Pipedrive to BigQuery: 2 Easy Methods

on CRMs, Data Warehouse, Google BigQuery, Pipedrive • April 25th, 2022 • Write for Hevo

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Striking the right balance between Sales data accessibility and retaining control over your Pipedrive account can be quite challenging. This is where exporting your data from Pipedrive to BigQuery can help save your day. Many organizations want to take advantage of Google BigQuery’s capabilities to swiftly run complex analytical queries across petabytes of data.

Google BigQuery has become a globally trusted Cloud Data Warehouse & Analytics solution due to its fast and unrivaled query speed. It is a reliable and secure cloud platform for enterprises of all sizes, thanks to its on-demand scalability, affordable pricing, and ability to effectively handle variable workloads.

In this blog, you will walk through Pipedrive and Google BigQuery. You will explore the various features offered by them and also understand the need for Pipedrive BigQuery Integration. Further in this post, you will explore the key methods to load your data from Pipedrive to BigQuery. At the end of this post, you will discover some of the amazing benefits offered by the Pipedrive BigQuery Integration. Read along, to gain more insights and understand how this integration can help your use case.

Table of Contents

What is Pipedrive?

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Pipedrive is a popular and effective Sales CRM Pipeline Management Software that makes Sales Management easier and less stressful for both small and large businesses. It has a drag-and-drop interface that displays a clear picture of existing deals, as well as thorough reporting and forecasting capabilities to illustrate how well the company is doing.

Pipedrive makes it incredibly simple to handle clients from the moment they contact you to the point when they buy your products or services at the end of your Sales Funnel. It allows you to simply define goals, schedule events, and keep track of deals. Pipedrive helps your organization flourish by ensuring that your Sales Drivers and Marketing Processes are in order.

Key Features of Pipedrive

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Pipedrive has numerous features that can help businesses organize their employees’ and clients’ data. Some of the key features offered by Pipedrive are:

  • Dedicated Sales Management Platform: Pipedrive focuses on providing a superior experience for sales teams.
  • Pipeline Focused: Pipedrive focuses on the Sales pipeline, which is the process your firm will utilize to acquire new clients.
  • Optimized Data Experience: While CRMs are all about user data, Pipedrive is designed to spend as little time as possible on data input, allowing your salespeople to focus on selling rather than managing data.
  • Multiple Integrations: Pipedrive can be used in conjunction with third-party apps to increase Sales or create invoices. This is a typical necessity, especially for brand managers who use their mobile devices to get updates and make real-time adjustments. Pipedrive allows you to use your existing set of preferred apps in collaboration with it. 
  • Active Reporting: Pipedrive is also a refuge for insightful data and active reporting on it. Measuring the organization’s performance against a set of goals becomes critical for developing an effective sales strategy for every institution. Pipedrive makes this easier with its stacked choices.
  • Advanced Automation: Furthermore, the selling point for all of these diverse features is that each one can be easily automated for efficient management. Every company wishes to reduce the number of dull and repetitive jobs. Pipedrive addresses this with an intelligent solution that is tailored to your needs.
  • Customizable Templates: Pipedrive allows you to develop your templates, as well as insert contexts and graphics, to strengthen your Sales funnel.  

What is Google BigQuery?

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Google BigQuery is a MultiCloud Data Warehouse. Its serverless architecture allows you to run SQL queries to solve your company’s most critical problems without having to worry about managing infrastructure. The platform is designed to query/store massive datasets in a matter of seconds, utilizing super-fast SQL searches against terabytes of datasets, and providing businesses with real-time data insights.

Google BigQuery is comprised of 2 parts: Storage and Query Processing. The Dremel Query Engine is used to process queries, while the Colossus File System is used for storage. These components are separable and can be scaled independently and on-demand. Since BigQuery takes a serverless approach, there’s no need to manage or set up sophisticated infrastructure, giving you a lot more freedom when it comes to gaining insights with normal SQL. It also offers a variety of price choices, from flat-rate to on-demand.

Key Features of Google BigQuery

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Google BigQuery has improved over time and includes some of the most remarkable features, such as:

  • On-Demand Storage Scaling: With ever-increasing data requirements, you can be confident that it will scale automatically as needed. It is based on Colossus (Google Global Storage System) and saves data in a columnar format, allowing users to work directly on compressed data without having to decompress files on the fly.
  • User Friendly: You can start storing and analyzing your data in BigQuery with only a few clicks. You don’t need to install clusters, establish your storage capacity, or configure compression and encryption settings. You can set up your cloud Data Warehouse easily using an easy-to-understand UI with simple instructions at every step.    
  • Real-Time Analytics: You can keep up to date with real-time data transmission and enhanced analytics, as BigQuery distributes any amount of resources optimally to deliver the greatest performance and results, allowing you to produce business reports as needed.
  • BigQuery ML: You can successfully design and develop data models using standard SQL Commands in BigQuery ML. This reduces the requirement for Machine Learning technical knowledge and allows your Data Analysts to evaluate ML models directly.
  • Optimization Tools: Google’s BigQuery partitioning and clustering technologies can help you get quicker responses from your queries. For optimal storage costs and utilization, you can also adjust the default datasets and table expiration settings.
  • Google Environment: BigQuery, which is maintained and managed by Google, benefits from the Google Ecosystem’s simple and fluid connections. You can link BigQuery to Google Sheets and Google Data Studio for additional analysis with little to no effort.

Explore Various Methods to Migrate Data From Pipedrive to BigQuery

Due to business and analytical needs, organizations require to migrate data to Google BigQuery. Here, you will learn 2 methods that will help you migrate your data from Pipedrive, a CRM-based SaaS source to Google BigQuery, a Cloud Data Warehouse.

Method 1: Load Data From Pipedrive to BigQuery using Hevo’s No Code Data Pipeline

Hevo Data, a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 100+ Data Sources straight into your Data Warehouse or any Databases like Google BigQuery. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code!

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Method 2: Load Data from Pipedrive to BigQuery using Pipedrive REST APIs

In this method, you will learn about various resources available in Pipedrive that can be loaded into Google BigQuery using Pipedrive APIs.

Methods to Load Data from Pipedrive to BigQuery

In this section, you will understand how to move your data from Pipedrive to BigQuery using the following 2 methods:

Method 1: Load Data from Pipedrive to BigQuery using Hevo’s No-Code Data Pipeline

Pipedrive to BigQuery - Google BigQuery Hevo

Hevo provides an Automated No Code Data Pipeline that helps you move data from your Pipedrive to BigQuery. You can set up a Data Warehouse managed by Hevo on the fly, as part of the Integration process. The ingested data from the source is first stored in a Data Warehouse managed by Hevo and then loaded to the destination such as Databases, or Business Applications. Learn the benefits of loading your data to BigQuery here.

Hevo’s fault-tolerant architecture will enrich and transform your data securely and consistently and load it to your destination without any assistance from your side. You can entrust us with your data transfer process by both ETL and ELT processes to a data warehouse, Reverse ETL processes to CRMS, etc and enjoy a hassle-free experience.

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 Fexibility designed for everyone.
  • Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
  • Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Try Hevo to easily load your data from Pipedrive to BigQuery!

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Method 2: Load Data from Pipedrive to BigQuery using Pipedrive REST APIs

Pipedrive provides a plethora of endpoints through which you can communicate with the platform. These endpoints can be used to do tasks such as adding a new contact to your contact list and retrieving information from it.

A unique feature of the Pipedrive API is that for many of the resources, a companion resource controls the custom fields that you may have developed for the resource. Users of the platform will have the greatest freedom possible as a result of this. Some of the accessible resources that can be accessed using Pipedrive API are listed below:

  • Activities: Appointments, assignments, and events in general that can be linked to a contract and your sales funnel are referred to as activities.
  • Currencies: Supported currencies that can be used to indicate the monetary value of a Deal or the value of any custom monetary field.
  • Deals: Deals are ongoing, lost, or won sales to a company or individual.
  • Email Messages: EmailMessages are E-Mail messages sent or received using the Pipedrive E-Mail account.
  • Files: Files are any type of document that is uploaded to Pipedrive such as pictures, spreadsheets, text files, etc.
  • Goals: Goals assist your team in achieving their sales targets.
  • Organizations: Companies and other types of organizations with whom you do business are referred to as organizations.
  • Persons: Persons are your contacts or the clients with whom you conduct deals.
  • Pipelines: Pipelines are groups of Stages that are arranged in a certain sequence.
  • Products: The commodities or services with which you are dealing.
  • Stages: A Pipeline’s Stage is a logical component and, in essence, a bucket that can carry several Deals.
  • Users: People who have access to your Pipedrive account are known as users.

The above are just a few data resources you can export from Pipedrive to BigQuery using Pipedrive APIs. To learn about other data resources, refer to the Pipedrive API Documentation.

What are the Benefits of Loading Data from Pipedrive to BigQuery?

Now that you have understood the various methods of loading data from Pipedrive to BigQuery, let’s check out some of the key benefits this integration offers.

  • Turn your Pipedrive Data into Insights: With the Pipedrive to Google BigQuery connectivity, you can harness the power of data analytics to gain deep insights into your company. Large sets of historical data can be managed and processed without causing performance difficulties. Analyze all of your sales, revenue, profit, and pipeline data in one location using analytics-friendly CRM data. Take use of Google BigQuery’s analytical querying and predictive sales analysis.
  • Secure Backup of Pipedrive Data: To keep all of your pipeline data safe and secure in a scalable cloud-based database, load data from Pipedrive to Google BigQuery on a specified schedule. Create a data lake for a reasonable price with Google BigQuery. Use common SQL queries to quickly explore your data. If you lose data on Pipedrive due to a server breakdown, an accidental deletion, or other disasters, you can restore it or move it to another machine.
  • Build Advanced Dashboards: To generate comprehensive and stunning visualizations, combine your data from Pipedrive and other sources in Google BigQuery and leverage a direct Data Studio connection. Fast Data Studio reports based on BigQuery are available. Collaborate on multiple dashboards with your peers using collaborative editing. Alternatively, use built-in connectors to connect Google BigQuery data views to other popular BI applications like Power BI or Tableau.

Conclusion 

In this article, you gained a basic understanding of Pipedrive and BigQuery. You also explored the 2 methods to load your data from Pipedrive to BigQuery. At the end of this article, you discovered the various advantages of migrating data from Pipedrive to BigQuery. 

However, knowing where to start and how to combine consumer data from various applications can be a challenge for many companies. This is where Hevo can help save your day!

Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 100+ Data Sources including 40+ Free Sources, into your Data Warehouse such as Google BigQuery. Hevo is fully automated and hence does not require you to code.

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SIGN UP 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 with loading data from Pipedrive to BigQuery in the comments section below!

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