Marketo to Snowflake Data Migration: 2 Easy Ways to Load Data

• July 21st, 2022

marketo to snowflake: FI

Marketo is a marketing automation platform that B2B and B2C businesses use to manage and deliver multi-channel campaigns and programs that are tailored to prospects and customers. With Marketo, businesses can organize their unfiltered user data to develop targeted campaigns and programs for a variety of marketing activities, from lead generation to marketing ROI analysis, across a variety of channels.

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 explains how to Connect Marketo to Snowflake using various methods. It also gives an overview of Snowflake and Marketo.

Table Of Contents

What is Marketo?

marketo to snowflake: marketo logo
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Marketo is a Marketing software solution provided as a Web Service by Adobe. Marketo allows users to seamlessly perform Lead Management, Lead Generation, and all activities involved in converting a lead to a customer. Solutions like Marketo help companies concentrate on their core value offering rather than spending resources creating and managing software for Marketing purposes. Marketo also provides a customer experience management dashboard and capabilities for related analytics.

Marketo also offers users access to its API in two forms, i.e., Lead APIs and Asset APIs. Lead APIs help users import, export, and manipulate the leads stored in Marketo. Asset APIs help users programmatically manage Marketing assets like images, videos, etc.

Key Features of Marketo

  • Lead Nurturing: Marketo gives businesses the ability to segment their leads by Target Industries, Marketing Personas, or the kind of interactions taking place. Based on the triggers that users set up, they can filter leads into Engagement Streams, or Drip Campaigns targeted precisely towards their needs and designed to keep the business top of mind. It also allows businesses to easily set transition triggers between streams so that their leads will always be in the right part of the funnel at any point in time.
  • Account-Based Marketing: Marketo’s Account-based Marketing allows businesses to name target accounts and roles within the company they wish to reach. Marketo Account-Based Marketing (ABM) tools let businesses build a smart list of characters describing the persona they wish to target, optimize their website experience for those leads, and engage them across channels, including Web, Email, Advertisements, etc.
  • SEO Tools:  Marketo houses numerous SEO tools that allow businesses to get insights into key SEO metrics such as the level of competition for keywords, pages that those keywords are ranked highest for, current Search Engine Results Page (SERP) rank, etc. Businesses can even get a list of suggested keywords for a specific page based on related target keywords or the page’s URL.

What is Snowflake?

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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.

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 deliver the results from the cache swiftly. To avoid re-generating 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 communicate live, controlled data securely. It also encourages you to improve data relationships throughout your business units, as well as with your partners and customers.

Why Integrate Marketo to Snowflake?

Marketo offers marketing automation software that enables marketers to become experts in both the art and science of digital marketing to engage clients and prospects. 

Snowflake offers the Data Cloud, a global network with nearly limitless scale, concurrency, and performance that enables the data mobilization of thousands of organizations. 

Integrating Marketo to Snowflake simplifies data problems. For advanced marketing analytics, multi-touch attribution, and other features, centralize your Marketo to Snowflake. Marketo to Snowflake unifies its fragmented data inside the Data Cloud so that it can be easily discovered, shared securely, and used for a variety of analytic workloads.

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Key Methods to Migrate Marketo to Snowflake

Method 1: Using Hevo to Set Up Marketo to Snowflake

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Hevo provides an Automated No-code Data Pipeline that helps you move your Marketo to Snowflake. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ data 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 securely and consistently with zero data loss.

Using Hevo, you can connect Marketo to Snowflake in the following 2 steps:

  • Step 1: Configure Marketo as the Source in your Pipeline by following these steps:
    • Step 1.1: For your Marketo instance, get authenticated access credentials.
    • Step 1.2: In the Asset Palette, select PIPELINES.
    • Step 1.3: In the Pipelines List View, click + CREATE.
    • Step 1.4: Select Marketo on the Select Source Type page.
    • Step 1.5: Enter the following information on the Configure your Marketo Source page:
marketo to snowflake: configure marketo as source
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  • Pipeline Name: A distinct name for the Pipeline that isn’t longer than 255 characters.
  • Client ID: Accessible at the recently launched service.
  • Client Secret: Accessible at the recently launched service.
  • Endpoint: The primary URL that is used for all API requests.
  • Identity Endpoint: The endpoint where access tokens can be retrieved using the client secret and client ID.
  • Step 1.6: Simply press TEST & CONTINUE.
  • Step 1.7: Configure the data ingestion and establish the destination after that.
  • 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.
marketo to snowflake: configure snowflake as destination
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  • Step 2.5: Click Test Connection to test connectivity with the Snowflake warehouse.
  • Step 2.6: Once the test is successful, click SAVE DESTINATION.

Deliver smarter, faster insights with your unified data

Using manual scripts and custom code to move data into the warehouse is cumbersome. Changing API endpoints and limits, ad-hoc data preparation, and inconsistent schema makes maintaining such a system a nightmare. Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work.

  • Wide Range of Connectors: Instantly connect and read data from 150+ sources including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
  • In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation. 
  • Near Real-Time Replication: Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.   
  • Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so you don’t face the pain of schema errors.
  • Transparent Pricing: Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
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Method 2: Using Custom Code to Move Data from Marketo to Snowflake

You use an indirect method to connect Marketo to Snowflake. First, you connect Marketo to Redshift and move data from Redshift to Snowflake.

Marketo to Redshift

To connect Marketo to Snowflake, first, you have to create an Access Token for Marketo API, log in to your Marketo account, and open the Admin page. Expand the Access API section and scroll down and select Read-only Lead or any other access control option based on your requirements. You will now need to create an API-only user and associate it with the API role that was created previously. Once the required information about the user has been created, click Invite.

Next, to connect Marketo to Snowflake, you Extract Leads Data using Marketo API, You can extract the Lead details from Marketo in JSON form using the Access Token generated previously. Marketo can return a maximum of 300 results per request. If the results consist of more than this number of entries, the API response will contain two additional parameters, i.e., moreResults and nextPageToken. To Transform the JSON Data to CSV, you can convert the JSON output to a CSV file.

Luckily, there are several tools available that can make this possible. The CSV data can then be copied and saved in a file in your local system. To load the CSV Data to Redshift, users can load their data exported from Marketo to Redshift using AWS Management Console and Amazon S3.

To learn more click here.

Redshift to Snowflake

Next, to connect Marketo to Snowflake you move data from Redshift to Snowflake.

Database Objects Migration

The first step is, to begin with, database objects, which primarily include Schema, Table Structures, Views, etc. Instead of changing the object’s structure during migration, we should prefer to leave it alone because doing so would harm the entire migration process. Later, DB objects in Snowflake must be created with the same structure as those in Redshift.

Data Migration
  • Any project involving migration must include this activity. The first step is to determine the historical data sets for each table and how they can be migrated because the data volume will be quite high and we need to take this into account before beginning data migration activity. It is strongly advised to create separate batches for each table (based on the filter options like transaction date or any other audit columns) and migrate data in these batches rather than in one batch. 
  • After all historical data from all tables has been transferred to Snowflake, it will be relatively easy to move incremental data.
  • Using Redshift’s “Unload Command” to unload data into S3 and Snowflake’s “Copy Command” to load the data from S3 into Snowflake tables could be one solution. This may also display some errors caused by compatibility problems, which you have already experienced on several occasions. 
  • Another strategy involves using any data replication tool that supports Snowflake as a target, which can then be used to load raw data from the source system into Snowflake. ETL/ELT pipelines can fill facts, dimensions, and metrics tables on the Snowflake platform on top of this raw data.
Migrating Code to Snowflake 
  • Compared to the previous two steps, this one has fewer difficulties and restrictions. In theory, Redshift and Snowflake both support ANSI-SQL, but they use different formats for various items, such as the lack of DISTKEY, SORTKEY, and ENCODE concepts in Snowflake. 
  • There are many other examples of using functions, but one of the key distinctions between date functions is that Redshift’s GETDATE() and Snowflake’s CURRENT TIMESTAMP() are both date functions. While JSON, AVRO, and PARQUET are semi-structured data types that can be supported by Snowflake’s “VARIANT” datatype, they cannot be directly stored in Redshift.
  • To match the JSON field names in Redshift, the target table must be created by examining the JSON source data. Without a set structure, we cannot import it directly. Only the first-level elements can be parsed into the target table by the COPY functions. 
  • As a result, the multi-level elements are loaded into a single column and treated as strings. Redshift provides JSON SQL functions that must be used to further parse the intricate, multi-level data structures or arrays of JSON files. When migrating code, one must be extremely cautious and convert the code to supported SQL syntax.
Data Comparison between Redshift & Snowflake
  • The final step in any migration project is to compare the data sets from the legacy and newly migrated platforms to make sure that everything was successfully migrated and that the output was accurate for the business. 
  • Given that you are moving from Redshift to Snowflake in this scenario, you must contrast the outcomes from the two systems. Data comparison between Redshift and Snowflake manually is a time-consuming and difficult task.
  • Therefore, you created a custom Python script solution to connect to each DB, run some crucial checks against both DBs (Redshift as the source and Snowflake as the target), and compare the results.
  • Some crucial checks include Record Counts, Data Type Comparisons, Metrics Comparisons in Fact Tables, DB Object Counts, Duplicate Checks, etc. A daily CRON job can be scheduled or automated to execute this solution.

Conclusion

This article discusses the different methods to Connect Marketo to Snowflake extensively. In addition to that, it also describes Marketo and Snowflake briefly.

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This platform allows you to transfer data from 150+ sources (including 40+ Free Sources) such as Marketo 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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