Load Data from Excel to BigQuery: 5 Easy Methods

Hevo automates your Excel to BigQuery data migration with no-code pipelines, real-time syncing, and full pipeline visibility for reliable performance.

Try Hevo Free

Key Takeaways

• Excel to BigQuery helps move spreadsheet data into a scalable platform for faster analytics and reporting.

• Top 5 methods:

1. CSV Upload – Simple, best for small files

2. BigQuery API – Automated, needs coding

3.Hevo Data – No-code, real-time pipelines

4. DataPrep – Visual data cleaning before loading

5. Cloud Storage – Best for large, batch uploads

• Quick tip:

  Use CSV for quick tasks, API/Cloud Storage for scale, and Hevo for full automation.

Excel is the world’s most widely used spreadsheet tool, but it was never built for the scale and speed that modern data analysis demands. As of 2026, 203,913 verified companies use Microsoft Excel, yet most organizations eventually hit a wall when their datasets grow too large, their queries run too slowly, or they need a single source of truth across teams.

That’s where Google BigQuery comes in. Moving your Excel data into BigQuery lets you run lightning-fast SQL queries across terabytes of data, connect to BigQuery data visualization tools like Looker and Tableau, and build automated analytics pipelines that go far beyond what spreadsheets can support.

This guide walks you through five practical methods to load data from Excel to BigQuery, from a simple manual CSV upload to fully automated pipelines, so you can choose the approach that fits your workflow.

Note: It’s important to check for data quality issues and adjust your BigQuery table schema accordingly before you attempt to upload Excel to BigQuery. When considering how to connect BigQuery to Excel, understand that you’ll primarily pull data from BigQuery into Excel spreadsheets, not push from Excel to BigQuery.

Why do You Need to Connect Excel to BigQuery?

Excel is a powerful tool for individual analysis, but it runs into real limits when used as a data infrastructure backbone. BigQuery eliminates those limits at every level.

Here’s a direct comparison of where Excel falls short  and how BigQuery addresses it:

Beyond overcoming these limitations, moving data from Excel to BigQuery enables several high-value use cases:

• Reporting and Visualization: While Excel File provides reporting tools, data visualization tools like Tableau, PowerBI, and Looker (Google Data Studio) can link with BigQuery, providing more advanced business intelligence options. If you need to convert an Excel File table to a BigQuery table, Airbyte can do it automatically.
• Advanced Analytics: BigQuery’s robust processing capabilities let you run complex queries and multi-table joins on your Excel data, the kind of analysis that would time out or crash in a spreadsheet. You can also apply BigQuery ETL transformations to prepare your data for downstream use.
• Data Consolidation:  If you’re pulling from multiple sources alongside Excel files, syncing them with BigQuery gives you a single, centralized view. For example, teams that move HubSpot to BigQuery alongside their Excel data can analyze sales and spreadsheet data together in one place. This pairs well with BigQuery ETL tools that automate the movement and transformation of data from many systems at once.
• Data Security and Compliance: BigQuery boasts robust data security features. By syncing Excel File data to BigQuery, you can ensure your data is secure and set up advanced data governance and compliance management.
• Scalability:  BigQuery can accommodate large volumes of data without impacting performance, making it an ideal solution for growing businesses with expanding datasets. If you’re evaluating whether BigQuery is the right platform for your scale, it helps to understand how it compares to alternatives. See our breakdown of Databricks vs BigQuery for a detailed comparison.

What are the Methods to Import Excel to BigQuery?

Before diving in, here’s a quick comparison of all five methods to help you pick the right one:

• Method 1: Load Data from Excel to BigQuery Using CSV
• Method 2: Upload XLSX to BigQuery Using BigQuery API
• Method 3: Load Data from Excel to BigQuery Using Hevo Data
• Method 4: Load Data from Excel to BigQuery Using DataPrep
• Method 5: Load Data from Excel to BigQuery Using Cloud Storage

Method 1: Load Data from Excel to BigQuery Using CSV

Converting your Excel file to CSV and loading it via the BigQuery console is the most straightforward approach. It requires no code and works well for one-time or infrequent imports of smaller datasets.

The steps to upload an Excel file to BigQuery using CSV are listed below:

• Go to your Web console and click “Create table”, then “Create a table from”.
• Specify the CSV file that will act as a source for your new table.

Image source

• In the “Source” dropdown, select your file or Cloud Storage location. Under “File format”, select CSV.
• Select a dataset and give your table a name.
• Either upload a sample JSON to define the schema, or leave it set to “auto-detect”.

Image source

• You can configure additional parameters like field delimiter, skip header rows, number of errors allowed, and jagged rows.
• Click “Create Table.” BigQuery will fetch the CSV, detect the schema, create the table, and populate it with your data.

For a deeper look at this approach, see our guide on CSV to BigQuery.

Limitations

Some limitations of using CSV to connect Excel to Google BigQuery are listed below:

• When you load files from a local data source, they must be loaded individually. Wildcards and multiple selections are not allowed. 
• Excel files loaded from a local data source must be 10 MB or less and must contain fewer than 16,000 rows.

Pro Tip: Unify all data across channels to understand your customer journey and generate actionable insights affecting different sales funnel stages.

Load Data from Google Sheets to BigQuery

Get a DemoTry it

Load Data from Google Ads to BigQuery

Get a DemoTry it

Load Data from MongoDB to BigQuery

Get a DemoTry it

Load Data from Salesforce to BigQuery

Get a DemoTry it

Method 2: Upload XLSX to BigQuery Using BigQuery API 

The BigQuery API is the right choice if you’re a developer who needs to automate uploads, integrate Excel data loading into an existing application, or handle files programmatically at scale.

The API supports two upload approaches depending on your file size:

• Multipart Upload is designed for smaller files. Simple to implement, but if the upload fails, it starts over from the beginning.
• Resumable Upload is best for larger files. You create an upload session and can resume from the point of interruption if something goes wrong, making it more reliable for big datasets.

The trade-off with the API approach is that it requires developer resources to implement and maintain. As your data structures evolve, you’ll need to update your integration code accordingly. For teams without that bandwidth, a managed pipeline tool like Hevo handles this automatically.

Method 3: Load Data from Excel to BigQuery Using Hevo Data

For teams that move Excel data into BigQuery regularly or who pull from multiple files across different storage locations, Hevo is the most practical solution. Rather than converting files manually or writing API code, Hevo connects directly to your Excel or CSV files stored in sources like Amazon S3, FTP/SFTP, Google Drive, and Box, and handles the entire pipeline automatically.

This is especially useful when working with recurring exports, multi-sheet workbooks, or data that needs transformation before it’s ready for analysis. Hevo lets you apply transformations and preprocessing steps to your Excel data before it lands in BigQuery, no custom scripts needed. If you’re thinking about a broader BigQuery ETL strategy, Hevo fits naturally into that workflow alongside other data sources.

Key features of Hevo Data:

• Simple & Fully Managed: No infrastructure to maintain. Hevo handles setup, monitoring, and updates automatically.
• Data Transformation: A simple interface to clean, enrich, and reshape your data before it reaches BigQuery.
• Real-Time Syncing: Hevo offers real-time data migration. Data is always current and ready for BigQuery analysis without manual refreshes.
• Schema Management: Automatically detects incoming schema and maps it to your destination table.
• Live Monitoring: A single dashboard to track pipeline activity, errors, and data flow in real time.
• Live Support: Available 24/7 via chat, email, and support calls.
• Reliable & Scalable: Built with fault-tolerant architecture, automated scaling, and resilient pipelines to ensure uninterrupted data flow.
• Transparent & Trustworthy: Offers end-to-end visibility, real-time monitoring, and predictable pricing for complete operational clarity.

Most of the limitations described in the other methods don’t apply when using Hevo; it’s designed to handle the edge cases that manual approaches can’t.

Method 4: Load Data from Excel to BigQuery Using DataPrep 

DataPrep (Google Cloud DataPrep) is a cloud-based service for visually exploring, cleaning, and preparing data before loading it into BigQuery. It’s a good fit when your Excel data needs transformation work, fixing inconsistencies, filtering rows, and renaming columns that go beyond what the BigQuery console can handle on import.

Steps to load data from Excel to BigQuery using DataPrep:

Step 1: Import Data in DataPrep

• On the DataPrep console, open the Library page and click “Import Data”.
• Select the Excel workbook you want to import. By default, all worksheets are imported as individual datasets. Use the EDIT option to merge them into a single dataset if needed.
• After adding the dataset, you can update its name and description.

Step 2: Load Data from DataPrep to BigQuery

• If transformations are needed, define a recipe in DataPrep that applies to your imported datasets.
• In the left navigation bar, click the Jobs icon to open the Jobs Definition page.
• Click the job identifier to open the Job Details page, then click the Output Destinations tab and select BigQuery as the destination.

Image source

Limitations 

Some limitations of using DataPrep for loading data from Excel to BigQuery are listed below:

• If your data in Excel cells has quotes, be very particular to have matching terminating quotes, else it could lead to undefined results. 
• Compressed and Password protected files are not supported.
• Object and Array data types in DataPrep, are written back to BigQuery as string values. Hence, try not to make them nested, in the source Excel files. 
• BigQuery does not support destinations with a dot (.) in the name. 

Solve your data replication problems with Hevo’s reliable, no-code, automated pipelines with 150+ connectors.

Get your free trial right away!

Method 5: Load Data from Excel to BigQuery Using Cloud Storage

This method works well for batch processing or when you’re dealing with files too large for a direct console upload. The workflow is: convert your Excel file to CSV, upload it to a Google Cloud Storage (GCS) bucket, then load it into BigQuery.

You have three main options for the final load step:

Option 1: Using the bq load command via the command line

First, copy your CSV to a GCS bucket:

gsutil cp path/file_name.csv gs://bucket_name/

Then load it into BigQuery:

bq load –source_format=CSV dataset_name.table_name gs://bucket_name/file_name.csv

You can use bq show to check the status of the load job and verify the table schema:

bq show test-applications-315905:test_dataset.user_details_json

Image source

Option 2: Using the Cloud Console / WebUI

Upload your CSV through the BigQuery web interface and optionally provide a JSON schema definition file to control how columns are mapped.

Option 3: Using the jobs. insert API or client libraries

For teams that want to integrate this into a pipeline or application, Google provides client libraries for Python, Java, C#, Node.js, and more. Here’s a Python example:

from google.oauth2 import service_account
from google.cloud import bigquery
 
# Create Authentication Credentials
project_id = "test-applications-xxxxx"
table_id = f"{project_id}.test_dataset.user_details_python_csv"
gcp_credentials = service_account.Credentials.from_service_account_file('test-applications-xxxxx-74dxxxxx.json')
 
# Create BigQuery Client
bq_client = bigquery.Client(credentials=gcp_credentials)
 
# Create Table Schema
job_config = bigquery.LoadJobConfig(
    schema=[
        bigquery.SchemaField("user_id", "INTEGER"),
        bigquery.SchemaField("first_name", "STRING"),
        bigquery.SchemaField("last_name", "STRING"),
        bigquery.SchemaField("age", "INTEGER"),
        bigquery.SchemaField("address", "STRING"),
    ],
    skip_leading_rows=1,
    source_format=bigquery.SourceFormat.CSV,
)
 
# CSV File Location (Cloud Storage Bucket)
uri = "https://storage.cloud.google.com/test_python_functions/user_details.csv"
 
# Create the Job
csv_load_job = bq_client.load_table_from_uri(
uri, table_id, job_config=job_config
)
 
csv_load_job.result()

• Using client libraries (custom programming) for Java/Python/C#/NodeJS etc. 

Limitations

Some limitations to take care of would be:- 

• All your data must be singular values; nested or repeated data is not supported. 
• You cannot use both uncompressed and compressed files together in a single load job. 
• Your DATE columns must have the “-” separator only, and the only supported format is YYYY-MM-DD (year-month-day). 
• Same for TIMESTAMP, additionally, the hh:mm: ss (hour-minute-second) portion of the timestamp must use a colon (:) separator. 

Learn more about – How to import Excel into MySQL

Move your Sheets/Excel data to BigQuery using Hevo

Start For Free

No credit card required

Additional Resources on Excel to Bigquery

• Move Data from Google Sheets to Bigquery
• Load Data from CSV to Bigquery
• Import Excel Data into PostgreSQL
• Connect AfterShip to BigQuery

Choose the Right Excel to BigQuery Method 

In this blog, you have learned about Excel and Google BigQuery. You also learned about five different approaches to load data from Excel to BigQuery. You can use any of the stated methods according to your requirements and business needs. All the methods encounter some limitations.

So, if you are looking for a fully automated solution to load data from Excel to BigQuery, then try Hevo.Hevo is a No-code Data Pipeline. It supports pre-built integration from 150+ data sources. Hevo provides you with a completely automated solution within minutes. Sign up for a 14-day free trial with Hevo and experience seamless data integration.

FAQ on Excel to BigQuery

Pratik Dwivedi Technical Content Writer, Hevo Data

Pratik Dwivedi is a seasoned expert in data analytics, machine learning, AI, big data, and business intelligence. With over 18 years of experience in system analysis, design, and implementation, including 8 years in a Techno-Managerial role, he has successfully managed international clients and led teams on various projects. Pratik is passionate about creating engaging content that educates and inspires, leveraging his extensive technical and managerial expertise.

Dimple M K Customer Experience Engineer, Hevo Data

Dimple is an experienced Customer Experience Engineer with four years of industry proficiency, including the last two years at Hevo, where she has significantly refined customer experiences within the innovative data integration platform. She is skilled in computer science, databases, Java, and management. Dimple holds a B.Tech in Computer Science and excels in delivering exceptional consulting services. Her contributions have greatly enhanced customer satisfaction and operational efficiency.