How to Load Data from GCS to BigQuery: Full Tutorial
Cloud storage and data warehousing are a powerful combination. Learn the best methods for moving data from GCS to BigQuery

As data is the backbone of any analytics project, managing it efficiently is essential for deriving meaningful insights. Google Cloud Platform provides the necessary support for storing and analyzing large datasets at scale. It offers two robust services: Google Cloud Storage (GCS) and BigQuery. GCS provides a highly durable object storage service, while BigQuery offers a fully managed, cost-effective data warehouse solution.

In this guide, we will explore how to load data from GCS to BigQuery using various methods. Whether you are a data analyst or a software developer, this guide will provide a step-by-step path to loading data from GCS to BigQuery. Let's dive in!

Google Cloud Storage Overview

Blog Post Image

Google Cloud Storage (GCS) is an object storage service provided by Google Cloud Platform (GCP). It allows you to store and access data in the cloud using a simple interface. You can store various data types, including unstructured data such as images, videos, audio files, and documents. With GCS, you can easily integrate your data storage with other Google Cloud services, such as BigQuery and Cloud Machine Learning Engine, enabling you to build powerful analytics and machine learning applications.

Google Cloud Storage offers the following storage options:

Standard Storage: Designed for frequently accessed data, also known as hot data. This storage option is suitable for data that needs to be accessed frequently and stored for only brief periods.

Nearline Storage: A low-cost storage option for infrequently accessed data that needs to be stored for at least 30 days. It provides a highly durable storage service with access times in seconds.

Coldline Storage: A storage option for infrequently accessed data that can be stored for at least 90 days. It is a very low-cost storage option designed for data that is rarely accessed but still needs to be preserved for a certain period.

Archival Storage: The most cost-effective storage option for data archiving, online backup, and disaster recovery. It provides highly durable data storage for up to 365 days. This storage option is best suited for data that is not frequently accessed but needs to be preserved for long-term retention.

BigQuery Overview

Blog Post Image

BigQuery is a serverless, cloud-native data warehouse solution from the Google Cloud Platform that enables you to analyze massive datasets easily. With BigQuery, you can perform complex SQL-like queries on petabytes of data in seconds, making it ideal for organizations that need to quickly process large volumes of data. BigQuery also integrates seamlessly with other Google Cloud services, such as Cloud Storage, Dataflow, and Dataproc, allowing you to build end-to-end data pipelines.

Here are some key features of BigQuery:

Real-Time Data Analysis: BigQuery supports real-time data analysis, enabling you to analyze data as it streams into the platform. This feature allows you to make decisions based on current data and respond quickly to dynamic needs.

Machine Learning: BigQuery provides a built-in machine learning service called BigQuery ML that enables you to perform basic machine learning tasks directly within BigQuery. Additionally, it integrates with Google Cloud AI platform, allowing you to build and deploy advanced machine learning models.

Powerful Visualization: BigQuery's integration with Power BI and Google Data Studio helps you create stunning visualizations for obtaining insights.

Advanced SQL Support: BigQuery supports standard SQL, making it easy to query and analyze data using familiar SQL syntax. It also supports advanced SQL features such as nested queries, and user-defined functions.

Secure Data Storage: BigQuery is built on Google's secure infrastructure. It offers advanced security features such as encryption at rest/transit, access controls, and auditing logs.

Why Load Data from GCS to BigQuery?

Loading data from Google Cloud Storage (GCS) to BigQuery offers several benefits. Firstly, GCS provides a scalable solution for storing large amounts of data in the cloud, making it an ideal data source for BigQuery. Additionally, GCS allows for easy data integration with other Google Cloud services, such as Cloud Functions and Dataflow, which you can use to transform and process data before loading it into BigQuery.

BigQuery is specifically designed to handle large datasets and perform fast queries, making it a powerful tool for data analysis. By loading data from GCS into BigQuery, you can take advantage of BigQuery’s built-in machine learning capabilities, real-time analytics with streaming data pipelines, and geospatial analysis features.

Therefore, loading data from GCS to BigQuery provides a scalable and cost-effective solution for analyzing large datasets with powerful querying and analysis capabilities, making it a popular choice for many organizations.

Methods to Load GCS Data to BigQuery

There are several approaches to move data from Google Cloud Storage to BigQuery. In this guide, we'll explore popular methods for loading data from GCS to BigQuery.

  • Method 1Using BigQuery’s Native Integration Capabilities
  • Method 2: Using SaaS Alternatives Like Estuary

Method 1: Using BigQuery’s Native Integration Capabilities

Natively loading GCS (Google Cloud Storage) data to BigQuery is a process where you upload files from GCS to BigQuery using the Google BigQuery web UI.

Here are the steps to load GCS data to BigQuery using Google’s native tools:

Step 1: Log in to your Google Cloud Storage account. If you haven't already uploaded your data to GCS, create a new cloud bucket and upload the data.

Step 2: Sign in to Google BigQuery account. Select the Data Transfers menu on the left side and click on Create Transfer.

Blog Post Image

Step 3: To upload data from GCS to BigQuery, follow these simple steps:

  • Source Type: Choose "Google Cloud Storage" from the drop-down list.
  • Transfer Config Name: Enter a name for the transfer configuration to be displayed.
  • Schedule Options: Set the Schedule options according to your needs, fill in the details like Repeat frequency, Start date, and run time.
Blog Post Image
  • Destination Settings: Select the destination for the transfer data. Create a new dataset or use an existing one.
  • Data Source Details: Here you have to specify the destination table and your cloud storage URL. Choose the write preference and file format from the drop-down list.
Blog Post Image
  • Make sure to specify your Transfer Options, then click on Save. Your Google Cloud Storage data has been successfully transferred to Google BigQuery.

However, there are a few limitations with the native integration capabilities. 

  • If the GCS data is in a format that is not directly compatible with BigQuery, additional pre-processing steps or transformations are necessary to ensure successful loading and querying of the data.
  • You’ll have to repeat the process to keep the data in BigQuery up to date. Although GCS allows you to schedule repeat jobs, they’re executed in batches, so there will be a delay between when new data arrives in GCS and when it’s available in BigQuery. 

Method 2: Using SaaS Alternatives Like Estuary

Estuary is a cloud-based data pipeline platform that allows you to move your data between different sources and destinations, including Google Cloud Storage (GCS) and BigQuery. It provides an easy-to-use interface for setting up and managing data pipelines.

Using SaaS alternatives like Estuary for moving data from GCS to BigQuery can offer several advantages. 

  • Flow makes sure data is in a format that’s accessible by the destination system (in this case, BigQuery). It does this with data quality checks, schema validation, and transformations.
  • Flow data pipelines move data in real time, so any data changes that happen in GCS will be reflected in BigQuery immediately.
  • Flow provides a central dashboard for monitoring the transfer process, which helps quickly identify and resolve any issues. 

Let's explore the step-by-step process in detail.

Step 1: Capture the Data From Your Source

  • Sign in to your Estuary account or sign up for free. Once you’ve logged in, click on Capture.
Blog Post Image
  • In the capture window, Click on + New Capture.
Blog Post Image
  • On the Captures page, search for Google Cloud Storage and click on Capture.
Blog Post Image
  • Give the Capture a name. Fill in the details of your source database, Such as Google Service Account, Google Cloud Storage Bucket, and Match Keys. 
Blog Post Image
  • Once you have filled in all the details, click on Next. Flow will initiate a connection with your Google Cloud Storage account and identify data tables.
  • Click Save and Publish.

Step 2: Set Up Your Data Destination

  • To initiate the materialization process, you have two options. First, you can click on Materialize Connections in the pop-up window. Alternatively, you can navigate to the Estuary dashboard and locate the Materializations option in the left-side pane. Click on New Materialization and Search for Google BigQuery. Now, click on Materialization.
Blog Post Image
  • BigQuery has some prerequisites you’ll need to meet before you can connect to Flow successfully. So before you continue, follow the steps here
  • Provide the Materialization name and Endpoint config details such as Google Cloud Project ID, Service Account, and Region. Click on Next.
Blog Post Image

Benefits of Using Estuary Flow

Using SaaS alternatives like Estuary to load data from Google Cloud Storage (GCS) to BigQuery has several benefits over other methods.

Here are some of them:

Pre-Built Connectors: Flow offers a suite of pre-built connectors designed for diverse data sources and destinations. This extensive collection simplifies the integration process with various systems, providing seamless connectivity.

Easy-to-Use Interface: Estuary Flow provides a user-friendly interface allowing you to easily set up data transfer configurations. You can simply connect your GCS and BigQuery accounts, choose the data you want to transfer, and set up the transfer configuration in just a few clicks.

Scalability: Estuary Flow is designed to handle large volumes of data, It can handle active workloads at up to 7GB/s change data capture (CDC) from databases of any size.

Cost Effective: Estuary Flow is often more cost-effective than building custom transfer configurations. Flow offers a pay-as-you-go model, where you only pay for the data transferred.


Efficiently managing data is crucial for any organization to gain valuable insights and make informed decisions. Google Cloud Platform offers powerful services like Google Cloud Storage (GCS) and BigQuery that can help you store and analyze massive datasets quickly and cost-effectively.

By loading data from GCS to BigQuery, you can use its advanced features and capabilities, including real-time analytics, machine learning, and powerful visualization tools. Depending on your specific needs, you have the flexibility to choose between using Native Integration Capabilities or opting for SaaS alternatives like Estuary. With the right approach, you can unlock the full potential of your data and drive your business forward.

Give it a quick try to replicate your GCS data to BigQuery in real time — you can start using Flow for free!