How to Enhance Performance Bottlenecks with Large-Scale Data in ServiceNow Using Databricks

In enterprise digital transformation, data is both an asset and a liability—especially when it scales beyond expectations. For organizations leveraging ServiceNow, managing performance at scale becomes a critical concern. As tables such as Incident or CMDB_CI swell with millions of records, traditional list views, reporting mechanisms, and query builders start to choke—leading to slow response times, timeouts, and in some cases, outright system crashes.

So how do you tame this data beast? Enter Databricks.

The Challenge: ServiceNow at Scale

Let’s consider a real-world scenario. A leading financial services provider, managing a massive IT infrastructure, encountered severe performance degradation in their Configuration Management Database (CMDB). With millions of Configuration Items (CIs) and ongoing updates, their CMDB_CI table had become bloated and unwieldy. As a result:

• CMDB Query Builder visualizations began timing out

• CMDB dashboards crashed under the weight of real-time queries

• Standard list views became unresponsive

• Reporting across related tables became nearly impossible during peak usage

These issues didn’t just inconvenience IT staff—they impaired critical decision-making, delayed incident resolution, and raised compliance risks due to limited visibility.

The Solution: ServiceNow + Databricks Integration

To overcome these performance bottlenecks, the organization implemented a two-fold strategy:

1. Data Archiving

They started by identifying historical, less-accessed records in the CMDB_CI table and related tables. These records were archived into a secondary datastore, thereby reducing the load on active ServiceNow tables. This brought immediate improvements in query response times and dashboard reliability.

2. Deep Analytics with Databricks

While archiving improved performance, it introduced a new challenge—how to continue deriving insights from archived data?

This is where Databricks entered the picture. With its ability to handle large-scale data processing and analytics, the financial provider set up an ETL pipeline to move archived ServiceNow data into

Databricks' Lakehouse platform. Here’s how it worked:

• ServiceNow data was extracted using APIs or MID Server connectors.

• Data was cleaned, normalized, and ingested into Delta Lake.

• Databricks notebooks were used to build complex analytics and ML models.

• Dashboards were built using Databricks SQL and integrated with BI tools like Power BI or Tableau.

The result?

Stakeholders could now run cross-system, high-volume queries in seconds and gain predictive insights without impacting live ServiceNow performance.

Benefits Delivered

1. CMDB query performance improved by over 60%

2. Reduced incident triage time due to faster access to CI dependencies

3. Empowered analytics teams to run advanced queries on historical data

4. Maintained compliance through audit-friendly archiving and retention policies

5. Enabled predictive maintenance strategies using machine learning on CI trends

Key Takeaways

• ServiceNow, while powerful, needs performance tuning and smart integrations at scale.

• Archiving historical data is essential for maintaining operational speed.

• Databricks can supercharge your analytics capabilities without taxing your live instance.

• The combination of real-time ITSM with batch analytics creates a future-proof data strategy.

Final Thoughts

If your organization is struggling with performance issues due to large datasets in ServiceNow, integrating with a scalable analytics platform like Databricks is not just a nice-to-have—it’s a necessity. With the right data architecture, you can strike a balance between operational performance and strategic insight.

Want to know how REDE Consulting can help you design and implement such scalable solutions?

Let’s connect. Reach out to REDE's Databricks Expert team to help you. Mail us at info@rede-consulting.com for a no cost or obligation discussion today.