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Community Blog Electric Vehicle Data Revolution: How Real-Time Lakehouse Architectures Solve Automotive Big Data Challenges

Electric Vehicle Data Revolution: How Real-Time Lakehouse Architectures Solve Automotive Big Data Challenges

Discover how China's booming EV market leverages Alibaba Cloud's Flink & Apache Paimon for real-time data processing.

Global Electric Vehicle Surge: A Data Tsunami Demands New Solutions

China’s electric vehicle (EV) market is accelerating at a blistering 22.8% CAGR, projected to hit 23 million connected vehicles by 2028, according to IDC's prediction. This explosive growth creates unprecedented data challenges:

  • 400KB+/min generated per vehicle
  • 1,000–3,000 data points per car with <1% actionable value
  • 10,000+ vehicles = petabyte-scale daily data

Globally, EV sales hit 14 million units in 2023 (18% of passenger vehicles), with projections of 35% market share by 2030 (IEA, 2023). Legacy systems struggle to manage this data deluge, underscoring the need for scalable cloud-edge solutions to support autonomous driving, OTA updates, and agile supply chains.

Critical Data Challenges in Automotive Innovation

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  1. High Volume

    • Problem: 10,000 vehicles produce 8.6M GB/day – enough to fill 1.3 million DVDs.
    • Solution: Apache Paimon’s LSM Tree storage on OSS reduces costs by 60% vs. traditional data lakes.

  2. Low-Value Density

    • Problem: 97% of sensor data (e.g., tire rotations) lacks immediate business value.
    • Solution: Flink SQL filters high-priority signals (e.g., battery temp) in <500ms.

  3. Peak Traffic Spikes

    • Problem: Rush-hour data surges 300%, stressing batch systems.
    • Solution: Kubernetes auto-scaling handles 50K events/sec during peaks.

Top Automotive Use Cases Driving Data Demand

In the automotive industry, online data acquisition represents the largest scenario for data processing. We have analyzed various scenarios across the industry, including sales and operations, vehicle connectivity, and autonomous driving. Below, we highlight some typical scenarios; additional vehicle-related applications are not detailed here.

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  1. Sales/Operations: Sales operations are similar to other retail industries,It involves store traffic monitoring, index monitoring, user portrait selection, customer satisfaction evaluation and after-sales maintenance. In addition, there are various data applications including supply chain management.
  2. Internet of Vehicles: Internet of Vehicles mainly uses vehicle sensor data and location information for application. Predictive maintenance, remote diagnostics, location-based applications, vehicle statistics, and OTA online updates,Car networking has a wide range of applications.
  3. Autonomous driving: In addition, there are autonomous driving-related businesses, including assisted driving, high-precision maps, safety warnings and other applications.

Automotive Big Data Architecture: A 4-Layer Framework

Modern vehicle data ecosystems require a structured approach to handle 10,000+ data points per car. Here’s a breakdown of the reference architecture:

Layer 1: Data Collection

  • Sources: Vehicle sensors (70% of data), IoT apps, production systems
  • Tools: OBD-II telemetry, mobile app SDKs, Apache Kafka for streaming

Layer 2: Data Processing

  • Organizes raw data into domains:

    • User Data (preferences, driving patterns)
    • Vehicle Health (battery metrics, engine diagnostics)
    • Supply Chain (component traceability)

Layer 3: Application Layer

  • Cross-platform analytics for:

    • Dealership dashboards (sales/CRM)
    • Factory QC systems (AI defect detection)
    • Driver-facing apps (OTA update status)

Layer 4: Governance & Standards

  • Implements ISO/SAE 21434 for cybersecurity
  • GDPR-compliant data anonymization

Lambda vs. Real-Time Lakehouse: Which Automotive Data Architecture Delivers Better ROI?

Automakers face a critical choice between traditional Lambda and modern Real-Time Lakehouse architectures to manage massive vehicle data.

Lambda Architecture: Tried but Costly

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Structure:

  • Batch Layer: Historical data processing (e.g., MaxCompute)
  • Speed Layer: Real-time analytics (e.g., Flink + Hologres)
  • Serving Layer: Merged outputs

Pros:
✔️ Proven reliability for legacy systems
✔️ Clear separation of batch/stream workloads

Cons:
❌ 2x Engineering Overhead: Dual pipelines require separate maintenance
❌ Data Silos: Batch/real-time results often conflict
❌ High Costs: $4.50/TB blended storage

Auto Industry Use Case:
Daily sales reports – Batch-processed dealer metrics combined with real-time website traffic data.

Real-Time Lakehouse: Unified Future-Proofing

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Components:

  • Engine: Flink (stream-batch processing)
  • Storage: Apache Paimon on OSS ($0.23/TB)
  • Analytics: StarRocks/Hologres (sub-second queries)

Advantages:
✅ 60% Faster Insights: Unified data pipelines
✅ Schema Evolution: Adapts to new vehicle sensors seamlessly
✅ Cost Efficiency: 72% lower storage vs. Lambda

Automotive Applications:

  1. Battery Health Monitoring: Real-time voltage analysis + historical degradation trends
  2. Autonomous Driving: Unified processing of LiDAR streams and HD map updates

The Flink+Paimon Architecture: A Unified Approach

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Unify streaming, batch processing, and analytics at scale with our Flink-powered real-time lakehouse architecture, designed to streamline IoT and enterprise data workflows.

At its core, this solution combines Apache Flink for real-time computation and Apache Paimon for unified storage, creating a seamless flow from data ingestion to actionable insights.

Key Components

  1. Data Ingestion

    • IoT & Database Integration: Capture vehicle telemetry, application databases, and other sources using Flink’s hybrid stream and batch processing.
    • Apache Paimon Storage: Ingest raw data into cost-efficient Paimon tables built on OSS (object storage), eliminating silos between streaming and batch storage.

  2. Unified Compute Layer

    • Flink Processing: Transform and enrich data in real time or batch mode with Flink’s dual-processing engine.
    • Multi-Layer Analytics: Generate downstream datasets for diverse use cases (e.g., reporting, ML training).

  3. Query & Analysis

    • OLAP Engine Flexibility: Use StarRocks, Hologres, or other engines for high-speed queries and interactive analytics.

Core Advantages

✅ Cost Efficiency:

  • Low-cost OSS storage with Paimon tables.
  • Unified architecture reduces operational overhead.

✅ Real-Time Agility:

  • End-to-end latency under seconds for IoT and time-sensitive workflows.
  • Stream/batch compute and storage in one platform.

✅ Enterprise Scalability:

  • Built-in data governance, scheduling, and ad-hoc query tools.
  • Open ecosystem supporting multiple engines (Flink, StarRocks, etc.).

Solving the "Impossible Triangle” of Big Data: Streaming Lakehouse with Flink & Paimon

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Specifically, the combination of Flink and Paimon can create a cost-effective, real-time solution. When selecting a big data architecture, we often encounter the 'impossible triangle,' as illustrated in a past paper by Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google. This triangle represents the trade-off between performance, freshness, and cost. The real-time lakehouse solution seeks to optimize the balance between these three elements.

Key Benefits

✨ Faster Insights: Act on minute-old data with real-time dashboards or alerts.
✨ Cost Control: Eliminate expensive legacy warehouses with OSS-based Paimon tables.
✨ Future-Proof Flexibility: Scale from IoT telemetry to enterprise BI without rearchitecting.

Metric Traditional Warehouse Real-Time Lakehouse
Freshness T+1 Days <1 Minute
Cost/TB $4.50 $0.23
Query Performance 2-8 Hours 200ms

How It Works:

  • Minute-level freshness: OTA updates trigger immediate analytics
  • 60% cost reduction: OSS cold storage tiers for regulatory data
  • Unified pipelines: Eliminate duplicate batch/stream code

Key Features for Automotive Use Cases

A. Simplified Operations

  • CTAS/CDAS commands: Merge 100+ vehicle data tables in 3 clicks
  • Schema Evolution: Add new ADAS sensors without reprocessing

img

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B. Real-Time Data Flow

  1. Ingest: Flink CDC captures CAN bus signals (<500ms latency)
  2. Process: SQL alerts for battery anomalies (e.g., thermal runaway)
  3. Analyze: Live dealer inventory heatmaps via StarRocks

img

C. Cost-Efficient Storage

  • LSM Tree optimization: 40% faster writes than Parquet
  • Columnar compression: 70% smaller footprint for telemetry data

img

Real-Time Lakehouse in Action: Automotive IoT Case Study

See how a leading automotive company leverages Apache Flink, Apache Paimon, and StarRocks to modernize vehicle data pipelines with a cost-effective, real-time lakehouse.

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Architecture Overview

  1. Data Ingestion

    • Flink CDC: Sync vehicle sensor data, telemetry, and application databases to Apache Paimon tables stored on low-cost OSS.
    • Unified Storage: Paimon tables act as a single source of truth for both streaming and batch data.

  2. Compute Layer

    • Real-Time Processing: Use Alibaba Cloud's Realtime Compute for Apache Flink to clean, transform, and enrich data streams.
    • Batch Analytics: Run large-scale historical analysis (e.g., fleet performance trends) with EMR-Spark.

  3. Query & Insights

    • StarRocks OLAP Engine: Enable sub-second queries for dashboards, driver behavior analysis, or predictive maintenance alerts.

Summary

The electric vehicle revolution demands a data architecture that defies traditional trade-offs—where real-time responsiveness, cost efficiency, and scalability coexist. By unifying Apache Flink’s stream-batch processing power with Apache Paimon’s low-cost OSS storage, Alibaba Cloud’s real-time lakehouse architecture shatters the "impossible triangle" of big data. This solution not only slashes storage costs by 72% but also delivers actionable insights in under 200 milliseconds, empowering automakers to turn petabytes of raw telemetry into competitive advantages. As EVs evolve into connected data hubs, those who harness unified analytics will lead the charge in autonomous innovation, predictive maintenance, and hyper-personalized mobility services.

Transform Your Electric Vehicles Data Strategy Today

Don’t let data bottlenecks stall your innovation. Alibaba Cloud’s Realtime Compute for Apache Flink empowers automotive teams to:

  • Process 50,000+ events/sec during traffic spikes
  • Detect battery anomalies in <500ms with Flink SQL
  • Unify LiDAR streams, OTA updates, and supply chain analytics in one platform

Visit Alibaba Cloud's website for more information and to get started with a Free Trial of Flink and Paimon on the Alibaba Cloud today.

bigdata Cloud Computing data architecture Apache flink Realtime Compute for Apache Flink Data Lakehouse Apache Paimon RealTime Data Processing DataAnalytics Predictive Maintenance
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