Tutorials

Connecting your Google Sheets to Data Pipelines

Data Pipelines allows you to mix pipeline builder widgets and native Spark SQL in the same pipeline

Updating a data pipeline that is already scheduled is a single step process

Increase your data pipeline's efficiency by using an initial SQL query to load data.

By default, partition files output by your data pipelines are named dynamically. This tutorial shows you how to predefine a fixed name.

Use the generated DAG to get an overview of your Data Pipelines

Data Pipelines offers tiered pricing to suit everyone's use case and budget.

Data Pipelines lets users connect to various SQL databases via JDBC

Mapping and adding columns using our built-in widget is a powerful feature you can use to build your data pipelines

Incorporate Apache Spark SQL directly in your pipeline

Download your reports delivered to AWS S3 directly via Data Pipelines

Learn how to use dynamic external variables as part of a collaborative data pipeline process

Data Pipelines lets you move and combine data between AWS DynamoDB and Google Sheets.

Learn how to change the structure of your connected data using our no code SQL themed tools

Create a simple data pipeline in a few clicks

Organize your data pipelines into logical groups

Shared resources within your Data Pipelines organization

Important information about disconnecting Google services

How data connections work

How Data Pipelines makes pipeline building efficient by using a cache

Run recurring workloads using Data Pipelines' built-in scheduler

How to interpret data pipeline error messages and debug your pipeline

Take advantage of data pipelines JSON definitions

Share pipelines and collaborate

Use views to load multiple tables with wildcards

Learn how to connect Google Analytics to Data Pipelines.

Learn how to connect Google Big Query to Data Pipelines.

Use Data Pipelines to connect S3 to Google Sheets & schedule data to feed reports.