How to load data from Apify Dataset to BigQuery

Learn how to use Airbyte to synchronize your Apify Dataset data into BigQuery within minutes.

Try Airbyte Cloud Manual method (the hard way)

Building your pipeline or Using Airbyte

Airbyte is the only open source 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 AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Apify Dataset connector in Airbyte

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

Set up BigQuery for your extracted Apify Dataset data

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

Configure the Apify Dataset to BigQuery 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.

Try Airbyte Cloud Manual method (the hard way)

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

Talk to our team

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

Try Airbyte Cloud Manual method (the hard way)

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 enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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 AI 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

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Learn more

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

Rupak Patel

Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

Learn more

How to Sync to Manually

Step 1: Extract Data from Apify

First, retrieve your data from Apify. Log in to your Apify account, navigate to the desired actor or task, and access the dataset that contains the data you want to export. Use the Apify API to fetch the data by sending a GET request to the dataset endpoint. You can use programming languages such as Python with requests or Node.js with axios to automate this process.

Step 2: Convert Data to CSV Format

Once the data is extracted, convert it into a CSV format, as it is a widely accepted format for data import in BigQuery. You can use a programming language like Python to parse the JSON data from Apify and write it into a CSV file using libraries like csv or pandas. Ensure that the CSV file accurately represents your dataset's schema.

Step 3: Set Up Google Cloud SDK

To interact with Google Cloud services, set up the Google Cloud SDK on your local machine. Download and install the SDK from the Google Cloud website, and initialize it by running `gcloud init` in your terminal. Follow the prompts to authenticate and configure your default project and region settings.

Step 4: Create a Google Cloud Storage Bucket

BigQuery can import data from Google Cloud Storage. Create a storage bucket in your Google Cloud project to temporarily hold your CSV file. Use the Google Cloud Console or the command line with `gsutil mb gs://your-bucket-name/` to create the bucket. Ensure that the bucket is in the same location as your BigQuery dataset for optimal performance.

Step 5: Upload CSV to Google Cloud Storage

Upload the CSV file to your newly created Google Cloud Storage bucket. You can use the `gsutil cp` command to transfer the file from your local machine to the cloud: `gsutil cp your-file.csv gs://your-bucket-name/`. Ensure the file is uploaded correctly by checking the contents of your bucket.

Step 6: Prepare BigQuery Dataset and Table

Before importing the data, ensure you have a BigQuery dataset and table ready. If not, create a dataset using the BigQuery Console or via `bq mk --dataset your_dataset_name`. Then, define the table schema to match the structure of your CSV file. This can be done in the BigQuery Console or by using a JSON schema file and the `bq mk --table` command.

Step 7: Load Data into BigQuery

Use the `bq` command-line tool to load the data from Google Cloud Storage into BigQuery. Execute a command like `bq load --source_format=CSV your_dataset_name.your_table_name gs://your-bucket-name/your-file.csv`, specifying the source format and the path to your CSV file. Verify that the data has been loaded correctly by querying the table in the BigQuery Console.

By following these steps, you can successfully transfer data from Apify to BigQuery without the need for third-party connectors or integrations.