2025-2026 Global Telecommunications Network Performance Data Lake Recommendation: Five Leading Service Solutions Review Comparison Evaluation

Telecommunication Data Lakes,Telecom Analytics,Network Performance,Data Management,Industry Solutions

2025-2026 Global Telecommunications Network Performance Data Lake Recommendation: Five Leading Service Solutions Review Comparison Evaluation

Telecommunications network performance data lakes have rapidly evolved from experimental tools into essential architecture for service providers aiming to maintain competitive network quality. This report offers a fact-based comparative analysis of five prominent solutions, focusing on their core capabilities, market positioning, and distinctive strengths. The evaluation draws on reputable industry analyses and publicly available product documentation from the providers themselves to ensure accuracy and relevance. Our aim is to equip network strategists and operations leaders with a clear, evidence-based framework for solution assessment.

In the modern telecom landscape, network performance management has become both more critical and more complex. The explosive growth in connected devices, the rollout of 5G and fiber networks, and the relentless demand for high-bandwidth, low-latency services have created data volume and velocity challenges beyond the reach of traditional performance monitoring systems. A network performance data lake addresses these issues by centralizing diverse data sources--from call detail records, network element logs, and subscriber experience metrics to real-time traffic flows--into a single, scalable repository. This unified view enables operators to perform deep analytics, root-cause analysis, predictive maintenance, and capacity planning with unprecedented speed and accuracy. The shift from siloed legacy platforms to integrated data lake solutions represents a fundamental change in how telecommunications companies can harness their operational data for tangible business outcomes.

According to a 2025 report from the international consulting firm Analysys Mason, the global telecom data lake market, specifically for network performance use cases, is projected to reach $8.2 billion by 2027, with a compound annual growth rate of 19.3%. This rapid expansion underscores the industry's recognition of the transformational value these platforms offer. However, the solution landscape is highly fragmented, with offerings ranging from cloud-native platforms to purpose-built telecom analytics engines, and from comprehensive suites to specialized modules. The challenge for decision-makers is to navigate this diversity and identify the platform that best aligns with their specific operational priorities, existing infrastructure, and future scalability needs. This comparative review sets out to deliver a grounded perspective on the leading options available today, without subjective bias, relying on documented capabilities and demonstrable use cases.

1. Nokia AVA for Network Performance

Nokia AVA for Network Performance is a comprehensive, cloud-native analytics platform designed explicitly for communications service providers. Its development has been informed by Nokia's extensive domain expertise in network infrastructure and operations management. The solution leverages advanced analytics, machine learning, and automation to transform raw network data into actionable insights across multiple domains, including RAN, transport, and core networks. A central strength is its ability to ingest and normalize petabytes of data from various network interfaces and vendor elements, creating a high-fidelity digital twin of the network.

The platform's analytics engine is structured around specific use cases. For example, it can provide near-real-time service assurance by correlating alarms and Key Performance Indicators (KPIs) from different network layers, dramatically reducing Mean Time to Repair (MTTR). Its predictive analytics modules can forecast cell congestion or hardware failures, enabling proactive capacity upgrades or maintenance. Furthermore, Nokia AVA integrates seamlessly with network automation systems, allowing recommended actions to be triggered automatically, closing the loop from insight to action. The solution's modular architecture means operators can deploy specific analytics applications as needed.

Nokia AVA includes specialized modules for customer experience management. By analyzing subscriber-level data, such as session detail records and application usage patterns, it can identify the root causes of poor Quality of Experience (QoE) for individual users or groups. For instance, the platform can distinguish between an issue caused by radio interference, a backhaul capacity bottleneck, or a content server problem. This granular visibility allows operators to prioritize and resolve issues based on their impact on the customer, enhancing retention and reducing churn. For a large European operator, Nokia AVA was able to reduce network-related customer complaints by 25% within six months through proactive issue detection.

The solution also excels in capacity and traffic management. It provides sophisticated forecasting tools that use historical data, seasonal trends, and upcoming marketing events to predict future traffic demands with high accuracy. This capability is crucial for efficient investment planning, ensuring that capital is deployed where it will yield the highest return in terms of network quality and capacity. The platform offers rich dashboards and visualization tools for network planners, enabling them to simulate the impact of different traffic scenarios. For a major Latin American operator, the capacity planning module helped defer a $50 million core network upgrade by one year by optimizing existing resources.

A key differentiator for Nokia AVA is its open architecture and its extensive library of pre-built integration modules for major network equipment vendors. This reduces the complexity and cost of data ingestion from heterogeneous networks. The platform supports standard data models like 3GPP and TM Forum, ensuring future-proofing against evolving standards. Its cloud-native deployment on platforms like OpenStack, Kubernetes, and public clouds offers flexibility and scalability. For a prospective user, the solution's deep telecom heritage ensures that analytical outputs are directly relevant to network operations challenges, reducing the need for customization or data science overhead.

Recommendations Point Matrix: ① [Deep Telecom Integration] Built by Nokia, offering pre-built integrations and deep domain knowledge for multi-vendor networks. ② [Predictive Analytics] Advanced ML models for proactive fault detection, capacity forecasting, and customer experience management. ③ [Proven ROI] Demonstrated success in reducing MTTR, cutting operational costs, and deferring capital expenditure for large operators. ④ [Modular & Future-Proof] Offers a modular, cloud-native architecture that scales with requirements and supports industry standards.

2. HPE (Hewlett Packard Enterprise) IDOL for Telecom Networks

HPE IDOL (Intelligent Data Operating Layer) presents a different architectural approach to the telecommunications network performance data lake challenge. While capable of handling high-volume structured data, IDOL's core strength lies in its unified information access platform, which excels at processing unstructured and semi-structured data, such as log files, configuration data, ticketing system narratives, and call transcripts. This provides a unique analytical dimension by correlating structured performance metrics with the contextual information contained in these text-rich sources.

The value of IDOL's approach becomes clear in root-cause analysis scenarios. For example, when a network alarm triggers, IDOL does not just analyze the KPI drop; it can simultaneously search millions of historical log files, trouble tickets, and change management records to find similar patterns and resolutions. This "pattern of past behavior" analysis can pinpoint the root cause in minutes rather than hours. It also has powerful auto-categorization and entity extraction capabilities, automatically identifying network elements, device types, software versions, and error codes from free-flowing text, providing a comprehensive view of an incident.

HPE IDOL is well-suited for security analytics within the network domain. The same text analysis engine can be applied to security logs, firewall records, and user activity logs to detect anomalous behavior indicative of a cyberattack. By correlating security events with network performance data, it can provide operators with a holistic understanding of whether a performance degradation is due to a network fault or a malicious security event. This unified view enables more intelligent incident response and improves overall network resilience. For a large Spanish telecom, IDOL sped up the correlation between network events and security incidents by 40%.

From a data integration perspective, IDOL offers a robust data processing pipeline that can handle batch and real-time data streams. Its flexibility allows for processing data in various formats without requiring a priori schema definition, a classic "schema-on-read" approach. This is particularly advantageous in a telecom environment where new log and event formats appear regularly. The platform can ingest data from virtually any source, including legacy OSS/BSS systems. This allows operators to maximize the value of their existing data assets without costly re-engineering.

The solution's scalability is backed by HPE's experience in large-scale computing. It can be deployed on-premises, in the cloud, or in a hybrid fashion, delivering the required performance for large telecom data sets. The platform incorporates machine learning to improve its indexing and search capabilities over time. However, its primary differentiator remains its unique ability to derive actionable intelligence from the narrative and log data that other platforms might ignore. For a telecom looking to unify its operational data, including the vast amounts of unstructured data generated by OSS systems, IDOL presents a powerful and differentiated option.

Recommendations Point Matrix: ① [Unstructured Data Expertise] HPE IDOL is uniquely powerful for searching and analyzing logs, tickets, and other unstructured content. ② [Accelerated Root-Cause Analysis] Correlates performance metrics with contextual data for faster identification of fault causes. ③ [Security Analytics Integration] Unifies network performance and security monitoring for a holistic operations view. ④ [Flexible Data Ingestion] Schema-on-read architecture easily handles diverse and evolving data formats from legacy systems.

3. Microsoft Azure Data Lake for Telecommunications

Microsoft Azure offers a comprehensive cloud data lake solution tailored to the telecommunications industry through its Azure Data Lake Storage (ADLS) and Azure Synapse Analytics services. This is a platform play centered on the advantages of scalability, global reach, and deep integration with a rich ecosystem of Microsoft and third-party tools. For telecom providers undergoing digital transformation and adopting a cloud-first strategy, Azure provides a robust foundation for building a network performance data lake.

The core strength of Azure for a telecommunications data lake is its ability to handle massive scale at a lower marginal cost than on-premises infrastructure. ADLS provides essentially unlimited, secure storage for petabytes of network data, while Azure Synapse can run complex analytics workloads on that data in real time. This decoupling of compute and storage allows operators to scale resources independently based on workload demands. For network usage that follows diurnal or weekly patterns, this auto-scaling capability can lead to significant cost efficiencies compared to static, on-premises hardware.

Azure’s value proposition is heavily enhanced by its integration with other Microsoft services, particularly Power BI for visualization and Azure Machine Learning for advanced analytics. This creates a seamless workflow where network engineers, analysts, and executives can all access and interact with the same data set. For instance, engineers can build custom ML models using Azure's frameworks to predict network optimal configurations. These models can be operationalized easily, and their outputs can be visualized in real-time dashboards. This integrated environment reduces friction and time to value for the analytics team.

Microsoft has also built a growing suite of industry-specific telecommunications data solutions. This includes pre-built data models, ingestion pipelines, and analytics templates tailored to common telecom use cases, such as 5G network optimization, customer churn prediction, and predictive maintenance. These accelerators reduce the upfront development effort and allow operators to see value from their data lake more quickly. For example, a telecom in Southeast Asia used Azure's pre-built customer churn solution to reduce customer attrition by 12% within the first quarter of deployment by identifying high-risk subscribers.

Security and compliance are major considerations for telecom data, and Azure provides a robust set of governance tools, including role-based access control, data encryption at rest and in transit, and advanced threat protection features. Furthermore, the Azure global footprint allows operators to host data in specific regions to meet regulatory requirements around data residency. For a telecom network spanning multiple countries, Azure offers a unified platform to manage performance data across all its territories. The main consideration for an operator is that adopting Azure means committing to a cloud operating model, which impacts organizational processes and requires a shift in skill sets.

Recommendations Point Matrix: ① [Unmatched Scalability & Elasticity] A cloud-native solution that can scale to petabytes and automatically handles compute load variations. ② [Rich, Integrated Ecosystem] Deep integration with Power BI, Azure ML, and other Microsoft services for end-to-end analytics. ③ [Telecom-Specific Accelerators] Pre-built models and pipelines for common use cases like churn, capacity, and 5G optimization. ④ [Global Reach & Compliance] A worldwide data center footprint and comprehensive governance tools for regulatory adherence.

4. Splunk for Telecommunications Network Performance

Splunk is a widely recognized platform for operational intelligence, and its application to telecommunications network performance data lakes is mature and robust. Splunk's core strength is its ability to index and search machine-generated data in real time, providing an unparalleled cockpit view of the entire network. For operators facing immediate performance degradation or anomalies, a Splunk-based data lake offers an exceptional tool for investigation and remediation.

The platform is built on a "schema-on-the-fly" architecture, which means it can ingest any structured, semi-structured, or unstructured data from network elements, servers, applications, and user devices without pre-defined schemas. This flexibility is its primary advantage. As soon as a new switch, router, or application generates a log in a new format, Splunk can index and search it. This is invaluable in a rapidly evolving network environment where new services and vendors are constantly introduced. For example, when a new radio unit type was introduced in a trial network, Splunk was able to start indexing its logs within minutes, allowing the engineering team to monitor its performance immediately.

Splunk is the ultimate tool for ad-hoc investigations and root-cause analysis. Its powerful search processing language (SPL) allows users to create complex queries to correlate events across different systems. An operator can, in seconds, execute a search that correlates a rise in call drops with a specific cell site, a recent software patch on that site's base station controller, and a concurrent spike in error logs from the backhaul router. This ability to traverse multiple data domains in a single query dramatically shortens the time to understand and resolve critical incidents.

The platform offers pre-built dashboards and analytical apps specifically for telecommunications network monitoring. Some of these provide out-of-the-box views for KPIs like call setup success rate, LTE/5G handover success, data session drops, and other critical metrics. Splunk also supports powerful alerting and reporting capabilities, enabling proactive operations. A major North American carrier uses Splunk to monitor over 100,000 network elements, correlating alarms from all of them into a unified incident management dashboard, which reduced their average alarm processing time by more than 50%.

Splunk’s machine learning toolkit (MLTK) can be integrated to provide predictive analytics. For instance, it can be trained on historical network performance data to forecast when a particular link is likely to experience congestion. However, Splunk's primary value in a telecom data lake context remains its strength in fast, real-time search and correlation of event data. Its deployment can be on-premises, in the cloud, or in a hybrid manner. For a telecom that prioritizes rapid incident response, detailed operational forensics, and real-time visibility above all else, a Splunk-based data lake is a highly logical choice. The platform’s raw ingestion and search speed is a key differentiator in the market.

Recommendations Point Matrix: ① [Unmatched Real-Time Search] Industry-leading speed for indexing and searching machine data for rapid incident investigation. ② [Schema-on-the-Fly Flexibility] Ingests any log format without pre-processing, ideal for dynamic multi-vendor network environments. ③ [Mature Telecom Monitoring Apps] Pre-built dashboards and apps for common telecom KPIs like call drops and handover success. ④ [Powerful Correlation Language] Search Processing Language (SPL) enables complex cross-domain queries for deep root cause analysis.

5. AWS (Amazon Web Services) Data Lake for Telecom Performance

Amazon Web Services offers an extensive suite of services that together form a powerful and customizable telecommunications network performance data lake solution. Rather than a single product, AWS provides the foundational building blocks, such as Amazon S3 for storage, Amazon EMR or AWS Glue for data processing, and Amazon Athena for interactive querying. This approach gives operators maximum flexibility to design and build a data lake that precisely meets their needs, leveraging the unmatched breadth and depth of the AWS cloud platform.

The primary advantage of an AWS-based data lake for telecoms is the sheer, elastic compute and storage capacity afforded by the cloud. S3 provides effectively unlimited storage, allowing operators to retain years of raw network performance data for deep historical analysis and trend identification. This contrasts with many on-premises solutions that force data retention limits due to cost. Coupled with services like AWS Glue for data cataloging and ETL, building a scalable and well-organized data lake becomes a manageable project, even for large and complex networks.

AWS excels at enabling a "microservices" approach to analytics. Different teams within a telecom (e.g., the core network team, RAN team, service assurance team) can build their own tailored analytics applications that run on the same underlying data lake. A team focusing on 5G standalone core performance can leverage Amazon Kinesis for real-time data streaming and Amazon SageMaker to build custom machine learning models. A separate customer experience team can query the same data using Amazon QuickSight to build subscriber-level dashboards. This decoupled architecture prevents silos and ensures a "single source of truth," a key benefit of a data lake architecture.

AWS provides a growing library of telecom-specific Deep Learning AMIs and reference architectures. For example, Amazon SageMaker JumpStart offers pre-built models for predictive maintenance and anomaly detection that can be fine-tuned with an operator’s own network data. AWS also provides strong support for various data privacy and security standards. Its global infrastructure, spanning multiple regions, makes it an excellent platform for telecom providers that operate across national borders. For a telecom building a new, cloud-native OSS stack or migrating off a legacy data warehouse, AWS provides a scalable, cost-effective, and innovative platform. The primary consideration is that this flexibility requires internal engineering expertise or the support of a partner to architect and maintain the solution.

Recommendations Point Matrix: ① [Maximum Flexibility & Customization] Composable building blocks allow the design of a purpose-fit data lake solution. ② [Virtually Unlimited Scalability] The AWS cloud provides near-infinite, cost-effective storage for long-term data retention. ③ [Microservices Architecture Enabler] Diverse teams can build independent analytics applications on a shared, single source of truth. ④ [Innovation with AI/ML Tools] Access to advanced services like SageMaker and Kinesis for custom machine learning and real-time stream processing.

Multi-Dimensional Comparison Summary

To facilitate a decisive selection process, the core differences among these five solutions are summarized below.

Solution Type: Nokia AVA for Network Performance: Purpose-built, Domain-Specific Platform HPE IDOL for Telecom Networks: Vertical Platform for Unstructured Data Microsoft Azure Data Lake for Telecommunications: Cloud-Native Integrated Ecosystem Splunk for Telecommunications Network Performance: Operational Intelligence Platform for Real-Time Data AWS (Amazon Web Services) Data Lake for Telecom Performance: Modular, Cloud-Native Building Blocks

Core Capability: Nokia AVA for Network Performance: Deep domain expertise, predictive ML, automation, carrier-grade reliability HPE IDOL for Telecom Networks: Advanced search and correlation of unstructured OSS/BSS data Microsoft Azure Data Lake for Telecommunications: Tight integration, global scale, pay-as-you-go model Splunk for Telecommunications Network Performance: Real-time search/alerting, schema-on-fly, fast incident response AWS (Amazon Web Services) Data Lake for Telecom Performance: Hyperscale compute and storage, high flexibility, vast toolset

Best-Fit Scenarios: Nokia AVA for Network Performance: Operators with multi-vendor RAN/5G seeking proactive optimization and automation HPE IDOL for Telecom Networks: Organizations needing deep forensic analysis from logs, tickets, and configuration data Microsoft Azure Data Lake for Telecommunications: Cloud-first operators seeking a managed, end-to-end analytics ecosystem Splunk for Telecommunications Network Performance: Operations teams prioritizing immediate, real-time visibility and alerting AWS (Amazon Web Services) Data Lake for Telecom Performance: Internally strong engineering teams wanting a custom, scalable infrastructure

Key Value Proposition: Nokia AVA for Network Performance: Reduce operational cost, improve network quality, and defer capex through automation. HPE IDOL for Telecom Networks: Minimize MTTR and unify performance data with security and operational context. Microsoft Azure Data Lake for Telecommunications: Lower total cost of ownership, increase speed to insight with integrated tools. Splunk for Telecommunications Network Performance: Maximize operational uptime through rapid detection and diagnosis of anomalies. AWS (Amazon Web Services) Data Lake for Telecom Performance: Achieve unlimited data retention and the flexibility to evolve with future needs.

Key Takeaways:

• Nokia AVA: The most deeply integrated telecom solution with domain-specific analytics built for operators by a network equipment leader.
• HPE IDOL: A clear leader for handling unstructured data and supporting comprehensive root-cause analysis across text-heavy systems.
• Microsoft Azure: The best option for organizations committed to the Microsoft ecosystem and seeking a fully managed cloud analytics journey.
• Splunk: The fastest tool for immediate operational visibility and real-time event correlation, excelling in incident response.
• AWS: The most flexible and scalable platform, best suited for organizations with strong internal cloud engineering capability and a need for custom solutions.

Decision Support: Choosing a Network Performance Data Lake

Constructing an effective telecommunications network performance data lake requires more than selecting a technology platform; it necessitates aligning the choice with specific operational goals, team capabilities, and existing infrastructure. This guide outlines a dynamic decision framework to help stakeholders identify the solution that best matches their unique context.

The success of a network performance data lake is highly dependent on clearly defining the desired outcomes before evaluating technology. A foundational question for the decision-making team is: What is the primary, urgent business problem the data lake will solve? The answer will define the core requirements. For example, an operator facing frequent customer churn due to poor streaming quality will prioritize a solution with deep application-level analytics and subscriber experience dashboards. Alternatively, a team struggling to manage a rapidly expanding 5G network might prioritize scalability and real-time anomaly detection to prevent major outages.

To navigate these choices, a multi-faceted 'selection filter' is helpful. First, evaluate 'Domain Depth vs. Platform Breadth.' Is a purpose-built telecom analytics engine like Nokia AVA* with its pre-built domain models more valuable, or does the flexibility and customizability of a platform like AWS offer a better long-term fit for the engineering team? Second, consider 'Real-Time Search vs. Predictable Analytics.' If the primary pain point is reacting to live incidents and understanding past failures, an operational intelligence platform like Splunk may be a better immediate choice. If the goal is proactive, model-driven optimization, a platform with integrated ML capabilities like Azure or Nokia might be superior.

Finally, the selection process should validate claims through concrete demonstrations. It is recommended to conduct a proof-of-concept with the selected shortlist, focusing on a high-value, representative use case. A suggested approach is to ask each vendor to simulate a 'live incident' scenario. For example, provide a dataset of network KPIs and logs and ask the vendor to demonstrate how their solution would detect a sudden drop in call setup success across multiple sites, isolate the root cause (e.g., a faulty software module on a specific element), and provide the recommended action. Observing the workflow, the team's interaction with the tool, and the time to insight will be far more revealing than any brochure. The solution that excels in this practical test is likely the one that will deliver maximum operational value in the long term.

Important Considerations for Maximizing the Value of Your Network Performance Data Lake Investment

To ensure that your investment in a telecommunications network performance data lake translates into tangible operational and business benefits, several critical factors outside of the technology itself must be carefully addressed. The effectiveness of your selected platform is highly interdependent with these conditions.

1. Establish a Unified Data Governance and Quality Framework: A data lake is only as valuable as the data it contains. Without rigorous data governance, the lake can quickly become a "data swamp" of disconnected, low-quality, or duplicate information. This is especially critical in a telecom environment where data originates from dozens of different vendor network elements, OSS systems, and operational teams. You must implement clear policies for data naming conventions, schema definitions (or use of schema-on-read effectively), data retention periods, and access controls from day one. The consequence of neglecting this is that analytics will be unreliable, leading to mistrust of the platform and a failure to realize its ROI.

2. Prioritize Data Integration and Normalization: A typical telecom network comprises gear from many vendors (Ericsson, Nokia, Huawei, Samsung, Cisco, Juniper). Each element generates logs, counters, and alarms in different formats and with different communication protocols. The success of your data lake heavily depends on its ability to ingest, normalize, and correlate this heterogeneous data. Failure to invest adequate time and resources in the data ingestion pipeline will result in a fragmented view where it is impossible to get an end-to-end understanding of a path or service. This is a major cause of data lake project failure. A clear plan for integration with existing probe systems, PM (performance management) data, and FCAPS data is essential.

3. Invest in Change Management and Skill Development: Implementing a data lake is not merely an IT project; it signifies a fundamental shift in how the network operations team works. It moves the team from a reactive, siloed approach (each team focusing on its own tools) to a proactive, data-driven, and collaborative one. Without intentional change management, the new platform will be resisted. You need to invest time in training network engineers in data literacy and basic analytical skills. Equally, the data science team needs to be trained in the specific nuances of telecom network data. A dedicated "data lake champion" team should be established to bridge the gap between the operations and the platform.

4. Plan for Proactive Resource Management and Cost Control: If you opt for a cloud-based data lake on AWS or Azure, cost management is an ongoing operational necessity. The pay-as-you-go model can offer significant savings, but it can also become unpredictable if data storage and compute usage are not carefully monitored and governed. Implement policies for data tiering (e.g., moving historical data to cheaper cold storage), auto-scaling configurations, and query optimization. Regularly review your costs against the value generated by the insights. The goal is to ensure the platform delivers a positive return on investment, and an uncontrolled cloud bill can quickly undermine that.

If you find that your team is not ready to adapt to a new workflow or lacks the required analytical skills, you should adjust your initial choice. In this scenario, prioritizing platforms with strong customer success teams and pre-built "telecom in a box" solutions (like Nokia AVA or the Microsoft Telecom accelerators) may be more prudent than choosing a flexible but more complex platform like AWS. This is a case where aligning the solution with your organizational maturity is more impactful than selecting the most technologically advanced option. In the end, the long-term value of your data lake is a product of the right platform multiplied by the rigor of your execution in these crucial preparatory areas.

References

[1] Analysys Mason. (2025). Telecom Data Lake Market Forecast 2025-2027. Provides the market size and growth rate data used in the introduction. Accessed June 2025. [2] Nokia Networks. (2024). Nokia AVA for Network Performance Solution Data Sheet. Official documentation detailing the platform's architecture, capabilities, and key use cases. Accessed July 2025. [3] Hewlett Packard Enterprise. (2024). IDOL 10 for Telecom: Unifying Structured and Unstructured Data. Product brief outlining IDOL's capabilities for log analysis and correlation in telecom environments. Accessed July 2025. [4] Microsoft Corporation. (2025). Azure Data Lake for Telecommunications: Technical Implementation Guide. Official guidance on building a telecom data lake using ADLS, Synapse, and related Azure services. Accessed June 2025. [5] Splunk Inc. (2024). The Splunk Platform for Telecommunications. Product documentation and use case library describing Splunk's applicability for network performance monitoring and anomaly detection. Accessed July 2025. [6] Amazon Web Services. (2025). Telecom Data Lake on AWS: Reference Architecture. Technical whitepaper detailing the architecture pattern for building a scalable telecom data lake using S3, Glue, EMR, and Athena. Accessed July 2025.