How to load data from Snapchat Marketing to BigQuery
Learn how to use Airbyte to synchronize your Snapchat Marketing data into BigQuery within minutes.
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
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
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.
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
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“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.”
Rupak Patel
"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."
How to Sync to Manually
Step 1: Access Snapchat Marketing Data
- Log in to Snapchat Ads Manager: Access your Snapchat Ads Manager account where your marketing data is stored.
- Export Data: Locate the reporting or analytics section within the Snapchat Ads Manager. Use the interface to select the data you want to export. This could be campaign performance data, audience data, etc.
- Choose Data Format: Select the format in which you want to export the data. CSV or JSON formats are generally compatible with BigQuery.
- Download Data: Export and download the data to your local machine.
Step 2: Prepare Data for BigQuery
- Inspect Data: Open the exported data file and review the structure and content to ensure it’s clean and organized. Remove any unnecessary columns or rows that are not required for the analysis in BigQuery.
- Format Data: Convert the data into a format that is supported by BigQuery. If your data is in CSV format, ensure it adheres to the CSV guidelines provided by BigQuery (e.g., proper escaping of special characters, consistent use of delimiters).
- Validate Data Types: Make sure that the data types in your file match the data types in BigQuery (e.g., INT64 for integers, STRING for text, FLOAT64 for decimals).
- Split Large Files: If your data file is very large, consider splitting it into smaller files to make the upload process more manageable and to avoid potential timeouts.
Step 3: Upload Data to Google Cloud Storage (Optional)
- Create a Google Cloud Storage Bucket: If your data file is large, it’s recommended to first upload it to Google Cloud Storage. Go to the Google Cloud Console and create a new storage bucket.
- Upload Files: Upload the prepared data files to the newly created Google Cloud Storage bucket.
Step 4: Create a BigQuery Dataset and Table
1. Access BigQuery: Go to the Google Cloud Console and access the BigQuery service.
2. Create Dataset: Click on "Create Dataset" and provide the necessary information such as Dataset ID and location.
3. Create Table: Within the dataset, click on "Create Table". You can create the table schema manually or let BigQuery auto-detect the schema if your data is self-descriptive (e.g., CSV with a header row).
Step 5: Import Data into BigQuery
- Import from Local File (Small Files):
- In the BigQuery UI, select your dataset and click on “Create Table”.
- Set “Create table from” to “Upload”.
- Choose your file and select the appropriate file format.
- Configure the schema settings, either by manually creating it or by selecting “Auto detect” if your data is well formatted.
- Import from Google Cloud Storage (Large Files):
- In the BigQuery UI, select your dataset and click on “Create Table”.
- Set “Create table from” to “Google Cloud Storage” and provide the path to your file(s).
- Choose the appropriate file format.
- Configure the schema settings, as above.
- Start the Import: Click on “Create Table” to begin the import process. BigQuery will process the file and import the data into the table you’ve created.
Step 6: Verify Data Import
- Check Job Status: In the BigQuery UI, go to the “Job History” tab to check the status of your import job. Ensure that it has completed successfully.
- Query Table: Run a simple SELECT query on the newly created table to verify that the data has been imported correctly.
- Inspect Data: Look through the imported data in BigQuery to ensure that it matches the original data from Snapchat Marketing and that there are no issues with the data types or formatting.
Step 7: Clean Up
- Delete Temporary Files: If you uploaded files to Google Cloud Storage and no longer need them there, clean up by deleting the temporary files to avoid unnecessary storage charges.
- Documentation: Document the process, including any schema mappings and transformations, for future reference or for other team members.