How to load data from Smaily to BigQuery

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

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

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

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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.

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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.

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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.

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Tech Lead at Symend

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Chase Zieman

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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."

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How to Sync to Manually

Step 1: Export Data from Smaily

Begin by logging into your Smaily account. Navigate to the section where you can access the data you wish to transfer to BigQuery, such as subscriber lists or campaign data. Use Smaily's export functionality to download this data in a CSV format. If needed, perform multiple exports to obtain all necessary datasets.

Once you have the CSV files, review them to ensure they contain the correct data and format. Clean and transform the data as necessary to align with the schema you will use in BigQuery. This step may include renaming columns, changing data types, or removing unnecessary fields to ensure compatibility.

If you haven't already, create a Google Cloud Platform (GCP) project. Go to the Google Cloud Console, click on the project dropdown, and select "New Project." Give your project a name and make a note of the project ID for future reference. Ensure that billing is enabled for the project.

In the Google Cloud Console, navigate to the "APIs & Services" section and search for "BigQuery API." Enable the API for your project. This step allows your project to interact with BigQuery services.

Access the BigQuery interface via the Google Cloud Console. Click on your project, then select "Create Dataset." Provide a name for your dataset and set any necessary data location and expiration settings. This dataset will serve as the container for your tables and data.

In the BigQuery console, select your newly created dataset and click "Create Table." Choose the option to upload from "Google Cloud Storage" or "Upload" if you are directly uploading a CSV file from your local machine. Specify the CSV file, configure the schema by either auto-detecting or manually setting field names and types, and finalize the upload.

After the upload is complete, verify that the data has been successfully imported by running basic queries in the BigQuery console. Check that the data types and values are as expected. Perform sample queries to ensure data integrity and correctness. Adjust the schema or re-upload data if necessary to correct any issues.

By following these steps, you can efficiently transfer data from Smaily to BigQuery without relying on third-party connectors.