Community

Blog Events Webinars Tutorials Forum
  1. Blog
  2. Events
  3. Webinars
  4. Tutorials
  5. Forum
Create Account
×
Community Blog Beyond Silos: How Unified Multimodal Analytics Is Redefining Data Infrastructure for the AI Era

Beyond Silos: How Unified Multimodal Analytics Is Redefining Data Infrastructure for the AI Era

Hologres 4.0 introduces HSAP 2.0—a unified multimodal analytics platform that consolidates OLAP, vector search, full-text retrieval, and AI processing into a single engine.

From Fragmented to Unified: Multimodal Analytics in the AI Era

img

As AI advances, enterprises are moving beyond traditional structured data analysis. Industries now rely on multimodal data—combining text, images, logs, sensor signals, and more—to power intelligent decision-making across use cases like:

  • E-commerce: "Search by image" or "text-to-image" product recommendations
  • Autonomous Driving: Correlating vehicle telemetry (e.g., speed, battery temp) with trajectory photos and signal logs
  • Gaming: Live content generation, anti-cheat behavior detection, personalized push notifications
  • Finance & Education: Contract compliance checks, investment advice via document understanding, "search-by-image" for math problems

img

Take autonomous driving as an example: vehicle signals are stored as wide tables containing structured data (VIN, firmware version), semi-structured data (CAN bus messages in JSON), and unstructured data (trajectory images). Business applications require point lookups by VIN, OLAP aggregations, full-text search on logs, vector similarity on images, and even hybrid queries combining all modalities.

Yet today's architectures force developers to stitch together multiple specialized engines—leading to complexity, inconsistency, and high costs.

Pain Points of Traditional Multimodal Architectures

img

Most current systems follow a "layered data + multi-engine" model:

  • OLAP: ClickHouse, Doris
  • Full-text search: Elasticsearch, Solr
  • Vector search: Milvus, FAISS
  • Point lookup (KV): Redis, HBase
  • Time-series: InfluxDB, TSDB
  • Wide tables: HBase

While each engine excels in its niche, this approach suffers from four critical flaws:

  1. Low development efficiency: Multiple data pipelines, disconnected metadata, and redundant synchronization jobs lead to longer delivery cycles and higher error rates.
  2. High storage and compute costs: Data duplication across systems results in redundant storage and inefficient resource utilization.
  3. Operational complexity: Managing disparate systems complicates troubleshooting and undermines SLA reliability.
  4. Data inconsistency: Varying write latencies cause discrepancies—such as records appearing in search but missing from analytics—compromising data trustworthiness.

Worse, cross-modal queries—like "find vehicles with battery >40°C AND images containing crosswalks"—require manual result stitching in application code, resulting in slow, brittle logic.

Hologres: One Engine to Replace Them All

img

Hologres 4.0 introduces Hybrid Search/Analytics Processing (HSAP)2.0 —a unified architecture that consolidates OLAP, point lookup, full-text search, vector search, time-series, and wide-table workloads into a single engine. Dubbed the "hexagonal warrior" of analytics, it delivers:

  • Simplified architecture: Manage one system, not many
  • Zero data redundancy: Store once, query everywhere—ensuring strong consistency
  • Higher processing efficiency: Near-real-time incremental pipelines via Dynamic Table
  • Unified SQL interface: Express any query—from simple lookups to hybrid multimodal searches—in standard SQL
  • Serverless elasticity: Scale compute per query, pay only for what you use

From Hybrid Serving and Analytics (HSAP 1.0) to Hybrid Search and Analytics (HSAP 2.0)

Since its launch in 2020, Hologres has been engineered from the ground up for high-performance analytics—and has consistently evolved in step with the shifting demands of modern data workloads. Its architectural journey mirrors the broader industry transition from siloed analytics to unified, AI-ready data infrastructure:

Hologres 1.0 (HSAP 1.0) introduced the concept of “unified analytics and serving processing,” seamlessly integrating OLAP and key-value point lookup in a single engine. This breakthrough eliminated the traditional divide between data warehouses and real-time serving systems. The architecture was recognized with a peer-reviewed publication at VLDB 2020.

img

Hologres 2.0 addressed cost and stability challenges by enhancing resource isolation, elastic scaling, and support for compute-group-based deployments. It also introduced native columnar storage for JSONB, significantly accelerating the processing of semi-structured data.

Hologres 3.0 embraced the lakehouse paradigm, enabling real-time interoperability with open data lake formats—including MaxCompute, Apache Paimon, and Apache Iceberg. With Dynamic Table, it delivered incremental computation directly on lake data, effectively replacing complex Lambda architectures with a simpler, more efficient model.

Hologres 4.0 (HSAP 2.0) marks a strategic leap into the AI era. Now reimagined as a “unified analytics and search processing” platform, it natively integrates vector search, full-text retrieval, and hybrid querying—all while embedding AI Functions that allow large language models to be invoked directly via SQL. This transforms Hologres into a full-stack engine for AI-native applications.

img

As enterprise demand for multimodal data processing surges—from text and images to telemetry and logs—Hologres is evolving beyond a high-performance structured data engine into the foundational infrastructure for AI-native, multimodal analytics.

Hologres 4.0 Architecture: One Data, One Compute, Multi-Modal Analysis

img

Hologres 4.0 is built around the vision of an “all-in-one multimodal analytics and search platform,” delivering a truly unified experience: one copy of data, one compute, and seamless multimodal analysis—all orchestrated through a single SQL statement that spans data ingestion, AI-powered transformation, and cross-modal querying.

Storage Layer
Hologres natively supports three types of data sources:

  • Internal tables: Efficient columnar storage and indexing for structured and semi-structured data, including vectors, text, and JSON.
  • Data lake integration: Direct, seamless access to open lake formats such as MaxCompute, Paimon, and Iceberg, enabling true lakehouse interoperability.
  • Unstructured data: Via Object Table, files in OSS (e.g., images, PDFs, PPTs, videos) are mapped into queryable table structures without requiring physical data movement.

Processing Layer
Powered by Dynamic Table, Hologres enables near-real-time incremental computation. Users simply declare desired data freshness (e.g., “1-minute latency”), and the system automatically triggers incremental updates based on upstream changes—supporting diverse patterns like lake-to-warehouse, warehouse-to-warehouse, or lake-to-lake—while significantly reducing resource overhead.

AI Capability Layer
Hologres embeds a rich set of AI Functions, leveraging Alibaba Cloud's shared GPU pool and large language models like Qwen. These functions can be invoked directly in SQL for tasks such as:

  • Content generation and translation (ai_gen, ai_translate)
  • Text understanding (ai_classify, ai_analyze_sentiment)
  • Embedding and chunking (ai_embed, ai_chunk)
  • Data masking for privacy (ai_mask)

Analytics Layer
A unified SQL interface supports five core query paradigms:

  • Point lookup: Millisecond responses on primary or non-primary keys
  • OLAP analytics: Complex aggregations, joins, and window functions
  • Full-text search: High-performance BM25-based retrieval powered by the Tantivy engine
  • Vector search: High-recall approximate nearest neighbor (ANN) search
  • Hybrid search: Combined filtering across scalar, vector, and full-text dimensions.

Building an All-in-One Multimodal Analytics Platform for the AI Era

Hologres 4.0 integrates OLAP analysis, point query services, full-text search, vector search, time-series processing, and KV wide tables into a single platform. However, as AI evolves rapidly, an all-in-one multimodal analytics platform requires advanced enterprise-grade capabilities to continuously enhance data processing and analysis efficiency. Hologres 4.0 introduces three key capabilities:

Object Table – Unstructured Data Access

Directly access unstructured files (e.g., images, PDFs) in OSS via table-like interfaces, with automatic synchronization of file metadata. Users can query and process data without migrating it into the warehouse.

Dynamic Table – Automated Incremental Processing

The system automatically detects data changes (additions, updates, deletions) in the data lake and triggers AI Functions for real-time processing.

AI Functions - Bring LLM to SQL

Hologres 4.0 integrates a comprehensive suite of built-in AI Functions spanning content generation, text analysis, vectorization, and data security, enabling direct SQL-native invocation—such as ai_embed(file) for converting images/text into vectors and ai_gen('Describe the image', file) for generating image-text summaries—without requiring UDFs or external service maintenance, while leveraging Alibaba Cloud's GPU resource pool for out-of-the-box large model execution with no pre-provisioning needed.

img

All-in-One Multimodal Data Analytics

Hologres provides unified, high-performance multimodal analytics. The entire pipeline is declaratively defined with just a few SQL lines, significantly lowering development barriers and operational costs.

Conclusion: A New Paradigm for All-in-One Multimodal Analytics Platform for the AI Era

Hologres 4.0 is more than a version upgrade—it's a fundamental reimagining of data analytics in the AI age. By unifying scalar, text, and vector data under one engine, integrating large models via SQL, and delivering serverless elasticity, it eliminates the fragmentation that has long plagued data architectures.

The future of analytics isn't a patchwork of tools—it's a unified, intelligent, and efficient platform. With Hologres 4.0, Alibaba Cloud empowers enterprises to build truly AI-native data systems, accelerating the journey toward intelligent, data-driven innovation.

Discover how Hologres 4.0 unifies analytics, search, and AI to power next-generation data applications—without the complexity of fragmented architectures.

👉 Learn more about Hologres

Big Data dynamic table Hologres LakeHouse Vector Search Hybrid Search Multimodal Analytics AI-Native Infrastructure Unified Data Platform HSAP 2.0 AI Functions
0 0 0
Share on

Read previous post:

Milvus Launches on Alibaba Cloud International: Empowering Global Businesses to Accelerate Vector Search

Alibaba Cloud Big Data and AI

3 posts | 0 followers

You may also like

Comments

Alibaba Cloud Big Data and AI

3 posts | 0 followers

Related Products

More Posts by Alibaba Cloud Big Data and AI

See All