So, you’re a Quickbooks user, right? That’s great knowing someone who gives utmost priority to the management of sales & finances of their business. Focusing on managing the payroll and finances optimally makes you a great player. 

At times, there would be a need to move your accounting data from Quickbooks to a data warehouse. That’s where you come in. You take the responsibility of replicating data from Quickbooks to a centralized repository. By doing this, the analysts and key stakeholders can take super-fast business-critical decisions.

Give a high-five! We’ve prepared a simple and straightforward guide without beating around the bush. Leap forward and read the 2 simple methods for performing the data replication from Quickbooks to Databricks.

How to Replicate Data From Quickbooks to Databricks?

To replicate data from Quickbooks to Databricks, you can do either of the following:

  • Either use CSV files or 
  • A no-code automated solution. 

We’ll cover replication via CSV files next.

Replicate Data from Quickbooks to Databricks Using CSV Files

Quickbooks, being a cloud-based accounting platform, stores data about customers, vendors, and items. You have to run multiple exports for different types of data.

You can even export the reports in CSV format.

Follow along to replicate data from Quickbooks to Databricks in CSV format:

Step 1: Export CSV Files from Quickbooks 

For exporting Customer & Vendor data

  • Go to the Customer or Vendor Center based on which data you want to export.
  • Now, select the “Excel” drop-down option. Now you can select
    • Export Customer/Vendor list: If you want to export customer/vendor data such as name, balances, and contact information.
    • Export Transactions: If you want to export transactions (either by name or transaction type).
  • The Export window appears. Now, select the “Create a comma-separated values (.csv) fileoption.
  • Now, select the “Exportbutton.
  • Now, you need to assign a name to your file. Then choose the location where you want to save the file.
  • After that, you can edit and transform your file according to your needs.

For exporting Items data

  • Go to the Lists menu, then select the “Item Listoption.
  • Now, select the “Excel” drop-down option. Then, click on the “Export all Items” button.
  • The Export window appears. Now, select the “Create a comma-separated values (.csv) fileoption.
  • Now, select the “Exportbutton.
  • Now, you need to assign a name to your file. Then choose the location where you want to save the file.
  • After that, you can edit and transform your file according to your needs.

For Reports & Lists

  • Go to the Reports/Lists tab.
  • Open the report or list which you want to export.
  • Now, select the “Excel” drop-down option at the top of the report.
  • Select the “Create New Worksheetoption.
  • Now, the “Send Report to Excel window” appears. You can select the “Create a comma-separated values (.csv) fileoption.
  • Now, select the “Exportbutton.
  • Now, you need to assign a name to your file. Then choose the location where you want to save the file.
  • After that, you can edit and transform your file according to your needs.

Step 2: Import CSV Files into Databricks

  • In the Databricks UI, go to the side navigation bar. Click on the “Data” option. 
  • Now, you need to click on the “Create Table” option.
  • Then drag the required CSV files to the drop zone. Otherwise, you can browse the files in your local system and then upload them.

Once the CSV files are uploaded, your file path will look like: /FileStore/tables/<fileName>-<integer>.<fileType>

Uploading CSV files while replicating data from Quickbooks to Databricks
Image Source

Step 3: Modify & Access the Data

  • Click on the “Create Table with UI” button.
  • The data now gets uploaded to Databricks. You can access the data via the Import & Explore Data section on the landing page.
Import & Explore data option in Databricks
Image Source
  • To modify the data, select a cluster and click on the “Preview Table” option.
  • Then, change the attributes accordingly and select the “Create Table” option.

The above 3-step guide replicates data from Quickbooks to Databricks effectively. It is optimal for the following scenarios:

  • Less Amount of Data: This method is appropriate for you when the number of reports is less. Even the number of rows in each report is not huge.
  • One-Time Data Replication: This method suits your requirements if your business teams need the data only once in a while.
  • Limited Data Transformation Options: Manually transforming data in CSV files is difficult & time-consuming. Hence, it is ideal if the data in your spreadsheets is clean, standardized, and present in an analysis-ready form. 
  • Dedicated Personnel: If your organization has dedicated people who have to perform the manual downloading and uploading of CSV files, then accomplishing this task is not much of a headache.

However, when the frequency of replicating data from Quickbooks increases, this process becomes highly monotonous. It adds to your misery when you have to transform the raw data every single time. With the increase in data sources, you would have to spend a significant portion of your engineering bandwidth creating new data connectors. Just imagine — building custom connectors for each source, transforming & processing the data, tracking the data flow individually, and fixing issues. Doesn’t it sound exhausting?

How about you focus on more productive tasks than repeatedly writing custom ETL scripts, downloading, cleaning, and uploading CSV files? This sounds good, right?

In that case, you can.. 

Replicate Data from Zendesk to Databricks Using an Automated ETL Tool

An automated tool is an efficient and economical choice that takes away a massive chunk of repetitive work. It has the following benefits:

  • It allows you to focus on core engineering objectives. By doing so, your business teams can jump on to reporting without any delays or data dependency on you.
  • Your support team can effortlessly filter, aggregate, and segment data from Quickbooks.
  • Without technical knowledge, your analysts can seamlessly standardize timezones, convert currencies, or simply aggregate campaign data for faster analysis. 
  • An automated solution provides you with a list of native in-built connectors. No need to build custom ETL connectors for every source you require data from.

Why not explore an automated data pipeline solution? For instance, here’s how Hevo, a cloud-based ETL solution makes the data replication from Quickbooks to Databricks ridiculously easy: 

Step 1: Configure Quickbooks as your Source

  • Fill in the required attributes required for configuring Quickbooks as your source.
Configure Quickbooks Online in Hevo
Image Source

Note:  If you select All Available Data, Hevo fetches the data created since 1 January 2000 for your company till the current date.

Step 2: Configure Databricks as your Destination

Now, you need to configure Databricks as the destination.

Configure Databricks as the destination in Hevo
Image Source

All Done to Setup Your ETL Pipeline

After implementing the 2 simple steps, Hevo will take care of building the pipeline for replicating data from Quickbooks to Databricks based on the inputs given by you while configuring the source and the destination.

The pipeline will automatically replicate new and updated data from Quickbooks to Databricks every 15 mins (by default). However, you can also adjust the data replication frequency as per your requirements.

Data Pipeline Frequency

Default Pipeline FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
15 Mins15 Mins24 Hrs1-24

For in-depth knowledge of how a pipeline is built & managed in Hevo, you can also visit the official documentation for Quickbooks Online as a source and Databricks as a destination.

You don’t need to worry about security and data loss. Hevo’s fault-tolerant architecture will stand as a solution to numerous problems. It will enrich your data and transform it into an analysis-ready form without having to write a single line of code.

Here’s what makes Hevo stands out from the rest:

  • Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
  • Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
  • Faster Insight Generation: Hevo offers near real-time data replication, giving you access to real-time insight generation and faster decision-making. 
  • Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
  • Scalable Infrastructure: With the increased number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
  • Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
  • Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

You can take our 14-day free trial to experience a better way to manage data pipelines.

Get started for Free with Hevo!

What Can You Achieve by Migrating Your Data from Quickbooks to Databricks?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Quickbooks to Databricks. Does your use case make the list?

  • How does CMRR (Churn Monthly Recurring Revenue) vary by Marketing campaign?
  • How much of the Annual Revenue was from In-app purchases?
  • Which campaigns have the most support costs involved?
  • For which geographies are marketing expenses the most?

Summing It Up 

Exporting & uploading CSV files is the go-to solution for you when your data & financial analysts require fresh data from Quickbooks only once in a while. But with an increase in frequency, redundancy will also increase. To channel your time into productive tasks, you can opt-in for an automated solution that will help accommodate regular data replication needs. This would be genuinely helpful to finance & accounting teams as they would need regular updates about marketing expenses, support costs of campaigns, recurring and annual revenue of your organization, etc.

Even better, your accounting teams would now get immediate access to data from multiple channels and thus deep-dive to explore better market opportunities.

So, take a step forward. And here, we’re ready to help you with this journey of building an automated no-code data pipeline with Hevo. Its 150+ plug-and-play native integrations will help you replicate data smoothly from multiple tools to a destination of your choice. Its intuitive UI will help you smoothly navigate through its interface. And with its pre-load transformation capabilities, you don’t even need to worry about manually finding errors and cleaning & standardizing them.

With a no-code data pipeline solution at your service, companies will spend less time calling APIs, referencing data, building pipelines, and more time gaining insights from their data.

Skeptical? Why not try Hevo for free and take the decision all by yourself? Using Hevo’s 14-day free trial feature, you can build a data pipeline from Zendesk to Databricks and try out the experience.

Here’s a short video that will guide you through the process of building a data pipeline with Hevo.

We’ll see you again the next time you want to replicate data from yet another connector to your destination. That is if you haven’t switched to a no-code automated ETL tool already.

We hope you have found the appropriate answer to the query you were searching for. Happy to help!

Former Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has written more than 100 articles on diverse topics related to data industry.

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