How to load your data from Teamleader to Google BigQuery

Pipes allows you to automatically replicate your Teamleader data into Google BigQuery on your defined schedule.
Load all your data from Teamleader to Google BigQuery to instantly get access to your Teamleader data. This data will always be updated on your defined schedule.
Pipes allows you to automatically load your Teamleader data into Google BigQuery. With ready-to-use connectors and pre-built data schemas for more than 200 APIs and web services you build data pipelines in minutes and without any coding. ​
1

Connect to Google BigQuery

This will be the destination of all data pipelines you build. Besides Google BigQuery, Pipes supports the most used relational databases in the cloud and on-premises.
2

Connect to Teamleader

Just enter your credentials to allow Pipes access to the Teamleader API. Then Pipes is able to retrieve your data from Teamleader.
3

Create a data pipeline from Teamleader to Google BigQuery

Pipes lets you select the data from Teamleader you want to have in Amazon Redshift. This pipeline will run automatically on your defined schedule!

About Teamleader

Teamleader provides a unified CRM, invoicing, and project planning platform for SMEs. Its easy-to-use software makes work easier for 10,000+ SMEs.

About Google BigQuery

Google BigQuery is a fully managed cloud data warehouse for analytics used by Fortune 500 companies as well as startups. While Google BigQuery works in conjunction with Google Storage for interactive analysis of massively large data sets it can scan TeraBytes in seconds and PetaBytes in minutes.

Your benefits with Pipes

Get central access to all your data

Access data from 200+ data sources with our ready-to-use connectors and replicate it to your central data warehouse.

Automate your data workflows

Stop manually extracting data and automate your data integration without any coding. We maintain all pipelines for you and cover all API changes!

Enable data-driven decision-making

Empower everyone in your company with consistent and standardized data, automate data delivery and measure KPIs across different systems.