Major Telco Boosts Data Ops With Cloudian, MemSQL & Druid

In a bold move to modernize its telecom data operations, a major telco deployed Cloudian object storage, integrated MemSQL analytics and Apache Druid real-time analytics platforms. This initiative revitalised their telecom data operations, delivering scalable object storage, real-time data processing and cost-effective data storage for their large-scale telecom data management needs. The transformation highlights how telecom organizations can integrate Cloudian Storage with MemSQL and Druid to become analytics-driven and agile.

This article will walk through why telecom data operations require change, how the architecture combining Cloudian, MemSQL and Druid was designed, what benefits the telco realized, and how your organization can replicate similar success. We will explore telecom data management best practices, highlight key integration steps, and examine how AI-enabled data analytics and real-time analytics with AI are leveraged in this environment.

Why Telecom Data Operations Needed a Transformation

The telco faced an explosion of data: call detail records, network telemetry, customer usage logs, streaming events and more. Legacy systems struggled with scale, cost and performance. Telecom data operations became a bottleneck and storage costs were mounting, analytics were slow and real-time insights were lacking.

They needed scalable object storage that could grow seamlessly, S3 API compatibility to leverage modern tools, real-time data processing to support streaming analytics, and a telecom data lake capable of supporting both historical and near-instant analytics. Deploying Cloudian storage, MemSQL analytics and Apache Druid real-time analytics answered these needs. A case study from Cloudian confirms this deployment at scale.

Architectural Overview of Cloudian + MemSQL + Druid in Telecom Data Operations

Cloudian Storage as the Foundation – Scalable Object Storage with S3 API Compatibility

The foundation of the telco’s new architecture is Cloudian object storage. As a highly scalable, S3-compatible solution, it allows the telco to store petabytes of data at a lower cost than traditional SAN/NAS. The S3 API compatibility means existing tools for big-data ingestion and analytics integrate seamlessly.

Cloudian’s role: bulk storage for raw and processed data, supporting the telecom data lake and feeding both MemSQL and Druid.

MemSQL Analytics Layer for Structured and Semi-Structured Workloads

MemSQL serves as the analytics engine for structured and semi-structured data sets. With high-performance SQL queries, it empowers analysts to run complex ad-hoc queries, dashboarding and reporting on telecom usage, churn, revenue and network performance data.

Apache Druid Real-Time Analytics Layer for Fast Event Processing

Apache Druid provides streaming ingestion and real-time queries—perfect for handling telemetry, event data and network operations metrics. It supports sub-second latency queries, enabling network engineers and business users access to near-real-time insights.

Integration: From Raw Data to Real-Time Insight

In this architecture, data flows from network systems and usage logs into Cloudian storage. Then streaming pipelines feed Druid for real-time processing, while batch and semi-structured data land in MemSQL for deeper analytics. Governance, cataloging and orchestration tie the system together.

Key Benefits realized by the Telco

Cost-Effective Data Storage and Lower Total Cost of Ownership

By shifting to Cloudian storage, the telco achieved significant cost savings versus legacy systems. The object storage model allowed them to scale without locking into expensive hardware refresh cycles.

Improved Real-Time Analytics for Network and Customer Intelligence

With Druid in place, the telco gained sub-second analytics on network telemetry and customer events. This improved response times, fault detection and operational agility.

Unified Data Platform for Structured and Streaming Workloads

Using MemSQL and Druid together allowed the telco to unify analytics across historic structured data and new streaming data—reducing silos and accelerating insight.

Higher Scalability and Future-Proof Architecture

The scalable object storage of Cloudian made it possible to handle future growth—exabytes of data, more devices, more events without rewriting architecture.

Better Decision-Making and Faster Time-to-Insight

Business stakeholders now access telecom analytics platform solutions with less delay. Real-time dashboards and predictive modelling support marketing, network operations and customer experience teams.

Steps to Implement a Cloudian + MemSQL + Druid Telecom Data Operations Platform

1. Define Use-Cases and Business Value Focus

Begin by identifying what aspects of telecom data operations need improvement: network reliability, churn, revenue leakage, and real-time network monitoring. Anchor the architecture around those requirements—how to boost telecom data operations.

2. Design Scalable Object Storage Strategy

Determine storage tiers, lifecycle policies, compliance needs, S3 API endpoints and performance metrics. Cloudian is selected for its scalable object storage and S3 compatibility.

3. Architect Analytics Engines (MemSQL + Druid)

Choose where to use MemSQL (structured SQL workloads) vs Druid (real-time event streaming). Define ingestion pipelines, retention policies and query patterns. “Integrating MemSQL and Apache Druid” becomes central.

4. Build Data Ingestion and Streaming Pipelines

Use streaming frameworks (such as Kafka or Pulsar) to feed Druid, and batch pipelines for MemSQL. Ensure telecom data lake is fed continuously and analytics stay current.

5. Implement Governance, Metadata and Data Management

Scale requires governance: cataloging, lineage, access controls, and audit logs. Telecom data management must satisfy regulatory and competitive standards.

6. Enable Self-Service Analytics and Real-Time Dashboards

Equip network operations, marketing and customer experience teams with dashboards, real-time analytics and business intelligence tools powered by the architecture.

7. Monitor, Optimize and Scale

Track performance, cost metrics (storage, compute), query latency, and user adoption. Continuously optimize the architecture for cost-effective data storage and real-time data processing.

Best Practices and Pitfalls in Large-Scale Telecom Data Management

Common pitfalls in telecom data operations include uncontrolled storage growth, lack of integration between real-time and batch analytics, poor governance and siloed tools. Avoid these by adopting integrated platforms, enforcing metadata and cataloging, and aligning analytics with business outcomes.

Best practices include selecting S3-compatible scalable object storage like Cloudian, combining structured analytics (MemSQL) with real-time streaming analytics (Druid), and building pipelines that accommodate future growth.

How Solix Complements Telecom Data Operations Platforms

While the telco case centers on Cloudian, MemSQL and Druid, another component worth considering is Solix for data management, governance and archiving. Solix provides enterprise-grade data operations tools, embedding governance, metadata, cataloging and archiving seamlessly into large-scale data platforms.

For telecom organizations, Solix helps with: scalable data retention policies, cost-effective tiered storage, compliance and audit readiness, cross-platform data integration and support for AI-enabled data analytics. Combining Cloudian + MemSQL + Druid with Solix can further boost telecom analytics platform solutions and optimize large-scale telecom data management.

Frequently Asked Questions

What benefits do Cloudian storage, MemSQL analytics and Apache Druid real-time analytics bring to telecom data operations?

Cloudian offers scalable object storage with S3 API compatibility, reducing cost and scaling effortlessly; MemSQL provides high-performance SQL analytics on structured data; Apache Druid supports streaming ingestion and real-time queries for event-driven workloads. Combined, they enable efficient, real-time, cost-effective telecom data management.

How does scalable object storage support real-time analytics in telecom?

Scalable object storage allows massive volumes of raw and processed data to be kept affordably, while streaming engines (like Druid) can ingest and query this data in near-real-time. The S3-compatible interface allows diverse analytics tools to connect easily.

Why integrate both MemSQL and Druid rather than choose one analytics engine?

Each engine has strengths: MemSQL excels at structured, SQL-based analytics; Druid excels at real-time, event-stream analytics with sub-second queries. A combined approach covers both historical and real-time needs in telecom data operations.

What are common pitfalls when deploying a telecom analytics platform at scale?

Pitfalls include uncontrolled data growth leading to cost overruns, lack of governance causing data silos, insufficient real-time capability resulting in delayed insight, and business teams not adopting the new platform. Address these early with architecture, governance and change management.

How should organizations start when looking to boost telecom data operations with modern analytics platforms?

Start by defining high-value use cases (e.g., network anomaly detection, real-time churn prediction), assess your current data infrastructure, choose scalable object storage like Cloudian, identify analytics engines (MemSQL and Druid), implement governance practices and measure impact over time.

DISCLAIMER: THE CONTENT, VIEWS, AND OPINIONS EXPRESSED IN THIS BLOG ARE SOLELY THOSE OF THE AUTHOR(S) AND DO NOT REFLECT THE OFFICIAL POLICY OR POSITION OF SOLIX TECHNOLOGIES, INC., ITS AFFILIATES, OR PARTNERS. THIS BLOG IS OPERATED INDEPENDENTLY AND IS NOT REVIEWED OR ENDORSED BY SOLIX TECHNOLOGIES, INC. IN AN OFFICIAL CAPACITY. ALL THIRD-PARTY TRADEMARKS, LOGOS, AND COPYRIGHTED MATERIALS REFERENCED HEREIN ARE THE PROPERTY OF THEIR RESPECTIVE OWNERS. ANY USE IS STRICTLY FOR IDENTIFICATION, COMMENTARY, OR EDUCATIONAL PURPOSES UNDER THE DOCTRINE OF FAIR USE (U.S. COPYRIGHT ACT § 107 AND INTERNATIONAL EQUIVALENTS). NO SPONSORSHIP, ENDORSEMENT, OR AFFILIATION WITH SOLIX TECHNOLOGIES, INC. IS IMPLIED. CONTENT IS PROVIDED "AS-IS" WITHOUT WARRANTIES OF ACCURACY, COMPLETENESS, OR FITNESS FOR ANY PURPOSE. SOLIX TECHNOLOGIES, INC. DISCLAIMS ALL LIABILITY FOR ACTIONS TAKEN BASED ON THIS MATERIAL. READERS ASSUME FULL RESPONSIBILITY FOR THEIR USE OF THIS INFORMATION. SOLIX RESPECTS INTELLECTUAL PROPERTY RIGHTS. TO SUBMIT A DMCA TAKEDOWN REQUEST, EMAIL INFO@SOLIX.COM WITH: (1) IDENTIFICATION OF THE WORK, (2) THE INFRINGING MATERIAL’S URL, (3) YOUR CONTACT DETAILS, AND (4) A STATEMENT OF GOOD FAITH. VALID CLAIMS WILL RECEIVE PROMPT ATTENTION. BY ACCESSING THIS BLOG, YOU AGREE TO THIS DISCLAIMER AND OUR TERMS OF USE. THIS AGREEMENT IS GOVERNED BY THE LAWS OF CALIFORNIA.