BigQuery Data Integrations & ETL Tools [Free & Paid]

CEO, Portable

BigQuery Overview

What is BigQuery? 

Google BigQuery is a multi-cloud, serverless data warehouse that stores enormous datasets. This warehouse enables you to store and analyze big read-only datasets and turn them into business intelligence (BI) via their APIs. BigQuery can run queries on billions of rows of data in seconds.

Big data analysis requires an enormous amount of human resources and infrastructure to perform data analysis, As a result, larger organizations tend to use it for enterprise analytics.

Generally, data science teams also find BigQuery attractive for its affordable pricing.

Pricing for BigQuery 

Resources of BigQuery will be allocated to the user only as they require, in the form of virtual CPUs, called slots. Thus, the users have to pay only for what they utilize. The BigQuery pricing varies according to what it is used for such as cloud storage, data analytics, or others. 

BigQuery offers three models of pricing to choose from for data analysis.

Big Query On-Demand Pricing

This model charges according to the amount of data (number of bytes) processed with each query. The first 1TB of data processed each month will be free of charge. However, your queries will run on a shared pool of slots which may result in a varied performance.

Generally, you will have access to up to 2,000 slots which will be shared among the queries of a project. But this might vary depending on the demand for the on-demand capacity of your location. Therefore, the pricing may vary according to the location as well. For example, on-demand query pricing for 1TB in the U.S. is $5 whereas, in Sydney, it is $6.50.

Big Query Flat-Rate Pricing

With this model, you can purchase slots, which are dedicated processing capacities with which you can run your queries. This suits high-volume customers who prefer a fixed monthly cost. The following commitment plans are available with slots.

  • Flex slots: initial 60 seconds commitment

  • Monthly: initial 30 days commitment

  • Annual: 365 days commitment

BigQuery Free Tier

BigQuery also offers certain resources free of charge up to a certain limit. This might be during your free trial period and after. You will be charged according to your usage if you cross the given limit and are not in the free trial period.

Common BigQuery Use Cases 

Data transferring from multiple data sources

BigQuery facilitates data transferring from multiple data sources via the following methods, making it easier to analyze petabytes of data from multiple data sources.

  • Upload data files from local sources, cloud storage, or google drive.

  • Automate data movement using BigQuery Data Transfer Service (DTS). 

Migration of data warehouses

BigQuery provides free and fully managed data migration services which let you streamline the data migration path from Redshift, Netezza, Oracle, Snowflake, or Teradata.

  • Migrate the data of an existing data warehouse to BigQuery.

  • Utilize BigQuery's secure and reliable storage and access options.

Real-time analytics

Built-in streaming capabilities of BigQuery automatically ingest streaming data and make them immediately available for query. This will allow businesses to stay agile and respond to real-time business events.

  • Stream data in real-time with BigQuery Datastream.

  • Utilize BigQuery Real-Time Processing for real-time analytics.

Predictive analytics

BigQuery ML can do predictive analysis on customer data stored in it, which can be used to build e-commerce recommendation apps and predict customer lifetime value.

  • Apply BigQuery ML for faster and better predictive analytics.

  • Implement recommendations in your apps, websites, or emails.

Log Analysis

You can store, analyze, and get a deeper insight into data logged by the servers, IoT sensors, and other devices.

  • BigQuery is a low-cost storage option for bulk data logs.

  • Collect and analyze server logs and trends with BigQuery.

Marketing data warehouse

BigQuery's built-in ML and Google analytics together facilitate you to build marketing data warehouses which helps increase marketing performance by letting you deliver timely, targeted, and tailored advertising experiences.

  • Implement a 360-degree view of customers and prospects to automate ad targeting.

  • Build real-time BI dashboards for stakeholders and decision-makers.

Top BigQuery Data Integrations 

Data integration is the process of gathering datasets from multiple sources and combining them into a single cohesive view after cleaning and transforming them. This is a very complex workflow and is generally done on a scheduled basis. Choose the appropriate tools depending on their functionality, for data integration.

Here are some of the best Google BigQuery data integration tools to consider.

1) Portable

Portable is a top BigQuery data integration tool that simplifies the process of ingesting and processing data from various sources. It supports over 300 pre-built data connectors and the flexibility to develop custom connectors. With Portable, you can easily extract, transform, and load data into BigQuery and other data warehouses, allowing you to derive insights from your data quickly and efficiently.

Key features 

  • Portable support over 300 pre-built data connectors. This allows users to easily connect with various data sources.

  • Portable also offers the flexibility to create and maintain custom connectors. You can ask the Portable team to create connectors based on specific needs.

  • The Portable team takes care of all the maintenance, troubleshooting, alerting, and monitoring tasks, allowing you to focus on your core business objectives.


  • Free plan for manual data workflow with no caps.

  • Charges a flat fee of 200$ a month for automated workflows.

  • You have to contact them for enterprise requirements and SLAs and get a quotation. 


2) Connected Sheets

Connected sheets will let you connect the Google spreadsheets to BigQuery. Thus it lets you manage and analyze petabytes of data in the familiar context of Google Sheets with the power of BigQuery data warehouse.

Key features

  • Lets you connect Google sheets to BigQuery.

  • You can analyze petabytes of data without specialized knowledge of languages like SQL.

  • Allows you to work with familiar sheet tools such as pivot tables, formulas, and charts.


  • Personal use: Free

  • Business : 12$/user/month


3) Looker Studio

Looker Studio is an online tool that lets you explore your data, and build and consume data visualizations, dashboards, and reports.

Key features 

  • Lets you gather your data by easily connecting to over 800 data sources.

  • Lets you create and share engaging and meaningful data visualizations and reports.

  • Easy to use web interface.


  • Personal use: Free

  • Business: Will be charged on upgrade to Looker Studio Pro


4) Apache NiFi

Apache NiFi is a popular BigQuery data integration tool that enables users to efficiently extract, transform, and load data from a variety of sources into BigQuery. It features an intuitive interface, and drag-and-drop functionality, and supports over 200 data connectors. With Apache NiFi, you can streamline your data management process and gain valuable insights from your data quickly and easily.

Key features

  • Data provenance tracking.

  • Secure communication mechanisms.

  • Flexible scaling model.


  • Free


5) Stitch

Stitch rapidly moves data from over 130 sources into a warehouse to be explored. Stitch also offers an automated schema generation feature that can help users quickly generate and optimize data schemas. This feature can help streamline the data integration process and reduce the time and effort required for data modeling and analysis.

Key features

  • Let you track and control your data pipeline

  • Extendable with open source

  • Provides enterprise-grade security and compliance.


  • Standard: 100$/month (two months free on an annual plan)

  • Advanced: 1250$/month

  • Premium: 2500$/month


6) ODBC/JDBC Drivers 

Google in collaboration with Simba, provides these drivers in order to leverage the power of BigQuery's GoogleSQL.

Key features

  • Leverages the query interface of BigQuery.


  • Drivers can be downloaded for free

  • Queries run on the drivers will be charged accordingly.


ETL Connectors for Google BigQuery 

Extraction, Transformation, and Loading (ETL) is a critical process for working with big data. It involves using data connectors to extract data from different sources, transform it into a format that's suitable for analysis and reporting, and then load it into data warehouses or other repositories.

Here are some of the best ETL tools for BigQuery to load data from various data sources.

1) Portable

Portable is a powerful BigQuery ETL tool that provides seamless integration with Google BigQuery. Portable's e-commerce ETL capabilities make it easy to connect Salesforce Commerce Cloud to BigQuery. It also offers connectors for Snowflake, Redshift, and PostgreSQL. With no-code required, businesses can quickly and easily sync their e-commerce data into their warehouse, giving them access to valuable insights in minutes. Portable's embedded ETL feature eliminates the need for managing cumbersome scripts, simplifying the process of accessing and analyzing e-commerce data.

Key features 

  • Has more than 300 hard-to-find data sources that other ETL tools do not support.

  • Offers building custom connectors upon your request with a quick turnaround time.

  • Free maintenance.


  • Free plan for manual data workflow with no caps.

  • Charges a flat fee of 200$ a month for automated workflows.

  • You have to contact them for enterprise requirements and SLAs and get a quotation.


2) Google Dataflow

This ETL tool is a part of the Google Cloud Platform (GCP), which accepts pipelines built in Java or Python.

Key features 

  • Integrates with BigQuery and other GCP products.

  • Works for streaming and batch data.

  • Speeds up the development with a wide range of templates.


  • Users will be charged according to the amount of data processed, the region, job type, CPU, and memory.


3) Google Data Fusion

This is another GCP product that focuses more on simple integrations than complex ones.

Key features 

  • No code, GUI-based interface.

  • Pre-built transformations to get the pipelines operational faster.

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


  • Developer: 0.35$/instance/hour

  • Basic: 1.80$/instance/hour 

  • Enterprise: 4.20$/instance/hour


4) Google Cloud Composer 

Google Cloud Composer is a fully managed workflow orchestration that lets you create, monitor, schedule, and manage workflow pipelines.

Key features 

  • multi-clouds and on-premises sources.

  • The fully managed version of Apache Airflow.

  • Opensource.


  • Has a complex pricing model.


5) Apache Spark Dataproc

This is a fully managed and highly scalable service for running 30+ open-source tools and frameworks.

Key features 

  • Fully managed and automated open-source big data software.

  • Integrated enterprise security with google cloud.

  • Enables you to easily apply any open-source tool, algorithm, or programming language that could scale data sets.


  • Calculated based on the number of vCPUs and the duration that they run.


6) Hadoop 

Hadoop is an open-source platform for distributed storage and managing big data sets.

Key features 

  • Open-source.

  • Highly scalable cluster.

  • Provides fault tolerance.


  • Free


BigQuery Integration Best Practices

When it comes to integrating with BigQuery, you must maintain consistency and reliability of your data. Avoid downtime with these helpful BQ best practices.

Optimize join patterns and data types

For queries that join data from multiple tables, it's recommended to begin with the largest table and follow with the smaller tables. It's also important to use a consistent data format, such as JSON, CSV, Avro, ORC, or Parquet, to ensure that your data is reliable, consistent, and accurate when working with BigQuery.

Split complex SQL queries into smaller ones 

Complex SQL queries are slow and more resource-consuming. Also when they are split into smaller chunks, they result in intermediate results stored either in variables or temporary tables. These intermediate results are reusable and cost-effective, as you will not be charged for temporary tables.

Avoid transforming data via SQL

If you use SQL to perform ELT or ETL operations, it is good practice to avoid repeatedly transforming the same data as it requires additional computational overhead. These transformed data can be stored in a destination table. Also, you can use schemas to group transformations by type. 

Use a dedicated ETL tool

It is recommended to use a dedicated ETL tool, such as Portable, to ensure efficient and organized cloud-based data management and replication when integrating with BigQuery. This best practice can simplify the process of extracting, transforming, and loading data, and make it easier to maintain data quality and consistency.

BigQuery Alternatives 

BigQuery is a data warehouse platform offered by Google Cloud Platform. But if you're comparing BigQuery to others, here's the rundown.

Amazon Redshift 

Amazon Redshift is the first cloud data warehouse. BigQuery separates storage and computation, while Redshift is confined to RA3 instances and lacks workload isolation. Additionally, BigQuery is supported by Google Cloud, while Redshift is supported by AWS.

Amazon Athena 

BigQuery and Athena are both serverless platforms. BigQuery allows for native tables and external storage options to be used as data sources. Meanwhile, AWS Athena solely supports Amazon S3, necessitating data ingestion to that storage service for use with Athena.


Snowflake has a much easier-to-use web UI than that of BigQuery. Snowflake data warehouse provides complete automation. Snowflake has native integration to the most popular CRM, Salesforce.

Microsoft Azure Synapse Analytics 

Microsoft Azure Synapse Analytics is a data analytics service offered by Microsoft that differs from BigQuery in several ways. Firstly, it uses SQL as its primary query language, making it an attractive option for businesses already familiar with SQL. Secondly, Synapse Analytics provides built-in data integration capabilities that simplify the process of ingesting and analyzing data. Overall, it provides a robust and scalable solution for businesses looking to derive insights from their data.

Engineering the Modern Data Stack

A data stack is a set of technologies and services an organization uses to handle data. A modern data stack should include the following aspects.

Data ingestion 

In data ingestion, you must gather data from different data sources. These sources can be SaaS apps, BI platforms, MySQL servers, etc. Then you load data to centralized storage.

For this purpose, you must use a connector connecting different data sources. When choosing a connector, opting for a low-code integration is better.

Data processing 

Data processing is the process of collecting raw data and transforming them into a usable format, which is also known as data engineering. This is the part where ETL and ELT tools come into play. They extract the data via their APIs, transform them, and load them to a repository like a warehouse.

There is a debate between ETL vs. ELT.

The key difference between the two is that ELT sends data directly to the data warehouse and performs transformations inside the warehouse. In contrast, ETL performs transformations without sending raw data directly to the warehouse. 

Nevertheless, automating the ETL process assures continuous data synchronization --- plus it saves you on computing resources.

Scalable by design 

Cloud data warehouses are hosted in the cloud, whereas on-premises warehouses are located locally. Thus, the modern data stack no longer depends on hardware like in on-premises warehouses.

Data-driven business value 

In data-driven businesses, all business decisions are backed by its data. Its embedded analytics apps allow businesses to generate real-time business intelligence to improve performance.

Streamline BigQuery Data Integration With Portable

You've got to check out Portable. See how easy it is to get your data from 300+ data sources into your preferred data warehouse. Take advantage of Portable's unlimited data volumes for your next data project.