How to load data from Excel File to BigQuery

Learn how to use Airbyte to synchronize your Excel File data into BigQuery within minutes.

Trusted by data-driven companies

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Excel File connector in Airbyte

Connect to Excel File or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Excel File data

Select BigQuery where you want to import data from your Excel File source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Excel File to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that supports both incremental and full refreshes, for databases of any size.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Jean-Mathieu Saponaro
Data & Analytics Senior Eng Manager

"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"

Learn more
Chase Zieman headshot
Chase Zieman
Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Learn more
Alexis Weill
Data Lead

“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria.
The value of being able to scale and execute at a high level by maximizing resources is immense”

Learn more

How to Sync Excel File to BigQuery Manually

  1. Open your Excel file: Ensure your data is clean and formatted correctly. The first row should contain column headers, which will become the field names in BigQuery.
  2. Save as CSV: BigQuery does not directly import Excel files (.xlsx or .xls), so you must save your Excel file as a CSV (Comma Separated Values) file. Click File > Save As and choose CSV (Comma delimited) (*.csv) from the file type dropdown menu.
  1. Create a Google Cloud Project: If you haven’t already, create a new project in the Google Cloud Console at https://console.cloud.google.com/.
  2. Enable BigQuery API: Navigate to the API & Services dashboard and enable the BigQuery API for your project.
  3. Install Google Cloud SDK: Download and install the Google Cloud SDK from https://cloud.google.com/sdk/docs/install. This will be used to authenticate and interact with your Google Cloud resources.
  1. Initialize the SDK: Open a command-line interface (CLI) and run gcloud init to authenticate and set up your Google Cloud environment.
  2. Login to your account: Follow the prompts to log in to your Google account that has access to the Google Cloud project.
  1. Create a dataset: In the Google Cloud Console, navigate to BigQuery and create a new dataset by clicking on Create dataset.
  2. Create a table schema: Define the schema for your table based on the data in your CSV file. You can do this manually in the console when creating a table or programmatically using a schema definition file.
  1. Create a storage bucket: In the Google Cloud Console, go to the Cloud Storage browser and create a new bucket where you will upload your CSV file.
  2. Upload the CSV file: Click on the newly created bucket and upload your CSV file by dragging and dropping it into the browser window or using the Upload files button.
  1. Navigate to your dataset: In the BigQuery interface, select the dataset where you want to import your data.
  2. Create a new table: Click on Create Table. In the source section, set the location to Google Cloud Storage and select the CSV file you uploaded.
  3. Configure the import settings: Choose the file format as CSV. Make sure to check Auto-detect for schema and input parameters if you want BigQuery to automatically detect your schema. Otherwise, specify the schema manually.
  4. Start the import: Click Create Table to start the import process. BigQuery will import the data from the CSV file into your new table.
  1. Check the table: After the import process is complete, you should see your new table in the BigQuery interface with the data from your CSV file.
  2. Run a query: To verify that the data has been imported correctly, run a simple SQL query against your table, such as SELECT * FROM your_dataset.your_table LIMIT 10;.
  1. Delete the CSV from Cloud Storage: To avoid incurring storage charges, delete the CSV file from your Cloud Storage bucket after confirming the data import.
  2. Remove any temporary datasets/tables: If you created any temporary datasets or tables during this process, consider removing them to avoid additional costs.

How to Sync Excel File to BigQuery Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

Excel File is a software application developed by Microsoft that allows users to create, edit, and analyze spreadsheets. It is widely used in businesses, schools, and personal finance to organize and manipulate data. Excel File offers a range of features including formulas, charts, graphs, and pivot tables that enable users to perform complex calculations and data analysis. It also allows users to collaborate on spreadsheets in real-time and share them with others. Excel File is available on multiple platforms including Windows, Mac, and mobile devices, making it a versatile tool for data management and analysis.

The Excel File provides access to a wide range of data types, including:  
• Workbook data: This includes information about the workbook itself, such as its name, author, and creation date.  
• Worksheet data: This includes data about individual worksheets within the workbook, such as their names, positions, and formatting.  
• Cell data: This includes information about individual cells within the worksheets, such as their values, formulas, and formatting.  
• Chart data: This includes data about any charts that are included in the workbook, such as their types, data sources, and formatting.  
• Pivot table data: This includes information about any pivot tables that are included in the workbook, such as their data sources, fields, and formatting.
• Macro data: This includes information about any macros that are included in the workbook, such as their names, code, and security settings.  

Overall, the Excel File's API provides developers with a comprehensive set of tools for accessing and manipulating data within Excel workbooks, making it a powerful tool for data analysis and management.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Excel File to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Excel File to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter