The Salesforce Software as a Service (SaaS) Marketing Automation Platform Pardot serves as the central hub for managing your email automation, targeted email campaign, and lead management projects. Tracking customer behavior and developing digital marketing campaigns are just two of the routine marketing tasks that Pardot automates.
Snowflake’s Data Cloud is built on a cutting-edge data platform that is delivered as Software-as-a-Service (SaaS). Snowflake provides Data Storage, Processing, and Analytic Solutions that are faster, easier to use, and more flexible than traditional options.
This article discusses the different methods for Pardot to Snowflake integration. In addition to that, it gives a brief introduction to Pardot and Snowflake.
What is Pardot?
Pardot is Salesforce’s B2B Marketing Automation tool, which enables marketing and sales teams to develop, implement, and manage online marketing campaigns that boost sales and improve efficiency.
It gives marketers the ability to pinpoint potential clients who are most likely to convert. By speaking with prospects at the appropriate time and in the right manner, marketers learn this. Each prospect will be treated as an individual as they learn about your product or service at their own pace with the help of marketing automation.
Pardot utilizes the following for its working:
- What you know about a prospect: the information you have collected about them through forms, your Salesforce CRM, or other sources.
- What the prospect does: how they use your online marketing tools, such as your website, social media accounts, or in-person interactions.
For marketing organizations, Pardot offers Lead Management, Email Automation, ROI Tracking, and Targeted Email Campaigns. Salesforce’s Customer Relationship Management (CRM) software can sync with Pardot to improve performance for businesses that already use it. Comparing this syncing to other marketing automation tools can also shorten the time required for analytics. User changes in Pardot typically take 10 minutes to reflect in Salesforce. To give users access to Pardot, configure the Pardot Lightning app. Your sales and marketing teams can collaborate on a single platform thanks to the enhanced integration experience provided by the Pardot Lightning app.
Key Features of Pardot
- Connected Campaign: A relatively recent feature that Pardot released in 2018 is the ‘Connected Campaign’. It is a useful feature for maintaining campaign organization between your B2B and B2C marketing divisions. You can combine your Pardot and Salesforce campaigns once you create a Connected Campaign on Pardot.To significantly increase the effectiveness of your campaign management. You can also lessen clutter and simplify campaign reporting with a more effective management structure.
- B2B Marketing Analytics: ROI (Return on Investment) is a metric that businesses use to assess the effectiveness of marketing campaigns. B2B Marketing Analytics combines sales and marketing data to provide you with information about the performance of your business. You are then able to prioritize projects based on the ROI of your marketing campaigns. You can allocate your budget to more effective campaigns that are in line with your organization’s objectives using the insights gained from B2B Marketing Analytics.
- Salesforce User Sync: You can manage both your Pardot and Salesforce accounts simultaneously by using the Salesforce User Sync administration feature. An optional feature is Salesforce User Sync. Only when Pardot and Salesforce are connected can you turn this on. You cannot disable this feature after opting in. The primary goal of this feature is to create a synced user. It combines your Pardot and Salesforce accounts so they can function as a single user. The way you use Pardot, as a result, has significantly changed. You end up with better user management tools, a better user experience, and better security.
- Dynamic Content: You can tailor content for each market segment using Dynamic Content. In other words, depending on a user’s engagement, you can design various iterations of forms, landing pages, websites, emails, and anything else. Dynamic Content is the best feature for maximizing ROI. Meeting each lead’s specific needs increases the likelihood that a lead will result in a sale. Your clients will believe that you are the only one who truly understands their needs by offering the best content for each market segment. Your conversion rates could significantly increase as a result.
What is Snowflake?
Snowflake is a fully managed SaaS (Software as a Service) that combines Data Warehousing, Data Lakes, Data Engineering, Data Science, Data Application Development, and Secure Sharing and Consumption of Real-time / Shared Data into a single platform. To meet the demanding needs of growing businesses, Snowflake includes out-of-the-box features such as Storage and Compute Separation, On-the-fly Scalable Compute, Data Sharing, Data Cloning, and third-party Tool Support.
Snowflake isn’t based on any existing database technology or “Big Data” software platforms like Hadoop. However, it combines a brand-new SQL query engine with cutting-edge Cloud Architecture. Snowflake also gives users all of the features and capabilities of an enterprise analytic database, plus a lot more.
Snowflake is a hybrid of shared-disk and shared-nothing Database architectures, which allows it to deliver results quickly. It uses a central repository for persisted data, similar to a shared-disk database, which is accessible from all compute nodes. It uses MPP (Massively Parallel Processing) compute clusters, in which each node stores a portion of the entire data set locally, similar to shared-nothing architectures.
Key Features of Snowflake
Here are some of the features of Snowflake as a Software as a Service (SaaS) solution:
- Snowflake enables you to enhance your Analytics Pipeline by transitioning from nightly Batch Loads to Real-time Data Streams, allowing you to improve the quality and speed of your analytics. By enabling Secure, Concurrent, and Monitoring Access to your Data Warehouse across your organization, you can improve the quality of analytics at your company.
- Snowflake uses the Caching Paradigm to swiftly deliver the results from the cache. To avoid re-generation of the report when nothing has changed, Snowflake employs Persistent (within the session) Query results.
- Snowflake allows you to get rid of silos and ensure access to meaningful insights across the enterprise, resulting in better Data-driven Decision-Making. This is a crucial first step toward bettering partner relationships, optimizing pricing, lowering operational expenses, increasing sales effectiveness, and more.
- Snowflake allows you to better analyze Customer Behaviour and Product Usage. You can also use the whole scope of data to ensure Customer Satisfaction, drastically improve product offers, and foster Data Science innovation.
- Snowflake allows you to create your own Data Exchange, which allows you to securely communicate live, controlled data. It also encourages you to improve data relationships throughout your business units, as well as with your partners and customers.
Benefits of Connecting Pardot to Snowflake
Some benefits of connecting Pardot to Snowflake are:
- Creating Leads: To bring in a steady flow of prospective customers, using this integrated marketing tools such as lead capture forms, form handlers, and landing pages.
- Email Promotion: Personalize, schedule, and send emails to your prospects as the credentials will be stored in the warehouse.
- Prospect Classification: Segment your prospects using lead grading and lead scoring.
- Prospect Administration: Discover who is most engaged with your content and connect with them on a more personal level.
- Alignment of Marketing and Sales: Connect with Salesforce CRM to improve the sales funnel, maximize future marketing efforts, and calculate marketing ROI.
Pardot assists marketers in locating potential customers and turning them into paying clients. Leads can be forwarded to sales, automated marketing campaigns can be made, prospect activity can be tracked, and prospects can be led through the purchasing process using Pardot. Snowflake provides data warehouse-as-a-service, a cloud-based data storage, and analytics service. Employing hardware and software-based in the cloud, businesses can use it to store and analyze data.
When integrated, moving data from Pardot to Snowflake could solve some of the biggest data problems for businesses. Pardot to Snowflake integration helps in Finding Leads and High-Quality Lead Capture processes. It unifies the entire Process Workflow into a single integrated unit that can be used consistently by anyone, without lack of coordination.
In this article, we have described two methods to achieve this:
Method 1: Using Hevo to Set Up Pardot to Snowflake
Hevo, an Automated Data Pipeline, provides you a hassle-free solution to connect Pardot to Snowflake within minutes with an easy-to-use no-code interface. Hevo is fully managed and completely automates the process of not only loading data from Pardot but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
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Method 2: Using Custom Code to Move Data from Pardot to Snowflake
This method would be time-consuming and somewhat tedious to implement. Users will have to write custom codes to enable Pardot Snowflake migration. This method is suitable for users with a technical background.
Both the methods are explained below.
Connecting Pardot to Snowflake
Method 1: Using Hevo to Set Up Pardot to Snowflake
Hevo provides an Automated No-code Data Pipeline that helps you move your Pardot data to Snowflake. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ Sources(including 40+ free sources)but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Using Hevo Pardot to Snowflake Migration can be done in the following 2 steps:
- Step 1: Configure Pardot as the Source in your Pipeline by following the steps below:
- Step 1.1: In the Asset Palette, select PIPELINES.
- Step 1.2: In the Pipelines List View, click + CREATE.
- Step 1.3: Select Pardot on the Select Source Type page.
- Step 1.4: Click + ADD SALESFORCE ACCOUNT LINKED TO PARDOT on the Configure your Salesforce Account linked to Pardot page.
- Step 1.5: Click CONTINUE after selecting the environment linked to your Salesforce login.
- Step 1.6: Enter your Salesforce login information.
- Step 1.7: To grant Hevo access to your Pardot environment, click Allow. The Configure your Pardot Source page then opens for you.
- Step 1.8: Enter the following information on the Configure your Pardot Source page:
- Pipeline Name: A name for the Pipeline that is unique and does not exceed 255 characters.
- Pardot Domain Name: Choose the domain name that hosts your Pardot data.
- Pardot Business Unit ID: A distinguishing number for the Pardot business unit whose information you want to replicate. It starts with the value 0Uv and has 18 characters.
- Historical Sync Duration: The amount of time that the historical data must be ingested. All Available Data is the default historical sync duration.
- Step 1.8: TEST & CONTINUE is the button to click.
- Step 1.9: Set up the Destination and configure the data ingestion.
- Step 2: To set up Snowflake 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 Snowflake from the Add Destination page.
- Step 2.4: Set the following parameters on the Configure your Snowflake Destination page:
- Destination Name: A unique name for your Destination.
- Snowflake Account URL: This is the account URL that you retrieved.
- Database User: The Hevo user that you created in the database. In the Snowflake database, this user has a non-administrative role.
- Database Password: The password of the user.
- Database Name: The name of the Destination database where data will be loaded.
- Database Schema: The name of the Destination database schema. Default value: public.
- Warehouse: SQL queries and DML operations are performed in the Snowflake warehouse associated with your database.
- Step 2.5: Click Test Connection to test connectivity with the Snowflake warehouse.
- Step 2.6: Once the test is successful, click SAVE DESTINATION.
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.
- 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.
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Method 2: Using Custom Code to Move Data from Pardot to Snowflake
Access Your Data On Salesforce Pardot
- Accessing your data and beginning data extraction is the first step in loading your Pardot data to any kind of data warehouse solution.
- Salesforce was a pioneer in the SaaS and API economies, and as one would anticipate from a Salesforce product, Pardot can be accessed via a web REST API. The REST API acts as a Pardot to Snowflake connector.
- It is simple to access data from Pardot using the API; you simply send GET requests to the appropriate API endpoints, and the API will respond with the requested information.
- The 22 resources that make up the API represent all of the things that can be done using the platform’s marketing automation features.
- When working with an API like the one provided by Pardot, you should keep the following in mind:
- Rate Limits: Each API has some rate restrictions that you must adhere to. Particularly when working with APIs from SalesForce, where the API calls are shared by users of the core product and integrations.
- Authentication: OAuth is used for Pardot authentication, which adds some extra work to the development of any application that tries to extract data from Pardot.
- Paging and Dealing with a Big Amount of Data: As they track how people interact with your brand, platforms like Pardot produce a lot of data. It might be challenging to extract large amounts of data from an API, especially if you take into account and adhere to any rate limitations the API may have.
Transform And Prepare Data
- You must transform your data based on two main factors after you have accessed it in Pardot:
- The restrictions of the database that will be used to store the data
- The kind of analysis you intend to conduct
- There are specific restrictions on the supported data types and data structures for each system. You can send nested data like JSON directly to Snowflake, for instance, if you want to push data into it. However, this is not an option when working with tabular data stores like PostgreSQL. Instead, before loading your data into the database, you will need to flatten it out.
- Additionally, you must pick the appropriate data types. You will once again need to make the appropriate decisions based on the system to which you will send the data and the data types that the API exposes to you. These decisions are crucial because they may restrict the expressiveness of your queries and the tasks that your analysts can perform directly from the database.
- In Snowflake, data is arranged in tables with clearly defined columns, each of which contains a particular type of data.
- A wide range of data types is supported by Snowflake. It’s important to note that various semi-structured data types are also supported.
- It is possible to load data in JSON, Avro, ORC, Parquet, or XML format directly into Snowflake. Similar to Google BigQuery, hierarchical data is treated as a first-class citizen.
- There is one notable popular data type that Snowflake does not support. The data type LOB, or large object, is not supported. Use a BINARY or VARCHAR type in its place. These types, however, are not very useful in use cases involving data warehouses.
- Creating a schema where you will map each API endpoint to a table is a typical approach for loading data from Pardot to Snowflake.
- You should ensure the proper conversion to a Snowflake data type and map each key within the Pardot API endpoint response to a column of that table.
- Naturally, you must make sure that your database tables are updated as necessary to accommodate potential changes in the data types provided by the Pardot API. Automatic data type casting is not a thing.
- You can proceed and begin loading your data into the database once you have a comprehensive and clearly defined data model or schema for Snowflake.
Export Data From Pardot To Snowflake
- The COPY INTO command is typically used to bulk load data from Pardot to Snowflake. The data is stored in files that are typically in JSON format and are kept on a local file system or in Amazon S3 buckets. Data is then copied into the data warehouse by using the Snowflake instance’s COPY INTO command.
- Before using the COPY command, the files can be pushed into Snowflake using the PUT command into a staging environment.
- Another option is to directly upload the data to a platform like Amazon S3, from which Snowflake can access it.
Updating Your Pardot Data On Snowflake
- You will need to update your older data on Snowflake because you will be producing more data on Pardot. Both new records and updates to older records that have been updated on Pardot for any reason are included in this.
- Repeat the previous steps while updating your currently available data if necessary. You will need to periodically check Pardot for new data. UPDATE statements are used to update an already existing row on a Snowflake table.
- The detection and elimination of any duplicate records from your database is a further concern that needs your attention. Duplicate records may be added to your database due to Pardot’s lack of a mechanism to recognize new and updated records or because of errors in your data pipelines.
- Generally speaking, ensuring the accuracy of the data inserted into your database is a significant and challenging issue.
This article describes the ways to Connect Pardot to Snowflake in a few easy steps. It also gives an overview of Snowflake and Pardot.
Visit our Website to Explore Hevo
Hevo offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Marketing, Customer Management, etc.
This platform allows you to transfer data from 150+ sources (including 40+ Free Sources) such as Pardot and Cloud-based Data Warehouses like Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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