Zhou Kang, head of Alibaba Cloud’s open-source OLAP engine team, explained that EMR Serverless StarRocks and DLF Paimon have together formed a multimodal data-lake retrieval solution. What deserves attention is that the engineering bottleneck in AI projects is shifting from model capability to whether enterprises can use their existing data efficiently.
This solution addresses a specific problem: after video, images, text, and vector data are stored on enterprise platforms, how can business teams find clips such as “traffic jams on rainy nights” and then connect them to vehicle models, model versions, and evaluation records? Alibaba Cloud’s approach is to connect content parsing, tagging and vector generation, storage, retrieval, and downstream analysis into a single pipeline.
What this is
Multimodal data-lake retrieval places data in different forms within a unified storage and analytics system that can be queried consistently. Here, “multimodal” means more than supporting multiple file formats: the system simultaneously processes structured fields, keywords, and semantic vectors. It first generates scene descriptions, tags, and vector features for raw video, then uses a single SQL query to combine filters for relevance.
EMR Serverless StarRocks handles query execution, while DLF Paimon manages lake tables, snapshots, and continuously changing data. The design focuses on two principles: filter early by narrowing the scope with conditions such as time and vehicle model; and read on demand by first returning candidate identifiers and scores before loading larger fields such as video. The core goal is not merely to “find a file,” but to make retrieval results directly usable for statistical and correlation analysis.
Industry view
We believe this reflects a change in the priorities of enterprise AI engineering. Model performance is important, but when internal data is scattered across object storage, databases, and business systems, retrieval latency, permission governance, result traceability, and data migration costs often sit closer to production reality than model demos do. A unified query entry point can also reduce the burden of moving data among multiple systems.
This approach, however, is not without risks. First, the original material comes primarily from Alibaba Cloud’s technical team. Although the solution has been deployed within Alibaba Group and Alibaba Cloud, no public performance data from external customers has been provided. Second, multimodal retrieval depends on accurate content parsing, tag generation, and vector quality. If incorrect descriptions are written into the index, downstream analysis may be built on results that appear precise but are actually distorted. Cost is another practical constraint: video parsing and vector generation occur at ingestion time, so the larger the data volume, the greater the investment in preprocessing, storage, and index rebuilding. Enterprises also need to validate permission isolation, data compliance, and long-term compatibility across different storage systems.
Impact on regular people
For enterprise IT: Internal videos, customer-service records, and images will become easier to search and analyze, allowing data platforms to evolve from repositories into infrastructure for AI applications. However, IT teams must also evaluate processing costs, permissions, and index-update mechanisms; they cannot compare query speed alone.
For individual professionals: In knowledge-intensive roles, finding cases, checking historical records, and performing cross-system analysis will gradually shift from manual searches to natural-language retrieval. In the short term, the more likely impact is on information-organization workflows rather than direct job replacement; judging whether retrieval results are reliable will remain an important skill.
For the consumer market: As enterprises gain easier access to their own content, use cases such as insurance claims assessment, e-commerce after-sales service, store inspections, and intelligent customer service may become more closely tied to individual experiences. At the same time, the deeper data is used for analysis, the more consumers should pay attention to authorization scopes, retention periods, and deletion mechanisms.