Databases support CSV files on a regular basis. BigQuery, Google’s data warehouse as a service, combines data storage and analytics in one package. BigQuery allows you to conduct real-time SQL queries on billions of records. Stakeholders are always looking, to find faster and better ways, to get all their data from all their sources, into BigQuery.

A very popular and semantically easy data format these days is CSV. It can store data from databases, clickstreams, browsing trails, social media interactions, page views, and a multitude of sources. In this article, you will see 4 ways to move data from CSV to BigQuery. Read along to select the method that works best for your business!

What is BigQuery?

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Google offers BigQuery, a fully managed Cloud data warehousing platform. It is built on Google’s renowned Dremel Engine. Since it is built on a serverless model, BigQuery provides a high level of abstraction. It is a fully managed warehouse, so businesses do not need to maintain any form of physical infrastructure or database administrators. Users of BigQuery can choose to only pay for the queries they run thanks to its pay-as-you-go pricing model. It is also quite affordable because you only pay for the queries you run.

You may concentrate all of your labor and effort on significant business objectives because there is no physical infrastructure to manage and maintain, as there is in conventional server rooms. Traditional SQL allows you to do complicated queries from several users simultaneously while precisely inspecting your data.

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Key Features of BigQuery

  • Storage: You can be sure that it will automatically scale in response to shifting data requirements by saying “scaling on demand.” Thanks to this system’s Colossus (Google Global Storage System) base and columnar data storage, users can work immediately on compressed data without having to decompress files on the fly.
  • Real-time Analytics: As Google BigQuery distributes any quantity of resources in the optimal method to achieve the best performance and outcomes, you may produce business reports as needed while staying up to date with real-time data transfers and speedier analytics.
  • ML Capabilities: Using conventional SQL commands, you can design and build data models with machine learning capabilities using Google BigQuery ML. This makes less of a need for technical machine learning expertise and makes it possible for your data analysts to assess ML models directly.

Methods to Load Data from CSV to BigQuery 

You can upload CSV to Bigquery using any of the following methods:

Method 1: CSV to BigQuery Using Hevo Data

Hevo Data, a No-code Data Pipeline, helps you automate the CSV to BigQuery data transfer process in a completely hassle-free & automated manner. Hevo is the only real-time ELT No-code data pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. Hevo lends itself well to any data cleansing, pre-processing, and transformations before loading them to your data source.

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If your data is in CSV files, you may upload it to Google Drive or Amazon S3, two file-based sources that Hevo interacts with, and then use that as the Source when creating a pipeline. Hevo may import data from the following sources that are already in CSV format:

If your data is in CSV files, you may upload it to Google Drive or Amazon S3, two file-based sources that Hevo interacts with, and then use that as the Source when creating a pipeline.

Let’s consider Google Drive for example:

To set up Google Drive as a Source in your Pipeline, follow these steps:

Step 1: Configure Drive Source

  • Navigate to the PIPELINES section and click.
  • In the Pipelines List View, click + CREATE.
  • Choose Google Drive from the Select Source Type page.
  • Enter Pipeline Name and Folders on the Configure your Drive Source page.
  • Press the CONTINUE button.
  • Set up the destination and continue customizing the data ingestion.

Hevo may import data from the following sources that are already in CSV format:

Next step to bigquery load data from CSV is to Configure Google BigQuery as a Destination

Step 2: Setting up BigQuery Destination

Once the Destination is established, you may only change a portion of the settings you provide here. See Changing the BigQuery Destination Configuration section.

  • In the Navigation Bar, choose DESTINATIONS.
  • In the Destinations List View, click + CREATE.
  • Choose Google BigQuery as the destination type on the Add Destination screen.
  • Specify the Destination Name and Account on the Configure your Google BigQuery Warehouse page.
  • On TEST CONNECTION, click.
  • Select “Save & Continue.

Read more on configuring Bigquery from Hevo’s Google BigQuery documentation.

Check out what makes Hevo amazing:

  • Auto Schema Mapping: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming CSV data and maps it to the BigQuery schema.
  • Transformations: Hevo provides preload transformations through Python code. Hevo does this bit for you on its own and converts the data encoding to UTF-8 thus allowing you a hassle-free data transfer.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
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With continuous Real-Time data movement, Hevo allows you to combine your data from multiple data sources and seamlessly load it to BigQuery with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!

Method 2: CSV to BigQuery Using the Command Line Interface

The bq load command creates or updates a table and loads data in a single step. 

E.g. Assuming you have a dataset named mydb and there exists a table named mytable in it.

bq load mydb.mytable mysource.txt name:string,count:integer

Explanation of the bq load command arguments:

datasetID: mydb 
tableID: mytable
source: mysource.txt: [if necessary, include the full path to the file] 
schema: name:string, count:integer ..... [Repeat for all columns in the CSV to be mapped into Bigquery columns]

To check if the table has been populated, you can run the following command:

bq show  mydb.mytable

Sample output will be: 

  Last modified         Schema            Total Rows       Total Bytes     Expiration
 -----------------           -------------------     -------------------    ------------
  22 Aug 15:31:00    |- name: string       352                    6456                     -- 
                                |- count: integer

The manual process has the obvious limitations of scalability, portability, and susceptibility to error.

Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Learn the best practices and considerations for setting up high-performance ETL to BigQuery

Method 3: CSV to BigQuery Using the BigQuery Web UI

You can make use of the simple Web UI of BigQuery and load CSV data using the following steps:

  • You can go to your Web console and click “Create table” and then “Create table from”.
CSV to BigQuery Create Table
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  • Next, you can specify the CSV file, which will act as a source for your new table.
  • The “Source” dropdown will let you select amongst many sources like Cloud storage.
CSV to BigQuery schema
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  • In “File format”, select CSV.
  • Then select a database and give your table a name.
  • You can either upload a sample JSON to specify the schema or leave the schema definition to “auto-detect”.
  • Some other configurable parameters are field delimiter/skip header rows/number of errors allowed/jagged rows etc.
  • Clicking on “Create Table” will now fetch your CSV, ascertain the schema, create the table, and populate it with the CSV data.

Method 4: CSV to BigQuery Using the Web API

A full discussion on the coding to import data to BigQuery from CSV beyond the scope of this article, but broadly speaking, your steps would be as follows:-

  • Specify the source URL, dataset name, destination table name, etc. 
  • Initialize the client that will be used to send requests(can be reused for multiple requests). 
  • Specify the “Load Job configuration“, and make sure you do not miss essential format options. 
  • Load the table using API commands, this load job will block until the table is successfully created and loaded or an error occurs. 
  • Check if the job was successfully completed or if there were some errors. 
  • Throw appropriate error messages, make changes, and retry the process. 

This is the most configurable and flexible option, but also the most error-prone and susceptible to maintenance whenever the source or destination schema changes.
Your program will need some time-tested trials to mature. 

Limitations of Moving Data from CSV to BigQuery

  • Nesting and repetitive data are not supported in CSV files.
  • BOM (byte order mark) characters should be removed. They may result in unanticipated consequences.
  • BigQuery will not be able to read the data in parallel if you use gzip compression. It takes longer to import compressed CSV data into BigQuery than it does to load uncompressed data. See Loading compressed and uncompressed data for further information.
  • You can’t use the same load job to load compressed and uncompressed files.
  • A gzip file can be up to 4 GB in size.

Why Move Data from CSV to BigQuery?

BigQuery upload CSV not only simplifies data management but also enhances the overall efficiency of your analytical workflows. Analyzing and handling a large amount of data can be cumbersome in CSV files.

When exporting or importing data, the most popular file format is CSV or Comma-Separated Values. Despite their widespread use, CSV files have several drawbacks when it comes to handling and evaluating big information.

On the other hand, Google BigQuery is effective at handling massive volumes of data. Quick query execution is possible when you submit CSV files to BigQuery. As a result, it takes less time to get insights from your data. ML and spatial data analysis are examples of advanced analytics tools that BigQuery provides. These features provide deeper insights that you may utilize to make wise decisions.

Furthermore, BigQuery has a pay-as-you-go approach, meaning that you only pay for the queries and storage that you really utilize. For the same, you don’t need any pricey gear or software.

BigQuery import CSV will help you smoothen your Analysis processes and gain the following features of BigQuery:

  • Real-Time Analytics
  • On-Demand Storage Scaling
  • BigQuery ML
  • Optimization Tools


This article provided you with a step-by-step guide on how you can set up CSV to BigQuery connection using 4 different methods. However, there are certain limitations associated with the first three methods. You will need to implement them manually, which will consume your time & resources, and writing custom scripts can be error-prone. Moreover, you need a full working knowledge of the backend tools to successfully implement the in-house Data transfer mechanism. You will also have to regularly map your new CSV files to the BigQuery Data Warehouse.

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Hevo Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. Hevo caters to 150+ data sources (including 40+ free sources) and can seamlessly transfer your data from CSV to BigQuery within minutes. Hevo’s Data Pipeline enriches your data and manages the transfer process in a fully automated and secure manner without having to write any code. It will make your life easier and make data migration hassle-free.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable Hevo Pricing that will help you choose the right plan for your business needs.

Share your thoughts on loading data from CSV to BigQuery in the comments!

Pratik Dwivedi
Freelance Technical Content Writer, Hevo Data

Pratik writes about various topics related to data industry who loves creating engaging content on topics like data analytics, machine learning, AI, big data, and business intelligence.

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