Integrating ServiceNow with a Data Lake or Data Warehouse

Data lakes and data warehouses are data storage solutions that are often integration targets for ServiceNow.

In this post, we address the differences between data lakes and warehouses, to help ServiceNow users make informed decisions as to what to integrate their ServiceNow data with.

What Data Lakes and Data Warehouses Have In Common

Data lakes and data warehouses are similar, in that they help organizations store and use data more efficiently. Both allow data to be collected from a number of sources.

As such, connecting ServiceNow to data lakes and data warehouses via integrations is common. 

Such integrations break down data silos, allowing organizations to combine data from disparate sources to create a unified view – a single source of truth – to support operations and business decisions. 

Example use cases for integrating ServiceNow data with a data lake/warehouse include analytics & reporting, data archiving, backups, business intelligence, machine learning, and artificial intelligence. 

Data Lakes vs. Data Warehouses: Key Differences

While data lakes and data warehouses both provide a means of storing data, the way in which they store that data differs. Neither approach is inherently better. Rather, each of them have their advantages in supporting particular use cases.

The key differences are as follows:

Structure & schema

Data lakes and warehouses support different types of data. With a data warehouse, data must be structured – meaning it is transformed to meet the requirements of a predefined schema – and organized to support a specific process. This is referred to as “schema-on-write”.

In contrast, data lakes have no constraints in terms of storing structured data, accepting a range of data in their native format. Schema is applied as and when the data is queried or analyzed in an approach known as schema-on-read.

Data quality

As data warehouses store structured data, data quality is generally better. Functions such as de-duplication, sorting and summarizing can be done before data is stored. As such, organizations can have greater confidence in the data they are retrieving.

With data lake’s schema-on-read approach, data quality should be verified after it is retrieved. 


Data warehouse’s schema-on-read approach benefits query performance. As the data is structured and organized,  it can be easily queried and retrieved. This makes data warehouses an efficient storage solution for regular reporting and analytics.

Data lake’s unstructured approach makes queries less efficient, but data can still be retrieved at reasonable speeds. As data lakes provide higher storage volumes at a lower cost, organizations should consider whether storage volumes should take priority over the fastest possible query performance.

Choosing the Right Solution for Integrating ServiceNow with Data Lakes and/or Data Warehouses

Taking the time to thoroughly evaluate integration options can save organizations from amounting a considerable technical debt. When rushing the process, common mistakes include:

  • Selecting an integration solution that cannot meet the organization’s requirements for throughput
  • Using an integration method that impacts the performance of the source and/or target
  • Underestimating the organization’s capacity to implement and maintain an integration over time

As well as avoiding technical debt, an awareness of the best practices to follow when integrating ServiceNow helps organizations increase time-to-value.

Particularly when integrating ServiceNow with a Data Lake and/or data warehouse, organizations should make the following considerations:


Both data warehouses and data lakes benefit from integration solutions capable of higher throughput. The more data that can be transferred without impacting ServiceNow’s performance, the better. 

While API-based integration solutions are widely available, the process they use to retrieve data impacts ServiceNow’s performance when transferring large data volumes. 

For organizations with large volumes of ServiceNow data, there is a ServiceNow-approved, native application capable of transferring over 1 million records per day with no impact to performance.

In-house or outsourced?

Organizations should also consider whether they have sufficient resources to implement and maintain integrations over time.

Insufficient resources can extend an integration project’s implementation and time-to-value, disrupting operations in the process. Similar is true when issues with the integration can not be efficiently solved by internal teams. 

Outsourcing integration implementation and management helps avoid such issues.

Pre- or post-send transformation?

Data warehouse’s schema-on-write approach requires data to be structured. With this in mind, a ServiceNow-to-data warehouse integration solution will require the ability to transform data pre-send. 

High-throughput ServiceNow Integrations, from Perspectium

Whether you’re planning to integrate ServiceNow with a data lake or data warehouse, Perspectium can help. 

Perspectium is a ServiceNow-native, integration-as-a-service (IaaS) solution designed by ServiceNow’s founding developer, David Loo. It supports a wide range of ServiceNow integration and replication use cases, including data lake and data warehouse building.

With Perspectium, ServiceNow users have an API-free, high-throughput integration solution capable of transferring over 1 million records per day. 

Data can be transferred pre- or post-send, meaning it arrives at the target destination in the correct format, ready for use. 

Perspectium’s integrations are delivered as a managed service, meaning we implement the solution on behalf of our customers.

Following implementation, Perspectium also maintains the solution and provides support 24/7 so our customers can enjoy a no-code experience.

The end-user controls data transfers via a ServiceNow-native application, meaning they benefit from using the ServiceNow UI with which they are already familiar. 

Speaking to the quality of Perspectium’s solution and service, ServiceNow themselves are a Perspectium customer:

The organization uses Perspectium to transfer internal ServiceNow data to external repositories to support the data requirements of Sales, Marketing and other teams within the organization.

Are you interested in learning more about our integration solutions? If yes, contact us today!

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