Data Lake Integration - Telecom Telemetry & Analytics Hub
🎯 Learning Objective: Learn what a telecom Data Lake is, why operators use it, how data moves from Raw to Processed to Curated zones, and how both batch data and streaming telemetry are used in OSS analytics.
Easy Analogy - Warehouse vs Lake
Think of two ways to store goods.
Data Warehouse
Everything must be cleaned and placed in the correct shelf before storage. It is neat and easy to query, but slower and more rigid.
Telecom Example: Traditional reporting database for daily KPI reports.
Data Lake
You first collect a large amount of raw data quickly, then clean and analyze it later when needed. It is flexible and cheaper for very large volumes.
Telecom Example: S3 or Azure Data Lake storing PM files, syslogs, alarms, and streaming telemetry.
Warehouse = clean first, store later. Lake = store first, clean later. Many operators use both together.
What Is a Telecom Data Lake?
A Data Lake is a central place where telecom companies store large amounts of raw network and business data.
- Structured data: PM counters, KPI tables
- Semi-structured data: JSON telemetry, alarm events
- Unstructured data: Syslogs, text logs
Sources
gNBs
Routers
Optical gear
Core network
Ingestion
Kafka
Collectors
File uploads
Storage
S3
ADLS
GCS
MinIO
Usage
Dashboards
Reports
ML models
Analytics
Telecom networks create huge volumes of data. A Data Lake is useful because it stores that data at lower cost and keeps it available for future analysis.
Three Simple Zones
Most telecom Data Lakes use three zones.
Raw Zone
Data is stored as received. Little or no cleaning is done here.
/raw/network/gNB/gNB-MUM-05/2026/05/13/
Processed Zone
Data is cleaned, normalized, and checked for quality.
/processed/canonical/alarms/dt=2026-05-13/
Curated Zone
Data is ready for dashboards, reports, ML, and business use.
/curated/kpi/cell_availability/dt=2026-05-13/
Raw = original. Processed = cleaned. Curated = trusted and ready to use.
Batch Data - Collected Every Few Minutes
Batch telemetry means data is collected at regular intervals like every 5, 15, or 60 minutes.
- Radio: PRB usage, success rates, handover failures
- Transport: Latency, jitter, packet loss
- Core: CPU, memory, session failures
// Raw PM file
<measData>
<measInfo>
<measType>prbUsage</measType>
<measValue>65.2</measValue>
</measInfo>
</measData>
// Cleaned format
{
"cellId": "gNB-MUM-05-1",
"timestamp": "2026-05-13T09:00:00Z",
"prbUtilizationPercent": 65.2
}
Streaming Data - Real-Time Telemetry
Streaming telemetry means devices send data continuously instead of waiting for the OSS to ask for it. gNMI is one protocol used for this style of modern telemetry.
Device
gNB or router sends data
Collector
Receives the stream
Kafka
Buffers and distributes messages
Data Lake
Stores the raw stream
// Example telemetry event
{
"device": "pe-mumbai-01",
"metric": "bgpSessionState",
"value": "ESTABLISHED",
"timestamp": "2026-05-13T09:10:00Z"
}
- Batch: Better for reports and long-term trends
- Streaming: Better for live monitoring and fast alerts (for example, real-time call drop detection)
Airflow - Moving Data Step by Step
Apache Airflow is used to schedule and control data pipeline tasks. It helps make sure jobs run in the right order.
Simple Pipeline
# 1. Wait for file task1 = SensorOperator() # 2. Clean data task2 = SparkOperator() # 3. Check quality task3 = PythonOperator() # 4. Store curated output task4 = S3Operator() task1 >> task2 >> task3 >> task4
Typical Timing
- Continuous: Streaming data enters Raw zone
- Every 15 minutes: Raw to Processed
- Hourly: Processed to Curated
- Daily: Reports and dashboard refresh
Without orchestration, teams would run many jobs manually. Airflow automates this and tracks failures and retries.
Simple Telecom Examples
Example 1: 5G Stadium Congestion
Thousands of users gather in one place. The operator wants to avoid congestion.
{"metric": "prbUtilization", "value": 92, "gnb": "stadium-01"}
{"metric": "connectedUsers", "value": 2847, "gnb": "stadium-01"}
The Data Lake keeps the raw data, analytics detect overload, and operations teams can expand capacity.
Example 2: Undersea Cable Fault Analysis
A cable problem affects multiple services. Engineers need historical data to find the root cause.
/raw/optical/2026-05-13/ ├── optical_power.json ├── router_syslog.log ├── alarm_events.json └── pm_aggregate.csv
Because the Raw zone kept everything, engineers can re-check old events and identify where the issue started.
Example 3: Predictive Maintenance
The operator uses historical data to predict which site may fail soon.
{
"gnb": "gNB-MUM-05",
"failureProbability": 0.87,
"likelyComponent": "Power Amplifier"
}
This helps maintenance teams act before customers notice a service problem.
Example 4: VIP Customer Complaint Analysis
A large enterprise customer reports repeated VPN slowness between Mumbai and Pune.
Inputs checked: - WAN latency data - Interface errors - BGP flaps - Customer trouble tickets - SLA reports
The Data Lake brings network, alarm, and customer data together so the operator can find whether the issue is transport, routing, or a site-specific fault.
Example 5: Daily OSS Executive Dashboard
Management wants one daily dashboard showing the health of the full network.
Daily dashboard shows: - Cell availability - Top 20 congested sites - Packet loss by region - Major alarms by domain - SLA trend
Curated data from the lake is used to build trusted dashboards for leadership and operations teams.
End-to-End Architecture
🌐 Network Sources
gNB • Router • Switch • Optical • Core
📡 Collection
Collectors • SNMP • Syslog • File uploads
⚡ Streaming Bus
Kafka topics (real-time events)
Raw Zone
As received
Processed Zone
Cleaned
Curated Zone
Trusted
⏱️ Orchestration
Apache Airflow schedules pipelines
⚙️ Processing
Spark • Flink • SQL engines
📊 Consumption
Grafana • Tableau • ML Models • BSS • NOC Dashboards
Network Sources → Collection → Raw → Processed → Curated → Orchestration → Dashboards & BSS
Common Challenges
Data Swamp
If data is stored without order or metadata, it becomes hard to use.
Governance
Operators must control access, retention, and compliance.
Latency
Some use cases need answers in seconds, so stream processing is also needed.
Cost
Large-scale storage must be managed carefully with lifecycle rules.
Changing Schemas
Vendor formats can change, so pipelines must adapt safely.
Skills
Teams need both telecom knowledge and data engineering knowledge.
Connection to BSS
Usage Mediation
Trusted usage data can support billing-related processes.
Customer Analytics
Marketing can study usage behavior and design better plans.
SLA Reporting
Enterprise customers can receive service quality reports.
Policy Support
Near-real-time analytics can help policy and charging systems.
Key Terms
Common Questions
Q1. What is a Data Lake in telecom?
It is a central place to store large amounts of raw telecom data such as PM files, alarms, logs, and streaming telemetry.
Q2. What are the three zones?
Raw stores original data, Processed stores cleaned data, and Curated stores trusted data for reports and analytics.
Q3. What is the difference between batch and streaming telemetry?
Batch is collected every few minutes or hours. Streaming is sent continuously in near real time.
Q4. Why is Airflow used?
It runs data pipeline jobs in the correct order and handles scheduling, retries, and monitoring.
Q5. Why is a Data Lake useful for OSS?
It enables long-term storage of raw telemetry for retrospective analysis, ML training, and compliance or audit use cases that are hard or expensive to support on traditional databases.
Q6. What is a Data Swamp?
It is a poorly managed Data Lake where data exists but is difficult to trust or use.
📌 Key Takeaways:
- Data Lake = Large central storage for network and OSS data.
- Three zones help move data from raw to trusted form.
- Batch and streaming are both important in telecom analytics.
- Airflow helps automate data movement and processing.
- Curated data supports dashboards, reports, ML, and BSS use cases.
- Good governance prevents the lake from becoming a data swamp.