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

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 set that matters to your business. And to do that, your data science team needs the right ETL tool.

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

Google BigQuery ETL Overview

BigQuery is a serverless, scalable, and fully managed cloud data warehouse part of the Google Cloud Platform (GCP).
Users create data pipelines with SQL using Google's integrated tools or third-party ETL tools.
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 data warehouse.
Google offers several BigQuery 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.

Pricing

Flexible on-demand and flat-rate pricing. Check out our deep-dive analysis of BigQuery's pricing plans.
On-demand data analytics: free for 1TB/month; $5.00/TB thereafter
Flat rate: Based on pre-committed amounts, with steep discounts for longer commitments

Key features

BigQuery divides its storage and compute resources, which means handling computations where the data is instead of replicating it elsewhere.
Serverless architecture means you don't need to worry about allocating clusters or resources to individual processes.
Machine learning is built-in with SQL queries, letting you access much more advanced features without learning a new skill set.
BigQuery is an OLAP (online analytical processing) solution that works best with relatively infrequent database writes and can handle much more frequent reads.

Pros and cons of using BigQuery

BigQuery Advantages	BigQuery Disadvantages
Google BigQuery supports JSON functions through SQL queries.	Google BigQuery only works with Google Cloud infrastructure.
Seven-day history of changes made to BigQuery tables.	"Black box" serverless architecture determines your settings automatically, giving you less flexibility and control than Amazon Redshift.
BigQuery Sandbox is for using BigQuery and other Cloud Console apps without commitment.	More expensive than competing data warehouses.
Engineered for performance and scalability, BigQuery can be used as a data lake, making it ideal for historical data analytics.

Who is BigQuery best suited for?

Data science teams seeking a powerful cloud data warehouse with fewer management requirements will be satisfied with BigQuery.

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

And, of course, BigQuery is an obvious option for data engineers familiar with the Google Cloud Platform ecosystem.

How to Choose the Best 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

Your BigQuery data should function as the foundation of the best data-driven insights. Tools that lack data integration features for mission-critical apps aren't going to deliver the 360-degree view your team needs.

Extensibility

Look for a tool that supports the data pipelines you need now and can grow with you in the future.

Choose a BigQuery ETL solution that supports various use cases and workflows and supports the different 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.

Pricing

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

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

Portable
Google Cloud Dataflow
Google Cloud Data Fusion
Stitch
Hevo

1) Portable

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

Portable has 500+ long-tail connectors for the kind of SaaS apps many enterprise data integrations lack. In addition to BigQuery, Portable supports Snowflake and Amazon Redshift.

Portable also develops custom connectors for apps you can't find anywhere else. Share the details on the data pipeline, and the portable app will add the API connection in a few hours.

Plus, the team handles all maintenance, alerting, monitoring, and troubleshooting, so you don't have to. As APIs evolve, Portable maintains connectors so they keep working, and you can rest easy.

Pricing

Free: Portable offers a free plan for manual data workflows without caps on volume, connectors, or data warehouses.
Automated data flows: $200 per data flow monthly
For enterprise requirements and SLAs, contact sales.

Key features

500+ data integrations for less common sources that other ETL tools don't support
Custom connectors at no additional cost and with fast turnaround times.
Integrates popular business apps such as Salesforce, Unbounce, and Twilio.
Premium support is available for users on all plans.

Disadvantages

Portable doesn't have connectors for major enterprise applications like Oracle.
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 data pipelines built in Java or Python and integrates seamlessly with BigQuery. Dataflow uses Apache Beam as its engine.

Pricing

Google Cloud Dataflow uses a complex consumption-based pricing model based on region, job type, CPU, memory, and amount of data processed.
Expect to pay approximately $0.10 per hour per GB to transform data. Depending on your ETL process, pricing varies.

Key features

Integrates with Google BigQuery and other GCP products.
Wide range of templates to speed up development.
Works for batch and streaming data.

Disadvantages

There are no built-in SaaS source integrations.
There are quotas for usage that can be limiting, although you can override some of them by contacting Google support.
It only works with Google's big data platform, so if you switch providers or use another data warehouse, you'll need a different data processing solution.

Who is Google Cloud Dataflow best suited for?

Dataflow is best suited for teams fully integrated into the GCP ecosystem looking for a code-friendly BigQuery ETL tool.

3) Google Cloud Data Fusion

Google Cloud Data Fusion is another GCP product 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.

Pricing

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

User-friendly interface that lets you create ETL workflows without code.
Pre-built transformations to get data pipelines up and running faster.
Ability to import from on-premises sources in real-time.
The serverless platform handles infrastructure provisioning, cluster management, and more automatically.
Plugins for loading data, performing common dataset transformations, and populating business intelligence dashboards (Looker)

Disadvantages

There are no built-in SaaS data source connectors.
A graphic interface can be challenging to use for creating complex pipelines.
Non-technical users might struggle with data management tasks.

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 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 data transformation using its GUI or Python, Java, or SQL scripts.

Pricing

Standard: Starts at $100/month for up to 5 million active rows per month, one destination, and 10 standard sources
Advanced: Starts at $1,250/month for up to 100 million rows and three destinations
Premium: $2,500/month for up to 1 billion rows and five destinations
A 14-day free trial is available.

Key features

137 data sources are supported.
Part of the Talend ecosystem and integrates with other tools on the platform.
Intuitive platform with GUI-based transformations.
Monitoring and alerts are handled automatically.

Disadvantages

Limited options for data transformations.
No on-premise deployment is available.
Destinations and sources can be limiting, depending on your plan tier.

Who is Stitch best suited for?

Stitch is ideal for teams with popular data sources that only need simple transformations. You'll need to upgrade your plan if you need more support than self-service tutorials and chat.

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 data transformations.

Pricing

Free: Up to one million events, defined as new records inserted or updated, from 50+ data sources
Starter: Starting at $239/month for 150+ connectors and five million events
Business: Custom quote

Key features

150+ data connectors (limited to 50+ on the free plan)
Data migration in real-time.
Robust data transformation support through Python scripting
24/7 live support

Disadvantages

Less granular controls over ingestion and loading schedules.
The 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 science teams with common data sources that prefer a no-code platform but want the flexibility to write code.

Runner-up BigQuery ETL Tools

Nearly every data integration tool has at least some integration with BigQuery. Here are some other tools we didn't feature but may fit your data engineering needs well.

Apache Spark

Spark is an open-source engine for processing large amounts of data. It works with batch and streaming data and recognizes Java, Python, R, Scala, and SQL languages. Its machine learning functionality scales big data management from a laptop to a cloud data warehouse.

Apache Airflow

Airflow is another open-source ETL tool that uses Python. It's designed for more technical users who want complete control over creating custom pipelines. It's intended for scheduling data pipelines or automating ETL processes.

Fivetran

Fivetran is a commercial ETL tool that supports more than 160 data sources. It's designed for enterprise-level organizations. Its approach to data processing is priced by monthly active rows (data volume), but it offers many sophisticated data integrations.

Matillion

Matillion aims to be an all-in-one ETL tool that supports BigQuery and other major destinations. It offers cloud-based and on-premise deployments. As a component of the modern data stack, Matillion helps surface real-time data insights for stakeholders.

Simplify Your BigQuery Data Transformation

Google BigQuery is one of the most scalable data warehouses, making it a must-have technology for data science teams. Its makes managing big data accessible for thousands of organizations worldwide.

These BigQuery ETL tools have strengths, weaknesses, and novel approaches to simplifying data flows.

The hard way is to custom-script your own data pipeline over many days and nights. The easy way is to use Portable to effortlessly extract, transform, and load data from over 500 unique data providers without writing code in minutes. It's a hallmark of modern data management.

Get your data team focused on uncovering new business insights rather than dealing with the menial tasks of moving data from one place to another.