In the age of artificial intelligence, data is no longer just about numbers and tables.
You may be dealing with a user-uploaded product image, a snippet of real-time voice conversation, a clickstream event log, or even key frames from a video. All of these are “multimodal data” — data in different forms and structures, yet carrying rich semantics.
SeaTunnel, an open-source project from Apache, originally focused only on data synchronization between structured databases. But today, it has been reborn with a leap-forward product upgrade:
Evolving from a traditional ETL tool into a “Unified Multimodal Data Integration Tool” for the AI era
This is not just a slogan; it’s an architectural overhaul, an upgraded plugin ecosystem, and deep adaptation to AI scenarios.
This article will show you how SeaTunnel is moving step by step toward “multimodality,” and how it empowers today’s AI data stack.
Why support multimodal data?
In the past, for data synchronization we only needed to handle the orders table, users table, and sales table.
But now?
- Recommendation systems need to process product images, user reviews, and click behaviors.
- In factory equipment monitoring, you not only collect temperature and voltage, but also video streams and image metadata.
- Financial risk control models must fuse user identity text, log traces, and OCR-extracted contract text…
All of these are multimodal scenarios. Structured, unstructured, streaming, and vectorized data coexist and interweave. The need for a unified tool to integrate all these data forms is becoming increasingly urgent.
SeaTunnel’s new positioning is designed to solve exactly this problem:
Whether you’re an AI engineer, a data developer, or an architect, you need an ingestion tool that can “eat” every data modality.
Where do SeaTunnel’s multimodal capabilities come from?
At its core, SeaTunnel is an “orchestrable heterogeneous data stream processing engine,” architected with three parts:
- Source: data source input (Kafka, MySQL, File, WebSocket…)
- Transform: in-between processing (field mapping, format cleansing, branching…)
- Sink: output targets (ClickHouse, Milvus, Kafka, object storage…)
Let’s break them down one by one.
1) Structured data? That’s SeaTunnel’s longtime forte
From the earliest MySQL to now supporting over 100 data sources, SeaTunnel’s support for structured data is no longer a problem:
- General JDBC support (MySQL / PostgreSQL / Oracle / SQL Server / DB2)
- Batch and incremental synchronization
- Supports primary-key merge, partition extraction, and checkpoint/resume
- Supports lake formats such as Iceberg / Hudi / Delta Lake
If your scenario is still “table-to-table,” SeaTunnel stands shoulder-to-shoulder with any traditional ETL tool.
2) Files + unstructured: ingesting metadata from images/logs/PDFs
SeaTunnel supports parsing the following file types:
- Text files (CSV, JSON, Log, INI)
- Tabular files (Excel, Parquet, ORC)
- Binary files (images, PDF, documents)
Via FileSource + binary mode, you can easily obtain:
- File name, file path, upload time
- File size, modification time, extension (extracted via external processing scripts)
Although these fields may look “unremarkable,” they are precisely the metadata foundation for building systems like image search and log analysis.
SeaTunnel supports structuring this information into SeaTunnelRow via plugins for subsequent use.
3) Real-time streams? SeaTunnel has always been stream-batch integrated
SeaTunnel supports a complete streaming execution architecture:
- Full support for Kafka, Pulsar, RocketMQ, RabbitMQ, and WebSocket
- Uses Hazelcast for state management, supporting exactly-once and checkpoint recovery
- Handling millions of messages per second is no problem
You can simultaneously process clickstreams in Kafka, order tables in MySQL, and product image info in S3, jointly constructing inputs for vector retrieval.
4) Vector data? SeaTunnel already supports it natively!
Since version 2.3, SeaTunnel has added native support for vector databases:
- Milvus Sink (supports writing vector data with specified dimension)
- PGVector Sink (write embedding vectors into PostgreSQL)
- OpenSearch Sink (write vector fields)
Just configure:
sink {
Milvus {
url = "http://127.0.0.1:19530"
token = "username:password"
batch_size = 1000
}
}
No SDK to write, no REST calls; configuration takes effect immediately.
5) Transform: flexibly building field-level semantic processing pipelines
SeaTunnel provides a rich set of Transform plugins to help complete field standardization, content mapping, expression enhancement, and more during structured data transformation.
Currently supported Transform plugins include:
-
FieldMapper Transform: field mapping and renaming -
Filter Transform: conditional filtering (supports SQL expressions) -
Replace Transform: string replacement and cleansing -
Split Transform: split fields by delimiter -
JsonPath Transform: extract fields from nested JSON -
Sql Transform: expression computation capability based on SeaTunnel SQL
With these plugins, users can achieve complex field derivation, data standardization, type conversion, and nested-structure flattening — essential building blocks for the AI semantic substrate.
In future versions, the SeaTunnel community is actively exploring more “programmable Transform” plugin capabilities, such as:
- HTTP transformation that connects to model inference services
- Embedded expression-engine optimizations
- Higher-level Map/Reduce-like streaming transformation semantics
These features will continuously enhance SeaTunnel’s expressiveness in multimodal processing.
Whether it’s field cleansing or feature enhancement, SeaTunnel’s Transform plugins provide a solid backbone for data preprocessing pipelines in the AI era.
Multimodal pipeline example: Image + Text + Behavior stream → Vector database
To build an image-text recommendation system, you only need three pipelines:
Product images (S3) → FileSource → Preprocessing service (CLIP) → MilvusSink
Product descriptions (MySQL) → JDBCSource → Preprocessing service (BERT) → MilvusSink
User behavior stream (Kafka) → KafkaSource → ClickHouseSink
In the end you will obtain:
- An image vector database
- A text vector database
- A real-time behavior log stream
You can then implement downstream:
- Similar image-text recommendation
- User-vector + item-vector recall
- Real-time hot-item identification
All accomplished based on SeaTunnel.
What the community is advancing next: an end-to-end AI data foundation
SeaTunnel currently supports multimodal task configuration in the WhaleStudio visual tool.
Going forward, the community is advancing:
- Multimodal data lineage analysis (source tracing / AI pipeline identification)
- Multimodal data quality checks (field consistency / missingness monitoring)
- Retrieval-augmented task templates integrated with LangChain / RAG
- Bidirectional synchronization between vector databases and large models (vector updates / LLM inference)
The AI data flows you can imagine are being implemented one by one by the SeaTunnel community.
Final words: SeaTunnel — born for structure, evolved for multimodality
SeaTunnel is no longer a traditional ELT tool.
It has transformed into
- A bridge connecting the world of data and the world of semantics
- A low-code, plugin-based, scenario-rich AI data ingestion tool
- A unified engine for the vector era that supports multimodal tasks
Official site: https://seatunnel.apache.org
GitHub: https://github.com/apache/seatunnel
If you’re building an AI multimodal system, consider whether SeaTunnel is the missing piece of your puzzle.