Replicating ServiceNow data in a data lake is a common use case for ServiceNow integrations.

As ServiceNow is essentially a data silo, accessible only to licensed users, data lakes help organizations democratize access to its valuable data and insight, and more.


In a data lake, ServiceNow data can be combined with data from other sources creating a unified view of enterprise data – or “a single source of truth” – and insight benefits from greater context. 

The data within a data lake can be used for predictive analytics, machine learning, data visualization, business intelligence, and more.

What is a Data Lake?

A data lake is a centralized and scalable repository for storing data. In a data lake, data can be stored in its raw, unstructured format and without schema or constraints such as size limits. 

Data lakes can be used for tasks such as predictive analytics and reporting, or even act as a source of data for other applications.  

They can support business leaders seeking to obtain a 360-degree view of their operations, and provide different teams and systems with the data they require to function.

As data lakes often contain large volumes of data, they can also be a good source of data for machine learning and artificial intelligence initiatives.

Benefits of Replicating ServiceNow Data in a Data Lake

Integrations that replicate ServiceNow data in a data lake deliver the following benefits: 

Improved ServiceNow performance

Running data-heavy reports and/or performance analytics on ServiceNow consumes system resources, slowing down performance. Organizations can instead run complex reporting and analysis operations within the data lake, or in dedicated reporting and analytics solutions integrated with the data lake.

Greater data democratization

Replicating ServiceNow data in a data lake allows organizations to efficiently distribute data throughout the enterprise without purchasing additional ServiceNow licenses. This promotes self-serve analytics, enabling employees to better use the data and collaborate with colleagues across solutions and departments.

Improved data access availability

Multiple data copies across various locations reduce latency, optimize data retrieval, especially for remote users, and reduce cache misses.

Enhanced application reliability

Data replication supports data availability, reducing downtime and strengthening system reliability, even during hardware failures or interruptions.

Easy backups and data recovery

Data replication can help create multiple copies of data, improving disaster recovery capabilities in the event of system failures.

Replicating ServiceNow Data in Data Lakes vs. Data Warehouses

While data lakes and data warehouses are both storage repositories, they are distinctive in a number of key areas.

Data lakes can handle massive volumes of data in its raw form, meaning data does not need to be processed before being ingested by the data lake.

This means data lakes are more flexible, and less time-consuming to manage when compared with data warehouses – where data must be processed before it is stored. 

Data lakes are also less costly than data warehouses, with a significant factor in this being the more straightforward maintenance requirements resulting in lower operational costs. 

However, a data lake’s more flexible approach to data storage does mean they are less business-end user friendly than data warehouses. 

The data in a data lake is typically accessed by data scientists, engineers, etc. who prefer to work with data in its raw form. As the data in a data warehouse is structured, it is in a ready state for business-end users seeking insight. 

However, business-end users still benefit from data lakes, as the data can be fed into other solutions and applications that business-end users can work with.

Data Lakes vs. Data Warehouses: Key differences

Structure & schema

Data Lake: Accepts a variety of data formats without requiring immediate structuring. Schema is applied when the data is queried or analyzed, known as “schema-on-read.”

Data Warehouse: Follows the “schema-on-write” rule, wherein data must be structured according to a predefined schema before being stored. 

Data Quality

Data Lake: Data quality verification occurs after data retrieval due to the schema-on-read approach.

Data Warehouse: The requirements for structured data enables pre-storage data processing such as deduplication and sorting – benefiting the quality of data within the repository.


Data Lake: Query efficiency may be lower due to unstructured data, but data retrieval speeds remain reasonable.

Data Warehouse: Schema-on-read approach enhances query performance for structured and organized data, making it efficient for daily reporting and analytics.

How to Replicate ServiceNow Data to a Data Lake?

Since data lakes are designed for storing large amounts of data, relying on manual data extraction and replication methods is unsuitable. 

Instead, organizations should utilize integrations as they provide an automated, consistent means of replicating data externally.

However, organizations must consider the type of integration they implement carefully, and the impact it may have on ServiceNow and business operations.

DIY/Custom integrations

Integrations built, implemented and maintained internally, allowing the organization to implement a solution for its specific requirements. However, planning and implementing an integration is a significant undertaking that requires expertise and experience – particularly for business critical solutions like ServiceNow.

While organizations may aim to implement an integration that meets their requirements, there is no guarantee that the goal will be realized, leading many custom integrations to become a costly technical debt

Significant resources are required for implementation and maintenance – adding significant hidden costs over time – and the integration is also threatened by developer turnover and poor documentation. 

If maintaining the integration is not the developer’s primary role, time spent on implementing and maintaining the integration will interfere with and delay other tasks.

Integration platforms

Integration platforms (integration platforms as-a-service/iPaaS) are pre-built solutions for integrating systems. 

They typically aim to serve common use cases, where requirements can be safely assumed and are built to be versatile and connect to common systems. As such, they use common technologies such as API to facilitate the transfer of data between systems. 

As they are pre-built, they save resources and time during the implementation stage. However, configuration and maintenance requirements are on the end-user which often includes significant customization to tweak the integration to meet specific needs. 

Poorly configured API can lead to missing data and outright integration failure. And as data volumes increase, API struggles to meet the throughput requirements. 

When integrating ServiceNow, API also leads to performance issues on the platform itself, as it requires ServiceNow’s operational bandwidth to facilitate data transfers. This degrades performance as the amount of queries and volumes of data queried escalate.

Integration outsourcing / Integrations-as-a-service

Integration outsourcing and integrations-as-a-service (IaaS) solutions provide a means of connecting systems without requiring internal resources to build, implement, or maintain the solution.

With integration outsourcing, organizations insulate themselves from integration-related technical debt, paying for the integration service as needed, via a subscription model. 

The burden of integration maintenance and the cost of maintaining the necessary infrastructure  also shifts to the IaaS provider.

With maintenance handled by external experts, IaaS customers also avoid the threat of poor documentation and developer turnover threatening the continuity of the integration.

As IaaS solutions are implemented and maintained by experts, they can be tailor-made to meet organization’s requirements. It also means that they’re not limited to using common technologies such as API, and can instead leverage more efficient technology capable of higher throughput. 

Quickly Populate ServiceNow Data in a Data Lake, with Perspectium

Created by ServiceNow’s founding developer, Perspectium is an integration-as-a-service and solution, purpose built to transfer massive data volumes out of ServiceNow. 

With Perspectium, users can replicate millions of records per day with no impact to the platform’s performance, making it a perfect integration solution for replicating ServiceNow data in a data lake and other targets.

Perspectium is able to achieve such high throughput and performance by leveraging its native installation within ServiceNow

Instead of relying on external API calls, Perspectium uses push technology to transfer data out into a message broker system. Records are then retrieved from the MBS to ensure minimal ServiceNow resources are consumed, allowing for massive data volumes to move in real-time.

Its native installation also benefits ServiceNow users, by allowing them to interact with the platform via the familiar ServiceNow user interface. 

By partnering with Perspectium, organizations can enjoy a number of benefits, including: 

Populate data lakes fast!

Perspectium users can transfer over 1 million records daily, without impacting performance, meaning data lakes and other repositories can be populated fast.

Outsource implementation and maintenance

Perspectium solutions are implemented and maintained by Perspectium, meaning the end-user can make more productive use of internal development resources. 

API-free solution

Perspectium does not rely on API to transfer data out of ServiceNow. As such, users experience better performance in their ServiceNow instances, and there are no requirements to configure API, or the risk of misconfiguration and/or API-related security concerns. 

Real-time data transfer

Perspectium integrations ensure that relevant stakeholders have access to the right data at the right time and in the desired format. 

ServiceNow-native application

Perspectium’s ServiceNow-native installation means ServiceNow users have a familiar UI to work within, benefitting the learning curve. 

Talk to our experts today to know more about our hassle-free integration solutions!

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