Best BigQuery ETL Tools: Pros, Cons, Features, Pricing

CEO, Portable

Google BigQuery has become a dominant leader in the world of big data.

But that data only works if you can collect and analyze metrics from every data source that matters to your business. And to do that, you'll need the right ETL tool.

Today, we'll look at the best ETL tools for Google BigQuery, which ETL solutions are best for your use case, and how to choose the right platform for your business case.

Overview of ETL on BigQuery

  • BigQuery is a serverless, scalable, fully managed cloud data warehouse that's part of the Google Cloud Platform (GCP).

  • Users can create ETL pipelines with SQL using Google's integrated tools or third-party platforms.

  • You can access BigQuery through the cloud console, command line tool, or REST API.

  • BigQuery connects with most major business intelligence tools to deliver data insights in a visual dashboard.

  • BigQuery offers superior performance, scalability, and speed compared to platforms like SQL Server. That's because of its fully managed architecture which handles backend tasks automatically to improve performance.

  • Google offers several ETL tools, including Dataflow and Data Fusion, but third-party tools offer more flexibility.

  • Whether you want  ETL (extract, transform, load) or ELT (extract, load, transform) processes, you can find a tool that works with BigQuery.

Why BigQuery is a smart choice for ETL

BigQuery is one of the most popular data warehouses, but it's not the only one. It's in a crowded field with competition from other players like Snowflake, Amazon Redshift, and Microsoft Azure Synapse Analytics.


  • Check out our deep-dive analysis of BigQuery's plans & pricing.

  • On-demand analysis: Free for 1TB/month; $5.00/TB thereafter

  • Flat-rate: Based on pre-committed amounts, with steep discounts for larger commitments

Key features

  • BigQuery divides its storage and compute resources, which means handling computations where the data is instead of needing to replicate it somewhere else.

  • Serverless architecture means you don't need to worry about allocating clusters or resources to individual processes.

  • Machine learning built-in with SQL queries, letting you access much more advanced features without needing to learn a new skill set.

  • BigQuery is a OLAP (Online Analytical Processing) solution, which works best with relatively infrequent database writes and can handle much more frequent reads.


  • Support for JSON functions through SQL queries.

  • Seven-day history of changes made to BigQuery tables.

  • BigQuery Sandbox for using BigQuery and other Cloud Console apps without any commitment.


  • BigQuery only works with Google Cloud infrastructure.

  • "Black box" serverless architecture determines your settings automatically, giving you less flexibility and control.

  • More expensive than competitors.

Who is BigQuery best suited for?

BigQuery is best for teams looking for a powerful cloud-based data warehouse with less management requirements.

BigQuery's managed platform, serverless architecture, and low administrative overhead mean less time overseeing infrastructure and more time using the platform.

And of course, BigQuery is an obvious option for data teams fully committed to the Google Cloud Platform ecosystem.

How to choose the right BigQuery ETL tool

If you're choosing a BigQuery ETL tool, there are a few features to pay careful attention to. Each solution has its advantages and disadvantages.

Data sources

For the best insights, your BigQuery data should have a complete picture. Tools that lack the data integration features for mission-critical apps aren't going to deliver the 360-degree view your team needs.


Look for a tool that not only supports the data pipelines you need now, but can grow with you in the future.

Choose a tool that can support various use cases and workflows, and support future data sources and SaaS apps you'll use down the road.

Customer Support

Your data engineering team should spend most of its time leveraging the data, not moving it from one place to the next. The best ETL tools will offer hands-on support to help guide you through this process.


Budgets matter, of course, but even more important for many teams is a pricing model that's easy to understand and predict.

Consumption-based pricing can change every month, making it hard to estimate costs from one billing cycle to the next.

Top 5 BigQuery ETL tools

  1. Portable
  2. Google Cloud Dataflow
  3. Google Cloud Data Fusion
  4. Stitch
  5. Hevo

1. Portable

Portable is the most ideal BigQuery ETL tool for long-tail data sources.

Portable has 300+ hard-to-find long-tail connectors for the kind of SaaS apps that most enterprise applications don't support.

Portable will also develop custom connectors for apps you can't find anywhere else. Share the details on the ETL pipeline you need to set up, and the Portable team can deliver a connector in as little as a few hours.

Plus, the team handles all maintenance, alerting, monitoring, and troubleshooting so you don't have to. When APIs change, Portable will repair connectors so they keep working---and so you can rest easy.


  • Portable offers a free plan for manual data workflows with no caps on volume, connectors, or destinations.

  • For automated data flows, Portable charges a flat fee of $200/month.

  • For enterprise requirements and SLAs, contact sales.

Key features

  • 300+ connectors for hard-to-find data sources other ETL tools don't support.

  • Development and maintenance of custom connectors at no additional cost and with fast turnaround times.

  • Premium support for users on all plans.


  • Portable doesn't have connectors for major enterprise applications like Oracle, Salesforce, or Quickbooks.

  • Does not support data lakes.

  • Only available for customers in the U.S.

Who is Portable best suited for?

Portable is best for teams with long-tail data sources that want to focus on insights, not data management.

2. Google Cloud Dataflow

Dataflow is an ETL tool that's part of the Google Cloud Platform. It accepts pipelines built in Java or Python and integrates seamlessly with BigQuery. Dataflow uses Apache Beam as its engine. 


  • Google Cloud Dataflow uses a complex consumption-based pricing model based on region, job type, CPU, memory, and amount of data processed

Key features

  • Integrates with BigQuery and other GCP products.

  • Wide range of templates to speed up development.

  • Works for batch and streaming data.


  • No built-in SaaS source integrations.

  • Quotas for usage that can be limiting, although you can override some of them by contacting Google support.

  • Only works with Google's suite of tools, so if you switch providers or use another destination, you'll need a different platform.

Who is Google Cloud Dataflow best suited for?

Dataflow is best suited for teams fully integrated into the Google Cloud ecosystem that are looking for a code-first ETL tool.

3. Google Cloud Data Fusion

Google Cloud Data Fusion is another GCP product, but one focused more on simple integrations than complex data transformation workflows.

Data Fusion is a no-code platform that uses a GUI to import data into BigQuery. It's built with the open-source Cask Data Application Platform (CDAP) under the hood.


  • Developer: $0.35/instance/hour (est. $250/month)

  • Basic: $1.80/instance/hour (est. $1,100/month)

  • Enterprise: $4.20/instance/hour (est. $3,000/month)

Key features

  • Point-and-click interface that lets you create ETL workflows without using code.

  • Pre-built transformations to get pipelines up and running faster.

  • Ability to import from on-premise sources in real-time.

  • Serverless platform handles infrastructure provisioning, cluster management, and more automatically.

  • Plugins for loading data, performing common dataset transformations, and  


  • No built-in SaaS data source connectors.

  • Graphic interface can be difficult to use for creating complex pipelines.

Who is Google Cloud Data Fusion best suited for?

Google Cloud Data Fusion is best suited for teams that work exclusively with GCP, but need a no-code tool for data integration.

4. Stitch

Stitch is an ETL tool that's part of the Talend suite of tools. It includes features to load data into BigQuery and handle replication tasks using change data capture.

Stitch also supports simple transformations using its GUI or scripts written in Python, Java, or SQL.


  • Standard plan starting at $100/month for up to 5 million active rows per month, one destination, and 10 sources (limited to "Standard" sources)

  • Advanced plan at $1,250/month for up to 100 million rows and three destinations

  • Premium plan at $2,500/month for up to 1 billion rows and five destinations

  • 14-day free trial available

Key features

  • 130+ data sources supported.

  • Part of Talend ecosystem and integrates with other tools in the platform.

  • Intuitive platform with GUI-based transformations.

  • Monitoring and alerts handled automatically.


  • Limited options for data transformations.

  • No on-premise deployment available.

  • Destinations and sources can be limiting, depending on your plan tier.

Who is Stitch best suited for?

Stitch is best for teams with popular data sources that only need simple transformations.

5. Hevo

Hevo is a no-code platform that has 150+ data connectors. It supports ETL, ELT, and Reverse ETL workflows and includes features like real-time data loading, replication, and transformations.


  • Free: Up to one million events (limited to 50+ data sources)

  • Starter: Starting at $239/month

  • Business: Custom quote

Key features

  • 150+ data connectors (limited to 50+ on free plan).

  • Data migration in real-time.

  • Robust data transformation support through Python scripting.

  • 24/7 live support.


  • Not as granular of controls over ingestion and loading schedules.

  • Platform doesn't always automate schema mapping from one tool to another, and may require manual work.

Who is Hevo best suited for?

Hevo is best for data teams with common data sources that prefer a no-code platform, but want the flexibility to write code if need be.

Runner-up BigQuery ETL tools

Nearly every data integration tool has at least some level of integration with BigQuery. Here are some other tools that weren't featured but may be good fits for your organization's needs.

Apache Spark

Spark is an open-source engine for processing large amounts of data. It works with both batch and streaming data, and recognizes Java, Python, R, Scala, and SQL languages.

Apache Airflow

Airflow is another open-source tool that uses Python. It's designed for more technical users who want complete control to create custom pipelines.


Fivetran is an industry-leading ETL tool that supports 160+ data sources. It's designed for enterprise-level organizations.


Matillion is an all-in-one ETL tool that supports BigQuery and other major destinations. It offers cloud-based and on-premise deployments.

BigQuery ETL: The Bottom Line

BigQuery is one of the most popular tools for big data, from machine learning, to data analytics, and beyond.

To make it all work, you need an ETL system that can pull in the most important data for your business. But every ETL tool has its strengths and weaknesses.

You need your data team focused on reviewing key metrics and discovering new insights---not dealing with the menial tasks of moving data from one place to another.