Data replication

Open-source ETL from Google Ads

The Google Ads API is the modern programmatic interface to Google Ads and the next generation of the AdWords API and it is a paid online advertising platform offered by Google. Google Ads is a paid search channel. Google Ads is a key digital marketing tool for any business which is looking to get meaningful ad copy in front of its target audience. Google AdWords is a well known marketplace where companies pay to have their website ranked at the top of a search results page, based on keywords.

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Google Ads

Setup in 3 easy steps

Start moving your data securely and reliably to any destination.

Setup Source

Authenticate securely with your instance using OAuth or API keys.

Choose Destination

Select from 50+ data warehouses, lakes, or databases to load your extracted data.

Configure Connection

Select streams, sync frequency, and sync modes (Full Refresh or Incremental).

Why Airbyte?

Airbyte is the only unified data movement platform built on an open standard, uniquely positioned for connector extensibility and AI workflows.

Syncing data from is only one of your 1,000 future data pipeline needs.

Leverage the largest Marketplace of 600+ pre-built connectors. Join 2,000 + data engineers who built 7,000+ custom connectors in minutes with low-code/no-code Connector Builder or AI Assistant.

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Create context for AI agents

Airbyte's pipelines transfer structured and unstructured data together for metadata preservation. With support for flexible destinations such as Iceberg, Airbyte is the ideal data movement solution for agentic applications.

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Any specific way you would like to sync data from ? Airbyte has you covered.

UI: Create connections and custom connectors in minutes.

API: Programmatic interactions, data syncing, and embedded connectors.

Terraform: Integration with CI/CD tools and rapid deployment with Infrastructure as Code.

PyAirbyte: Build LLM applications with Python libraries, SQL tools, and AI frameworks.

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Flexible deployment options: self-hosted, cloud, and hybrid

Secure and compliant: ISO 27001, SOC 2, GDPR, HIPAA, data encryption, audit/monitoring, SSO, RBAC, and more. Centralized multi-tenant management with self-serve capabilities.

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Trusted by AI and Data leaders

Raman Singh

Tech Lead at Symend

"With our legacy framework, if one of the pipelines fails for one client, it will stop everything for the rest of our clients. But with Airbyte, things are run in parallel because of the platform’s distributed nature, which means that we can process multiple clients at the same time without impacting performance."

75%
reduction in sync times
$ 900K
in annual savings
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Sean Carver

Director of Data at PetDesk

"The real ROI is in our ability to iterate quickly, especially at our increasing scale. At the end of the day, you want a tool like that to just work. We can forget about it and know that it's configured and it's connecting and it's working. That hands-free capability is a big appeal for the platform.”

20+
data sources integrated and growing
+1
FTE engineer in productivity efficiency
85%+
reduction in  data source integration time
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Mondor La Grange

Head of BI and Data Engineering

"Unlike Fivetran's credit-based system that created budget uncertainty, Airbyte's pricing model allows Kuda to forecast expenses accurately and avoid surprise bills."

20+
data sources integrated and growing
+1
FTE engineer in productivity efficiency
85%+
reduction in  data source integration time
Learn more

Amy Zhao

Senior Manager of Data Engineering

"What's different from Stitch Data or Informatica is the way that we can configure Airbyte connections and Airbyte entities through code. That's a huge plus to us as data engineers, because we are used to checking code and being able to manage changes from Github."

3 to 1
reduction in data integrations solutions for reduced TCO
1
week Shopify and Stripe integration with Airbyte
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Franziska Ibscher

Product Manager at Drivepoint

"Airbyte allows us to stay flexible while scaling from hundred-million to billion-dollar enterprise clients."

75%
of customers increased profitability
6.7%
EBITDA increase for customers
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FAQs

What is ETL?

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

What is ?

What data can you extract from ?

provides access to a wide range of data types, including:

1

Structured data: This includes data that is organized into tables with defined columns and data types, such as CSV, JSON, and Avro files.

2

Semi-structured data: This includes data that has some structure, but not necessarily a fixed schema, such as XML and JSON files.

3

Unstructured data: This includes data that has no predefined structure, such as text, images, and videos.

4

Time-series data: This includes data that is organized by time, such as stock prices, weather data, and sensor readings.

5

Geospatial data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and spatial databases.

6

Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets and feature vectors.

7

Streaming data: This includes data that is generated in real-time, such as social media feeds, IoT sensor data, and log files.

Overall, 's API provides access to a wide range of data types, making it a powerful tool for data analysis and machine learning.

How do I transfer data from ?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1

Set up as a source connector (using Auth, or usually an API key)

2

Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector

3

Define which data you want to transfer from and how frequently

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

What are top ETL tools to transfer data from ?

The most prominent ETL tools to transfer data to include:

Airbyte

Fivetran

StitchData

Matillion

Talend Data Integration

These tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into and other databases, data warehouses and data lakes, enhancing data management capabilities.

What is ELT?

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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