How to Connect Quickbooks to BigQuery? 2 Easy Methods

Easily move your data from Quickbooks To BigQuery to enhance your analytics capabilities. With Hevo’s intuitive pipeline setup, data flows in real-time—check out our 1-minute demo below to see the seamless integration in action!

All of your bookkeeping can be automated by QuickBooks Accounting Software, allowing you to easily keep track of your income and outgoings. You may see a graphic picture of your sales, expenses, profit, and loss among other things using QuickBooks Reports. A fully managed Cloud data warehousing platform called BigQuery is provided by Google. It is constructed using Google’s well-known Dremel Engine.

BigQuery offers a high level of abstraction due to its serverless architecture. In this article, you will learn how to transfer data from Quickbooks to BigQuery using CSV Files. But, if you prefer an automated no-code solution, you can opt for Hevo.

Methods to Connect Quickbooks to BigQuery

Method 1: Using an Automated Data Pipeline to Connect Quickbooks to BigQuery

Hevo is the only real-time ELT No-code data pipeline platform that cost-effectively automates data pipelines that are flexible to your needs.

The following steps can be implemented to connect Quickbooks to BigQuery using Hevo:

Step 1: Configure Quickbooks as a Source 

Perform the following steps to configure QuickBooks Online as the Source in your Pipeline:

• Step 1.1: Click PIPELINES in the Asset Palette.
• Step 1.2: Click + CREATE in the Pipelines List View.
• Step 1.3: In the Select Source Type page, select QuickBooks Online.
• Step 1.4: In the Configure your Quickbooks Online account page, click + ADD QUICKBOOKS ONLINE ACCOUNT.

• Step 1.5: In the Sign In page, log in to your account using the registered email or user ID.

• Step 1.6: In the QuickBooks App Store page, do the following:

1. From the drop-down, select the company that you want to ingest data from, and click Next. This is applicable when multiple companies are associated with your account.

2. Click Connect to grant Hevo access to the selected company’s data.

• Step 1.7: In the Configure your QuickBooks Online Source page, specify the following:
  

• Pipeline Name: A unique name for your Pipeline, not exceeding 255 characters.
• Authorized Account (Non-editable): This field is pre-filled with the unique ID of the company that you selected earlier when connecting your QuickBooks Online account.
• Historical Sync Duration: The duration for which the historical data must be ingested. Default value: 6 Months.
  Note: If you select All Available Data, Hevo fetches the data created since 1 January 2000 for your company till the current date.

• Step 1.8: Click TEST & CONTINUE.
• Step 1.9: Proceed to configuring the data ingestion and setting up the Destination.

Step 2: Configure BigQuery as a Destination

To set up Google BigQuery as a destination in Hevo, follow these steps:

• Step 2.1: In the Asset Palette, select DESTINATIONS.
• Step 2.2: In the Destinations List View, click + CREATE.
• Step 2.3: Select Google BigQuery from the Add Destination page.
• Step 2.4: Choose the BigQuery connection authentication method on the Configure your Google BigQuery Account page.

• Step 2.5: Choose one of these:
  • Using a Service Account to connect:
    • Service Account Key file, please attach.
    • Note that Hevo only accepts key files in JSON format.
    • Go to CONFIGURE GOOGLE BIGQUERY ACCOUNT and click it.
  • Using a user account to connect:
    • To add a Google BigQuery account, click +.
    • Become a user with BigQuery Admin and Storage Admin permissions by logging in.
    • To grant Hevo access to your data, click Allow.

• Step 2.6: Set the following parameters on the Configure your Google BigQuery page:
  • Destination Name: A unique name for your Destination.
  • Project ID: The BigQuery Project ID that you were able to retrieve in Step 2 above and for which you had permitted the previous steps.
  • Dataset ID: Name of the dataset that you want to sync your data to, as retrieved in Step 3 above.
  • GCS Bucket: To upload files to BigQuery, they must first be staged in the cloud storage bucket that was retrieved in Step 4 above.
  • Sanitize Table/Column Names: Activate this option to replace the spaces and non-alphanumeric characters in between the table and column names with underscores ( ). Name Sanitization is written.

• Step 2.5: Click Test Connection to test connectivity with the Amazon Redshift warehouse.
• Step 2.6: Once the test is successful, click SAVE DESTINATION.

To learn more about the steps, visit Quickbooks connector details and Hevo’s Google BigQuery documentation.

Here are more reasons to try Hevo

• Smooth Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your schema in the desired Data Warehouse.
• Exceptional Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
• 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.

Method 2: Connect Quickbooks to BigQuery Manually using CSV Files

You cannot directly export the data from Quickbooks to BigQuery. To export data from Quickbooks to BigQuery, first you will have to export data from Quickbooks as CSV files and then load the CSV files into BigQuery.

Step 1: Export Data from Quickbooks as CSV

The first step in exporting data from Quickbooks to BigQuery is exporting data from Quickbooks as CSV files. In Quickbooks you can have CSV files for Customers/Vendors, Item and Report.

Customers/Vendors

• Activate the Customer/Vendor Center.
• Choose the Excel drop-down and then select:
  • If you want to export customer/vendor data, including name, balances, and contact details, select Export Customer/Vendor List.
  • If you want to export transactions, select Export Transactions (either by name or transaction type).
• Select Create a comma separated values (.csv) file from the Export window.
• Choose Export and then choose the location where you wish to save the file after giving it a name.

Item

• Item List can be found under the Lists menu.
• Export all Items is available after using the Excel drop-down.
• Select Create a comma separated values (.csv) file from the Export window.
• Choose Export and then choose the location where you wish to save the file after giving it a name.

Report

• Check out the report and choose Excel from the drop-down menu at the report’s top and select Create a New Worksheet.
• Create a comma separated values (.csv) file by selecting it on the Send Report to Excel window.
• Choose Export and then choose the location where you wish to save the file after giving it a name.

Now you have your CSV Data with you. The first step in exporting data from Quickbooks to BigQuery is complete now.

Step 2: Load CSV Data into BigQuery 

The second step in exporting data from Quickbooks to BigQuery is importing CSV data into BigQuery. The following steps can be used to load CSV data into BigQuery’s basic Web UI:

• You can click “Create table” and then “Create table from” on your Web console.

• The CSV file that will serve as the source for your new table can then be specified.

• You can choose from a wide range of sources, including cloud storage, using the “Source” selection.
• Choose CSV under “File format.”
• Give your table a name, then choose a database.

• To specify the schema, you may either provide a sample JSON or select “auto-detect” in the schema specification.
• Field delimiter, skipping header rows, the amount of permissible errors, jagged rows, and other variables are additional adjustable settings.
• By selecting “Create Table,” you may now retrieve your CSV, choose the table’s schema, build the table, and add CSV data to it.

You have successfully replicated Quickbooks to BigQuery data transfer.

Limitations of Connecting Quickbooks to BigQuery Manually

• Data can only be moved from Quickbooks to BigQuery in one direction. To maintain and keep both tools up to date, two-way sync is required.
• The manual process takes time because the records need to be updated often. This is a waste of time and resources that could be used for more crucial company duties.
• Some users may find the amount of engineering bandwidth needed to maintain workflows across numerous platforms and update current data bothersome.
• No transformation is possible during data transport. This could be a big problem for businesses who wish to edit their data before moving it from Quickbooks to BigQuery.

Method 3: Using API to connect Quickbooks to BigQuery

QuickBooks API Rate Limits:

API Request Limits: QuickBooks API has a limit of 500 API requests per realm/company per user per minute.

BigQuery API Rate Limits:

API Request Limits: BigQuery API enforces a per-project rate limit, which varies depending on the type of API request (queries, load jobs, etc.).

Step 1: Extract data from QuickBooks

Let’s discuss how to access the data and extract it through the web API in QuickBooks. Given below are the groups of resources around which the API is designed.

1. Transaction resources.

2. Name list resources.

3. Report resources

4. Supporting resources.

Note: Out of these, report resources include all the reports that QuickBooks also offer. The data model is different for it compared to other resources. Other resources contain other entity defined by QuickBooks. Each of them have various data models that is serialized in JSON.

Bonus tips to keep in mind while dealing with QuickBooks API:

1. Be aware of the API limits while using.

2. API keys are used to authenticate on QuickBooks.

Step 2: Prepare data by modifying

You need to perform transformation based on how you want to modify your data. The factors considered are,

• Any limitation your database has when data is loaded.
• The end goal of the data integration (analysis you want to perform)

There are limitations in the system with regards to the supported data types and data structures. Suppose, you want to move data to Google BigQuery. Because of its capability, you can directly send nested data like JSON. But when your data store is in tabular format,  like Microsoft SQL Server,  you need to first flatten out your data before moving to the database.

You need to select the right data types based on the destination and the data types that the API exposes to you. Because these limit the ability to perform queries and what analysts can do directly on the database.

The data model of QuickBooks is rich. Therefore, you need to flatten the resources that you can access and put into multiple tables.

The resources of QuickBooks, the reports, have a tabular but nested format like a spreadsheet. You need to redesign, parse, and transform the reports to a tabular form to store in a database so that the reports are compatible with a database data model.

You can use the access token to make API requests to fetch data from QuickBooks, such as invoices, customers, or transactions.

# QuickBooks API endpoint and headers
api_endpoint = 'https://quickbooks.api.intuit.com/v3/company/{realm_id}/query'
headers = {
    'Authorization': f'Bearer {access_token}',
    'Accept': 'application/json',
}

# Example query to fetch invoices
query = "SELECT * FROM Invoice"
params = {
    'query': query,
}
response = requests.get(api_endpoint, headers=headers, params=params)
invoices_data = response.json()['QueryResponse']['Invoice']

Step 3: Move data to Google BigQuery

Use any of the following supported data sources for this.

1. Google Cloud Storage (You can do this by using the console directly)

2. A POST request (JSON APIs can help you with not only extraction but also the loading of data into our data warehouse.You cna do an HTTP POST request using CURL or Postman.)

3. Google Cloud Datastore Backup

4. Streaming insert

Once your data is in Google Cloud Storage, create a Load Job for BigQuery to load the data. Provide source URIs to point the job to the source data in Cloud Storage that have to be imported.

Use the BigQuery API to insert fetched data from QuickBooks into a BigQuery dataset and table.

from google.cloud import bigquery
# Initialize BigQuery client
client = bigquery.Client()
# Specify BigQuery dataset and table
dataset_id = 'your_dataset_id'
table_id = 'your_table_id'
# Insert data into BigQuery
job_config = bigquery.LoadJobConfig()
job_config.autodetect = True  # Automatically infer schema
job_config.write_disposition = 'WRITE_TRUNCATE'  # Replace existing table data
table_ref = client.dataset(dataset_id).table(table_id)
job = client.load_table_from_json(invoices_data, table_ref, job_config=job_config)
job.result()  # Wait for the job to complete

Limitation of this method for Quickbooks to Google BigQuery

• When you are dealing with a large volume of data, using API will be difficult. There will be restrictions in pulling data due to API rate limits.

Use Cases of Integrating Quickbooks to Google BigQuery:

• Data Analytics: Quickbooks to BigQuery integration enables you to run complex analytical queries on large volumes of data. You can also perform real-time analysis on streaming data sources. 
• IoT: Connecting Quickbooks to Google Sheet can help Internet of Things (IoT) devices analyze large volumes of data and derive meaningful insights from them. 
• Business Intelligence (BI): Building interactive dashboards and reports can make your decisions data-driven. To achieve this, you can migrate from Quickbooks to Google BigQuery. It also helps to do better financial analysis by facilitating forecasting, budgeting, and performance analysis.

Before wrapping up, let’s cover some basics.

What is Quickbooks ?

QuickBooks is one of the popular accounting software. It is used by small and medium-sized firms to monitor their financial situation and control their revenue and expenses. It can be used to monitor cash flow, control invoices, settle accounts, produce reports, and file taxes. Utilize this tool to keep track of your clients, vendors, consumers, inventory, and finances.

With the Report Center, you can keep tabs on aspects of your company like Sales, Income, Costs, and Overall Company Growth. Among the business management tasks that QuickBooks automates are Sales Tax Calculation, Product Tracking, and automatically updating transactions in your Customer/vendor, Item and Report sections. Both the On-Premises and Cloud-based editions of Quickbooks are accessible.

Key Features of Quickbooks

• You may easily produce and modify reports to track the performance of your company.
• Quickbooks can be customized to accommodate a person’s unique needs and tastes, making them suitable for a variety of businesses.

What is BigQuery?

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.

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.

Conclusion  

In this article, you got a glimpse of how to connect Quickbooks to BigQuery after a brief introduction to the salient features, and use cases. The methods talked about in this article are using an automated solution such as Hevo and CSV files. The second process can be a bit difficult for beginners. Moreover, you will have to update the data each and every time it is updated and this is where Hevo saves the day!

Learn how to transfer data from QuickBooks to Redshift to enhance your data integration. Our guide provides clear steps for effective data migration.

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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 pricing that will help you choose the right plan for your business needs.

Share your experience of learning about Quickbooks to BigQuery! Let us know in the comments section below!

FAQs to connect Quickbook to BigQuery