How to load data from Merge to BigQuery

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

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Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
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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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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 Merge 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 Merge 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 Merge 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 CloudManual method (the hard way)

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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 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.

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What our users say

Raman Singh

Tech Lead at Symend

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

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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.”

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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."

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How to Sync to Manually

Step 1: Export Data from Merge

Begin by exporting the data from your Merge environment. Access your Merge dashboard and locate the data you wish to export. Utilize the built-in export functionality to download the data in a format compatible with BigQuery, such as CSV, JSON, or Avro.

Once you have exported the data, review and clean it if necessary. Ensure that the data is in a consistent format, with correct data types and no missing fields. This step is crucial for ensuring seamless import into BigQuery.

Log into your Google Cloud Platform (GCP) account and create a new Google Cloud Storage bucket. This bucket will be used to store your exported data temporarily. Name your bucket appropriately and configure it for your desired region and access permissions.

Upload the cleaned data files to your newly created Google Cloud Storage bucket. You can do this via the GCP Console by selecting "Upload Files" within the bucket interface, or by using the `gsutil` command-line tool, which allows for efficient data transfer to GCS.

In the BigQuery section of your GCP account, create a new dataset where you will store the imported data. Choose a descriptive name for your dataset and set the appropriate data location. Datasets in BigQuery function as containers for your tables.

Utilize the BigQuery Console or the `bq` command-line tool to load your data from the GCS bucket into the BigQuery dataset. When configuring the load job, specify the source format (e.g., CSV, JSON), schema, and any necessary data transformations. Ensure that the data is mapped correctly to your BigQuery table schema.

After loading the data into BigQuery, run queries to verify that the data integrity and structure are as expected. Check for discrepancies in data types, missing values, or any other inconsistencies. Use BigQuery's built-in query editor to perform these checks and confirm the successful migration of data.

By following these steps, you can effectively move data from Merge to BigQuery without relying on third-party connectors or integrations.