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 Apify Dataset as a source connector (using Auth, or usually an API key)
2. set up Google Firestore 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 Apify Dataset

What is Google Firestore

Prerequisites

1. A Apify Dataset account to transfer your customer data automatically from.
2. A Google Firestore 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 Apify Dataset and Google Firestore, for seamless data migration.

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

Step 1: Set up Apify Dataset as a source connector

Step 2: Set up Google Firestore as a destination connector

Step 3: Set up a connection to sync your Apify Dataset data to Google Firestore

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

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Google Firestore data warehouse is always up-to-date with your Apify Dataset data.

Use Cases to transfer your Apify Dataset data to Google Firestore

Integrating data from Apify Dataset to Google Firestore provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Apify Dataset account as an Airbyte data source connector.
2. Configure Google Firestore as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Apify Dataset to Google Firestore after you set a schedule