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Razorpay to Google BigQuery – Made Easy

12 minutes read

Modified on August 2, 2022

eCommerce

If you’re reading this, you are probably looking for a way to transfer data from Razorpay to Google BigQuery quickly & efficiently. In this article, we will talk about why using Razorpay is essential and how you can get data from it and all your apps and tools together in one place without having to write any code.

The choice for eCommerce business when it comes to marketing and selling their merchandise is growing every day. eCommerce vendors have to decide on what channels they want to sell on, which channels they want to spend their advertising dollars on, whether the channels include:

Branded Websites
- In some cases branded eCommerce sites per country
Marketplaces
- In many instances, marketplaces per country
Retail Stores
- To create an omnichannel presence and to engage buyers where the shop

In a competitive digital landscape that we live in, it has become imperative that eCommerce businesses of all sizes that aspire to grow and stay profitable have to look into their data deeply and leverage this for growth. Understanding the user behaviour in every stage of the conversion funnel becomes necessary. Marketing apps, e-commerce platforms, customer support platforms generally provide numerous data which is usually analyzed to understand customer behaviour across those stages of the conversion funnel. Payment gateway data provides insights on customer behaviour in the final stage of the conversion funnel and analysis of this data is of paramount importance as it takes a substantial amount of money and effort to bring a customer to that stage. It is essential to ensure that bounce in this stage is minimal.

With the increase in competition, eCommerce Companies should strive to be more data-driven for various reasons. Some of these reasons include:

Understanding the balance between demand and supply,
Understanding customer lifetime value (LTV)
Segmenting customer base for effective marketing
Finding opportunities to reduce wasteful spend
Optimizing digital assets to maximize revenue for the same marketing spend,
Improving ROIs on Ad campaigns and
Offering an engaging and seamless experience for customers in every channel that the customer engages with the brand.

Businesses these days need to be efficient in terms of their data analysis. They are struggling to make sense of the data generated from various applications and tools used to manage different processes efficiently.

Payment gateways like Razorpay generate numerous data like payment dropouts, payment methods, fraud attempts, subscriber data. Use this data to get meaningful insights, like when a customer used or searched EMI option to make a payment, whether the payment got declined due to insufficient funds or security issues. Businesses can block the user to reduce losses in case of fraud. In case of payment decline, the customer might purchase again if remarketed later or given a discounted offer. If a user has cancelled a purchase or a subscription, then discount offers or other benefits may be pushed to them based on their reasons for cancellation. Hence, reducing the bounce rate for companies, and thus increasing revenues.

Businesses typically operate at least 10-15 different software/platforms to deliver on their customer expectations. As a result, data silos are created, which makes it more difficult to consolidate data and use the data for reporting, operations, analysis, and taking informed forward-looking decisions. These data need to be analyzed along with data generated from Razorpay to get a clear picture of the business, which helps in optimizing the business.

Thus the data coming from Razorpay needs to be fed into marketing tools to provide more personalized ads to customers, or into tools such as customer support platforms, website, inventory management, CRMs. These feeds would help optimize the various processes and give a more personalized experience to customers which would increase conversion rates, thus increasing revenues & reducing losses. Since various tools are creating different data silos in use, it makes generating reports and analyzing these data difficult and time-consuming.

These separate silos make the analysis of the entire business data comprehensively, challenging. Data Savvy eCommerce businesses try to reduce the effort of reporting and analysis by integrating data from all these data Silos into a cloud data warehouse like Google BigQuery. By taking this step, the process of reporting and analysis becomes easy, inexpensive, and consequently done more frequently.

In this post, we will be looking at methods to replicate data from Razorpay to Google BigQuery.

Before we start exploring the process involved in data transfer, let us spend some time looking at these individual platforms.

Razorpay Overview

Razorpay is a cloud payment platform which enables businesses to manage their finances through payment gateway, subscription options, vendor payouts, employee payouts, invoicing and process automation options. It even provides short term credit for its users to manage cash crunches. It allows several payment modes like debit cards, credit cards, net banking, UPI and prepaid digital wallets. Razorpay integrates with popular apps such as JavaScript, WooCommerce, Flutter, Magento, and Hiveage. Hence, it makes it possible for its users to purchase or sell goods and services through the same payments platform. It also provides fraud screening tools to reduce the risk of losing information. Razorpay user love :

Razorpay required minimal legal documentation hence instant activation
The dashboard looked super cool and sleek.
Seamless Refund Process
A drastic increase in the number of users paying successfully
Easy Integration and API Driven Automation
Prompt and responsive customer support
Real-time data and insights on the dashboard

Google BigQuery Overview

Google BigQuery is the first genuinely serverless data warehouse-as-a-service offering in the market. There is no infrastructure to manage, no patches to apply, or any upgrades to be made. The role of a database administrator in a Google BigQuery environment is to architect the schema and optimize the partitions for performance and cost. This cloud service automatically scales to fulfil the demands of any query without the need for intervention by a database administrator. Google BigQuery service also introduced an unusual pricing model that is based not on the storage capacity or the compute capacity needed to process your queries. Instead, the pricing relies on the amount of data processed by incoming queries.

The best part about Google BigQuery is that you can load data to the service and start using the data immediately. Users no longer have to worry about what runs under the hood because the implementation details are hidden from them. All you need is a mechanism to load data into Google BigQuery and the ability to write SQL queries. By making data warehousing so simple, Google BigQuery has revolutionized the cloud data warehousing space and has put the power back in the hands of the analysts.

It is good practice to understand the architecture of Google BigQuery. Understanding the architecture helps in controlling costs, optimizing query performance, and optimizing storage. The factors that govern Google BigQuery Pricing are Storage and Query Data Processed.

Why Do Businesses Need to Replicate Razorpay data to Google BigQuery

Let’s take a simple example to illustrate why data consolidation from Razorpay to Google BigQuery can be helpful for an eCommerce business.

An eCommerce company selling in multiple countries is running campaigns on a variety of marketing platforms across channels. They have different selling platforms, inventories management systems, logistics channels and target audiences in each country. They use Razorpay to collect payments from all of their sales platforms and managing vendor and employee payment in all countries. Now the decision-makers want to increase profits and optimize their Ad campaigns to maximize ROIs; they primarily focus on increasing conversions and understanding why users are bouncing off at the final stage of the conversion funnel is significant for this. In this process, they face the following problems:

While calculating profits/losses of the overall business, it becomes a nearly impossible task to pull all of these data from multiple platforms for each country separately, and then analyze all of this data together with the expense data and calculate profits. It involves a lot of working hours which costs money, and there is usually a time lag involved, which reduces the accuracy of the analysis and its effectiveness as the data analysis does not happen in real-time. Since all the financial transactions are being done on Razorpay. The only solution to this problem is to pull all the data from Razorpay to a cloud data warehouse on a real-time basis enabling useful analysis.
Audience profiling data from e-commerce platforms, CRMs, customer support systems need to be analyzed along with conversion data from Razorpay optimize audience targeting.
There are separate data silos for inventory data, logistics data, which need to be separately downloaded and compared and updated regularly to optimize the ad campaigns and ensure that irrelevant ads are not running. For example, an ad might be running off a product which might no longer be in stock, or might not be deliverable in the location which it is running, rendering these ads as redundant and thus causing a substantial loss for the company. But the most crucial data for understanding Advertisement ROI is the conversion data, which is most accurate in the payments platform, i.e. Razorpay.
For effective remarketing, target the people who have not completed payments, or have encountered a failed transaction in addition to people who have added products to their cart, wishlists or favourites. Most of this data is present in Razorpay. People who have responded to other marketing campaigns like email, SMS, social media marketing also need to be targeted. So again separate data silos from various selling platforms, payment gateways, marketing tools need to be downloaded, analyzed and compared.
The compilation and processing of data from multiple sources for thorough research is a considerable challenge if carried out manually.

For these reasons, top companies consolidate all of their data from Razorpay and other apps and tools into a data warehouse like Google BigQuery to analyze the data and generate reports at a rapid pace.

Replicate data from Razorpay to Google BigQuery

There are two board ways to pull data from any source to any destination. The decision is always a build vs buy decision. Let us look at both these options to see which option provides the business with a scalable, reliable, and cost-effective solution for reporting and analysis of Razorpay data. You can also retrieve the data from Google BigQuery any time you want. To know more, click here.

Use a Cloud Data Pipeline

Building support for APIs is not only tedious but it is also extremely time-consuming, difficult, and expensive. Engaging analysts or developers in writing support for these APIs takes away their time from more revenue-generating endeavours. Leveraging an eCommerce data pipeline like Daton significantly simplifies and accelerates the time it takes to build automated reporting. Daton supports automated extraction and loading of Razorpay data into cloud data warehouses like Google BigQuery, Snowflake, Amazon Redshift, and Oracle Autonomous DB.

Configuring data replication on Daton on only takes a minute and a few clicks. Analysts do not have to write any code or manage any infrastructure but yet can still get access to their Razorpay data in a few hours. Any new data is generated is automatically replicated to the data warehouse without any manual intervention.

Daton supports replication from Razorpay to a cloud data warehouse of your choice, including Google BigQuery. Daton’s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from Razorpay data into Google BigQuery. Daton takes care of

Authentication
Rate limits,
Sampling,
Historical data load,
Incremental data load,
Table creation,
Table deletion,
Table reloads,
Refreshing access tokens,
Notifications

and many more important functions that are required to enable analysts to focus on analysis rather than worry about the data that is delivered for analysis.

Daton – The Data Replication Superhero

Daton is a fully-managed, cloud data pipeline that seamlessly extracts relevant data from many data sources for consolidation into a data warehouse of your choice for more effective analysis. The best part analysts and developers can put Daton into action without the need to write any code.

Here are more reasons to explore Daton:

Support for 100+ data sources – In addition to Razorpay, Daton can extract data from a varied range of sources such as Sales and Marketing applications, Databases, Analytics platforms, Payment platforms and much more. Daton will ensure that you have a way to bring any data to Google BigQuery and generate relevant insights.
Robust scheduling options allows users to schedule jobs based on their requirements using simple configuration steps.
Support for all major cloud data warehouses including Google BigQuery, Snowflake, Amazon Redshift, Oracle Autonomous Data Warehouse, PostgreSQL and more.
Low Effort & Zero Maintenance – Daton automatically takes care of all the data replication processes and infrastructure once you sign up for a Daton account and configure the data sources. There is no infrastructure to manage or no code to write.
Flexible loading options allows you to optimize data loading behaviour to maximize storage utilization and also easy of querying.
Enterprise-grade encryption gives your peace of mind
Data consistency guarantee and an incredibly friendly customer support team ensure you can leave the data engineering to Daton and focus instead of analysis and insights!
Enterprise-grade data pipeline at an unbeatable price to help every business become data-driven. Get started with a single integration today for just $10 and scale up as your demands increase.

For all sources, check our data connectors page.

We Saras can help with our eCommerce-focused Data pipeline (Daton) and custom ML and AI solutions to ensure you always have the correct data at the right time. Request a demo and envision how reporting is supercharged with a 360° view.

Other Articles by Saras Analytics,

Depending on the quantity of data being exported and the frequency with which it is exported, there may be a cost associated with exporting Razorpay data to Google BigQuery. Google BigQuery charges users based on how much data is processed for analysis and how much is stored. The number of transactions processed by Razorpay and its size will determine how much it will cost to export data to Google BigQuery. Before exporting Razorpay data to Google BigQuery, businesses must carefully assess their data requirements and budget to ensure that the integration cost is justified by the insights gained. Businesses can estimate the cost of using Google BigQuery for their data analysis requirements with a pricing calculator and other resources provided by Google.

Exporting Razorpay data to Google BigQuery has some restrictions. First, businesses must be able to set up the integration themselves or hire a developer with a technical understanding of the process. Second, Razorpay data can only be exported to Google BigQuery in structured formats like CSV, JSON, and Avro, which might only work with some tools for data analysis. Thirdly, the business's choice of subscription plan may limit the amount of data exported from Razorpay to Google BigQuery. At long last, organizations should consider the security and consistency ramifications of trading delicate monetary information to research BigQuery and ensure appropriate access controls and safety efforts are set up. Before exporting Razorpay data to Google BigQuery, businesses should thoroughly consider their data requirements and resources, as well as the integration's limitations and potential difficulties.

Businesses benefit from integrating Razorpay with Google BigQuery in several ways: Added understanding: Businesses can learn more about their financial data and transaction patterns by exporting Razorpay data to Google BigQuery. This enables them to make decisions based on data and find growth opportunities. Analyses in real-time: Continuous examination of Razorpay information in Google BigQuery permits organizations to rapidly distinguish and resolve issues as they emerge, giving an upper hand in the present quick-moving business climate. Scalability: Because Google BigQuery is a scalable data warehousing platform, businesses can quickly scale up or down based on their data requirements without worrying about infrastructure upgrades or maintenance. Cost-effective: Businesses can avoid the expense and complexity of maintaining their data warehousing infrastructure by storing and analyzing their Razorpay data in Google BigQuery and only paying for the resources they use.

To learn how to integrate Razorpay with Google BigQuery, several resources are available: Guide to Google Cloud: Step-by-step instructions, sample code, and recommendations for integrating Razorpay with Google BigQuery are all included in the Google Cloud documentation. Documentation for the Razorpay API: For integrating with Google BigQuery, the Razorpay API documentation provides instructions on accessing and exporting transaction data from Razorpay. Online instructional exercises and courses: Udemy, Coursera, and Google Cloud Training are just a few of the numerous online tutorials and courses that cover the integration of Razorpay with Google BigQuery. Support groups and community forums: Users of both Google Cloud and Razorpay can get assistance integrating Razorpay with Google BigQuery through active community forums and support channels. Businesses can select the resources that best meet their requirements and preferences when learning to integrate Razorpay with Google BigQuery.

Businesses can export a wide range of data to Google BigQuery for analysis using Razorpay, such as: Information about the transaction: This includes the transaction ID, amount, currency, status, and the date and time of each transaction processed by Razorpay. Information about the customer: This includes information about Razorpay customers, like their customer ID, name, email address, phone number, and address. Data on refunds: This incorporates data about discounts given through Razorpay, like discount ID, sum, money, status, and date and season of discount. Data in dispute: This includes the dispute ID, status, reason, and date and time of any disputes customers have filed regarding Razorpay-processed transactions. Data on the payment gateway: This incorporates data about the installment passage utilized by clients to make exchanges through Razorpay, like entryway ID, name, and status.