the Gridly project you want to connect to.
7. In the "Schema" section, select the schema you want to use for your data.
8. In the "Tables" section, select the tables you want to include in your data sync.
9. Click on the "Test" button to ensure that your connection is working properly.
10. If the test is successful, click on the "Create connection" button to save your configuration.
11. You can now use your Gridly source connector to sync data with your destination of choice.

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 Gridly data to Databricks Lakehouse

Once you've successfully connected Gridly 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 Gridly 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 Gridly 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 Gridly 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 Gridly data.

Use Cases to transfer your Gridly data to Databricks Lakehouse

Integrating data from Gridly 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 Gridly data, extracting insights that wouldn't be possible within Gridly alone.
Data Consolidation: If you're using multiple other sources along with Gridly, 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: Gridly 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 Gridly 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 Gridly data.
Data Science and Machine Learning: By having Gridly data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Gridly 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 Gridly 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 Gridly 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 Gridly 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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How to Sync Gridly to Databricks Lakehouse Manually

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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 Gridly?

What data can you extract from Gridly?

Gridly's API provides access to various types of data that can be used to manage and organize content for web and mobile applications. The following are the categories of data that Gridly's API gives access to:

1. Content data: This includes all the content that is stored in Gridly, such as text, images, videos, and audio files.
2. Metadata: This includes information about the content, such as the date it was created, the author, and any tags or categories associated with it.
3. User data: This includes information about the users who access the content, such as their login credentials, preferences, and activity history.
4. Analytics data: This includes data about how users interact with the content, such as page views, clicks, and engagement metrics.
5. Configuration data: This includes settings and configurations for the application, such as user permissions, access controls, and integration with other systems.

Overall, Gridly's API provides a comprehensive set of data that can be used to build and manage content-rich applications.

How do I transfer data from Gridly?

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 Gridly 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 Gridly 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.

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Warehouses and Lakes

Marketing Analytics

How to load data from Gridly to Databricks Lakehouse

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

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Gridly

What is Databricks Lakehouse

Prerequisites

Step 1: Setup Gridly

Step 2: Setup Databricks Lakehouse

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Gridly and Databricks Lakehouse

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Gridly 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 Gridly

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 Gridly with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A Gridly 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 Gridly as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

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

Once you've successfully connected Gridly 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 Gridly 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 Gridly 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 Gridly 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 Gridly data.

Use Cases to transfer your Gridly data to Databricks Lakehouse

Integrating data from Gridly 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 Gridly data, extracting insights that wouldn't be possible within Gridly alone.
Data Consolidation: If you're using multiple other sources along with Gridly, 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: Gridly 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 Gridly 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 Gridly data.
Data Science and Machine Learning: By having Gridly data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Gridly 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 Gridly table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.