Traditional Modern Data Stacks are inherently fragmented, relying on separate silos for streaming, batch processing, lakehouses, and AI systems. This fragmentation forces organizations to maintain multiple copies of data, increases operational complexity, and creates expensive retention overhead in systems like Kafka. Additionally, analytical freshness suffers because data pipelines are often batch-oriented and disconnected from real-time applications.
As real-time AI systems, recommendation engines, fraud detection, and agentic workflows become business-critical, the industry is shifting from batch-first architectures toward streaming-first persistent systems, where streams and tables converge into a unified abstraction.
This new architectural paradigm enables organizations to process, store, query, and serve data continuously without maintaining separate infrastructures for streaming and analytics.
The Core Architecture: How It Works
This unified ecosystem moves away from traditional broker-centric designs and introduces a streaming-native analytical storage model.
1. Ingestion Layer (CDC, IoT, Logs)
Continuous event streams such as:
- Change Data Capture (CDC)
- IoT telemetry
- Application logs
- Clickstream events
are written directly into the storage core, bypassing heavy broker retention dependencies.
This reduces:
- Kafka storage costs
- Operational overhead
- Data duplication across systems
2. Storage Core — Apache Fluss
At the center of the architecture sits Apache Fluss, which acts as a real-time streaming storage engine.
Responsibilities
- Maintains streaming tables
- Stores changelogs
- Preserves low-latency hot data
- Automatically tiers cold data into object storage (S3/OBS)
Key Innovation
Instead of treating streams as temporary transport layers, Fluss treats them as a persistent analytical substrate.
This enables:
- Real-time reads/writes
- Stateful streaming
- Stream-table unification
- Efficient historical access
3. Compute Layer — Apache Flink SQL
Apache Flink SQL performs stateful transformations and real-time analytics.
Major Capability: Union Reads
Flink can simultaneously query:
- Hot data from Fluss
- Historical cold data from the lakehouse
This creates a seamless analytical experience across real-time and historical datasets.
Typical Workloads
- Sessionization
- Fraud detection
- Feature engineering
- Aggregations
- Real-time ETL
4. Persistence Layer — Apache Iceberg
Cold and immutable historical datasets are persisted into Apache Iceberg.
Benefits
- ACID table guarantees
- Schema evolution
- Time travel
- Partition optimization
- Open table format interoperability
Catalogs such as:
- Nessie
- Polaris
manage metadata and versioning for Iceberg tables.
5. Query & OLAP Layer
Specialized analytical engines accelerate different workloads.
Databend
Optimized for:
- High-throughput OLAP
- Warehouse-scale analytical queries
- Concurrent workloads
Dremio
Provides:
- Semantic acceleration
- BI query optimization
- Lakehouse exploration
Trino
Enables:
- Federated SQL querying
- Cross-platform analytics
- Distributed query execution
Together, these engines provide a flexible analytical ecosystem over the unified lakehouse.
6. AI & Vector Layer
Modern AI applications require real-time embeddings and semantic retrieval systems.
Vector Databases
- Qdrant
- Milvus
store embeddings generated from streaming pipelines.
Use Cases
- Recommendation systems
- Semantic search
- Retrieval-Augmented Generation (RAG)
- Real-time personalization
- Agent memory systems
This enables AI systems to continuously consume fresh streaming data.
7. Infrastructure & Operations Layer
The entire ecosystem is deployed using cloud-native infrastructure.
Kubernetes
Provides:
- Container orchestration
- Horizontal scaling
- Self-healing deployments
Terraform
Enables:
- Infrastructure-as-Code (IaC)
- Reproducible environments
- Automated provisioning
Airflow
Handles:
- Workflow orchestration
- Batch coordination
- Dependency management
Implementation & Practical Use Case
Real-Time E-Commerce Platform
Consider a large-scale e-commerce system.
Data Sources
- CDC events from transactional databases
- User clickstreams
- Product interactions
- Payment events
- Inventory updates
Processing Flow
- CDC and clickstream events continuously flow into Apache Fluss.
- Apache Flink computes:
- Live sessions
- Fraud signals
- User activity windows
- Recommendation features
- Hot operational data remains in Fluss for low-latency access.
- Historical data persists into Apache Iceberg.
- Dremio accelerates BI dashboards over the lakehouse.
- Databend powers heavy OLAP analytics workloads.
- Qdrant stores vector embeddings for personalized recommendations.
Strategic Evaluation
Key Advantages
Reduced Costs
- Minimizes Kafka retention overhead
- Reduces unnecessary data duplication
- Uses cheaper object storage for cold data
Unified Processing Logic
Stream-table unification enables Flink to seamlessly access both:
- Real-time streaming data
- Historical lakehouse data
without separate architectures.
AI-Ready Infrastructure
Native support for:
- Vector databases
- Real-time feature pipelines
- Streaming embeddings
- RAG architectures
makes the system ideal for modern AI workloads.
Cloud-Native Scalability
Designed for:
- Kubernetes deployments
- Remote object storage
- Elastic compute scaling
- Multi-cloud infrastructure
Conclusion
The rise of Apache Fluss signals a fundamental architectural shift in modern data engineering.
Streaming is no longer treated as a transient transport mechanism — it is becoming the primary abstraction for data persistence and analytics.
By collapsing the traditional boundaries between ingestion, storage, streaming, analytics, and AI, this architecture provides the low-latency foundation required for:
- Real-time intelligence
- Continuous feature freshness
- AI-native applications
- Agentic systems
- Next-generation recommendation engines
This unified streaming-first ecosystem represents the future of modern data platforms.
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