Overview

Engagement Overview

A global networking technology provider sought to improve the performance and flexibility of its data platform to better support real-time analytics and increasing data volumes. Its existing Hive-based environment was primarily designed for batch processing, which met historical needs but limited responsiveness and cross-team collaboration.

Working closely with the client’s data teams, a Databricks-based platform was introduced to support faster processing, improved scalability, and more collaborative analytics workflows. The initiative focused on modernizing the data foundation while enabling engineers and analysts to operate within a shared, more responsive environment.

Our Client

Telecommunications   United States
The client is a global provider of networking technologies, delivering secure and scalable connectivity solutions for enterprises and service providers. Its platforms support mission-critical network operations across industries such as healthcare, education, financial services, and the public sector.
Idanim

Business Objective

As analytics requirements expanded, the organization aimed to adopt a more flexible and scalable data platform capable of supporting near real-time insights and advanced analytics.

01

Modernize the existing data environment to simplify configuration, upgrades, and maintenance

02

Improve query execution performance to accelerate access to insights

03

Expand beyond batch-oriented processing to enable near real-time analytics use cases

04

Strengthen scalability to support system stability and operational efficiency

05

Enable stronger collaboration between data engineers, scientists, and analysts through a shared platform

06

Improve interoperability with modern ML frameworks and Python-based analytics

Our Solution

A Databricks-based data platform was implemented to address performance, scalability, and collaboration requirements.

  • Data workloads were migrated from Hive to Databricks to support improved scalability and performance
  • Auto-scaling clusters were configured to balance availability, performance, and cost efficiency
  • A shared data environment was established to support collaboration between engineering, science, and analytics teams
  • Near real-time data availability enabled more timely analysis and reporting
  • Delta Tables were used to help maintain data accuracy and consistency
  • Tableau was integrated to support interactive and business-friendly dashboards

Business Outcomes

The modernized data environment helped improve query performance, team collaboration, and access to real-time insights.

70% reduction in query execution time through optimized compute and caching

Real-time analytics capabilities helped reduce reliance on batch-only reporting

Improved system stability and scalability through automated cluster management

Improved cross-functional Collaboration across data engineering, science, and analytics teams

Reduced TCO through Databricks’ pay-as-you-go pricing model

Tech stack

  • Data Engineering
  • Data Visualization
  • Programming
  • Data Storage
Cloud
Database
python
delta