How to load data from Google Ads to Teradata
Learn how to use Airbyte to synchronize your Google Ads data into Teradata 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: Export Data from Google Ads
First, log in to your Google Ads account. Navigate to the campaign or data set you wish to export. Use the built-in reporting tools to customize your report to include all necessary metrics and dimensions. Once your report is configured, download the report in CSV format.
Step 2: Prepare Local Environment for Data Transformation
Ensure that your local environment has the necessary tools for data transformation. This often involves setting up a Python or R environment, or using spreadsheet software like Excel, to clean and manipulate the data. Install any required libraries or packages that will aid in data processing.
Step 3: Clean and Transform Data
Open the downloaded CSV file in your chosen tool. Perform data cleaning operations such as removing duplicates, handling missing values, and data type conversion to ensure consistency. Transform the data to match the schema of your target Teradata tables, ensuring that data types and formats align.
Step 4: Set Up Teradata Client Tools
Install Teradata SQL Assistant or a similar client tool that allows you to interact with your Teradata database. Ensure you have the necessary credentials and permissions to create tables and upload data. Configure the connection settings to connect to your Teradata database.
Step 5: Create Table in Teradata
Using Teradata SQL Assistant, write and execute a SQL script to create a table schema that matches the transformed data. Define appropriate data types, primary keys, and indexes. Verify the table structure to ensure it is ready to receive data.
Step 6: Load Data into Teradata
Utilize Teradata's FastLoad utility or a similar tool to load the cleaned and transformed data from your local environment into the Teradata table. If using scripts, ensure they are properly configured to handle the CSV input and insert data correctly. Monitor the loading process for any errors or warnings.
Step 7: Verify and Validate Data Transfer
Once the data is loaded, run SQL queries in Teradata to verify that all data has been accurately imported. Check for data integrity by comparing row counts and sample data between Google Ads and Teradata. Ensure that all metrics and dimensions are correctly represented and that there are no anomalies.
By following these steps, you can successfully move data from Google Ads to Teradata without relying on third-party connectors or integrations.