Multimodal data visualization refers to the practice of analyzing and visualizing insights across multiple data types in a single analytical workflow. This often includes structured data such as tables and transactions alongside unstructured or semi structured data such as logs, events, text, or metrics. As organizations move toward real time analytics, the ability to visualize these different modalities together becomes essential for operational dashboards and decision making.
Traditional visualization stacks were designed for static reporting on structured data. Modern applications now require dashboards that reflect live system behavior, user activity, and business performance in near real time. Multimodal data visualization addresses this shift by unifying diverse data sources and making them queryable and visualizable through a single analytical interface.
What Is Multimodal Data Visualization? (Definition + Examples)
Multimodal data visualization is the process of combining, querying, and visualizing multiple data modalities within the same analytical context. Instead of treating transactional data, event streams, and derived metrics as separate silos, multimodal visualization enables teams to explore how these data types interact and change over time.
Multimodal data explained: structured vs unstructured data
Structured data typically includes rows and columns stored in relational tables, such as orders, users, or inventory records. Unstructured and semi structured data includes logs, events, JSON payloads, text, and time series metrics. Multimodal data visualization brings these formats together so analysts can correlate operational events with business outcomes using a unified query and visualization layer.
Why visualization matters for modern analytics (and BI dashboards)
Data visualization has become an essential tool in modern analytics, helping transform raw data into actionable business intelligence. Effective visualization allows stakeholders to quickly grasp complex data relationships, trends, and patterns. With the rise of big data, visualization tools have become more sophisticated, enabling the handling of larger and more complex datasets.
Visualization is more than just creating charts and graphs. It’s about telling a story with data, making it accessible and understandable for a wider audience. This democratizes data insights, empowering non-technical stakeholders to participate in decision-making processes. Leveraging advanced visualization techniques can reveal trends that were previously hidden in plain sight, driving better-informed decisions and fostering innovation.
What makes multimodal visualization hard: data quality, latency, and governance
Despite its advantages, multimodal data visualization comes with its own set of challenges. One of the primary difficulties is the integration of different data types. Structured data from SQL databases needs to be combined with unstructured data from sources like social media or IoT devices. Ensuring data consistency and quality during this integration process can be complex and time-consuming.
Another challenge lies in the technical infrastructure required to process and visualize such diverse datasets. Traditional databases are often not well-suited for handling unstructured data, and systems that can handle both types are usually not optimized for real-time analytics. Additionally, visualization tools must be capable of representing multimodal data in ways that users can easily interpret.
Data governance and compliance add another layer of complexity. Ensuring that all data, regardless of type, adheres to regulatory standards is critical. This involves managing data privacy, security, and ethical considerations, which can be particularly challenging when dealing with vast amounts of unstructured data.
Why Multimodal Visualization Breaks Traditional BI Pipelines
As data becomes more diverse and more real time, traditional BI architectures struggle to keep up. Multimodal visualization often requires combining transactional records, event streams, time series data, and derived metrics. Legacy pipelines built for static reporting were not designed for this level of scale, freshness, or complexity, which leads to slow dashboards and fragmented insights.
The ETL bottleneck: extract, transform, load at scale
Traditional BI relies heavily on extract, transform, load pipelines to move data from operational systems into a separate analytics store. As data volumes grow and update frequencies increase, these pipelines become brittle and expensive to maintain. Long batch windows introduce latency, complex transformations delay availability, and schema changes require frequent rework. For multimodal visualization, this means dashboards are often built on stale or partially synchronized data rather than reflecting what is happening now.
Big data visualization challenges: freshness, joins, and cost
At scale, visualization pipelines face three recurring challenges. Freshness suffers when data must pass through multiple staging layers before it can be queried. Joins across large datasets become slow or require pre-aggregation, limiting analytical flexibility. Costs increase as teams duplicate data across systems and overprovision infrastructure to meet peak demand. Together, these issues make it difficult for traditional BI stacks to deliver timely, accurate visualizations when working with high volume, multimodal data.
How TiDB Powers Real Time Analytics for Multimodal Data (HTAP + SQL)
HTAP explained: OLTP + OLAP for fresh dashboards
TiDB is an open-source, distributed SQL database that natively supports Hybrid Transactional and Analytical Processing (HTAP) workloads. This unique capability allows TiDB to handle both transactional (OLTP) and analytical (OLAP) queries in real-time. The architecture of TiDB separates the data storage and computing, enabling seamless scaling and performance optimization.
With TiDB, businesses can perform real-time analytics on freshly written transactional data without affecting the performance of their primary database. This is a game-changer for organizations that need to make immediate data-driven decisions. Learn more about HTAP databases for real-time analytics.
Model multimodal data in one place with SQL + JSON
One of the standout features of TiDB is its compatibility with the MySQL ecosystem, making it easy to migrate existing applications. TiDB’s data integration tools like TiDB Data Migration (DM) allow for seamless data movement from various sources into TiDB, providing a unified platform for multimodal data.
TiDB’s support for both structured and unstructured data is further enhanced by its integration capabilities. For example, TiDB can natively handle JSON data types, making it ideal for semi-structured data storage and querying. Unstructured data, such as text, can be managed using TiDB’s compatibility with external storage systems or through the integration with systems like Hadoop for distributed data processing.
Here’s a simplified example of integrating JSON data into a TiDB table:
CREATE TABLE customer_reviews (
id INT PRIMARY KEY,
review JSON
);
INSERT INTO customer_reviews (id, review)
VALUES
(1, '{"product": "Laptop", "rating": 5, "comment": "Excellent product!"}'),
(2, '{"product": "Phone", "rating": 4, "comment": "Good value for money"}');
Query structured and semi-structured data together (JSON patterns)
TiDB’s architecture is designed to facilitate the management of structured, semi-structured, and unstructured data. For structured data, TiDB offers strong consistency and horizontal scalability, ensuring high availability and fault tolerance.
For semi-structured data, TiDB provides native support for JSON columns, allowing complex queries to be performed directly on JSON data. This is particularly useful for applications that need to store loosely defined data structures.
Unstructured data integration can be accomplished by interfacing TiDB with external tools like Apache Kafka for real-time data streaming, or Apache Hadoop for batch processing. This flexibility ensures that TiDB can manage diverse data workloads efficiently.
Consider the following SQL query to extract specific details from JSON data:
SELECT
review->>"$.product" AS product_name,
review->>"$.rating" AS product_rating,
review->>"$.comment" AS product_comment
FROM
customer_reviews
WHERE
review->>"$.rating" >= 4;
This query extracts the product_name, product_rating, and product_comment from customer_reviews for products with a rating greater than or equal to 4. TiDB’s ability to handle such queries natively makes it a powerful tool for managing semi-structured data.
From events to insights: CDC + streaming ingestion into TiDB
Multimodal analytics is not limited to static or batch data. Many real time dashboards and operational workflows depend on a continuous stream of events such as user activity, transactions, and system updates. To support these scenarios, TiDB enables change data capture and streaming ingestion so data arrives as it is generated.
TiDB Change Data Capture, or TiCDC, captures row level changes from transactional tables and streams them downstream in near real time. These changes can be published to systems like Apache Kafka, where they become part of an event stream that is easy to process, replay, and integrate with other data sources. This ensures that multimodal datasets remain fresh without relying on periodic batch jobs.
Streaming processors can consume these events, apply transformations or aggregations, and write the results back into TiDB. Because TiDB supports both transactional and analytical queries on the same data, the ingested streams can power live dashboards and operational intelligence alongside existing structured data using standard SQL.
Example: Streaming row changes from TiDB to Kafka with TiCDC
The following example creates a TiCDC changefeed that streams row level changes from TiDB into a Kafka topic for downstream processing.
tiup cdc cli changefeed create \
--pd http://127.0.0.1:2379 \
--sink-uri "kafka://127.0.0.1:9092/tidb_cdc?protocol=canal-json" \
--changefeed-id "tidb_to_kafka"
With this pattern, every insert, update, and delete is captured as an event and made available for real time analytics. The resulting data can be processed and visualized within seconds, enabling TiDB to serve as a unified foundation for multimodal, real time insights.
Build BI Dashboards on TiDB (Grafana, Tableau, Apache Superset)
TiDB is well suited for powering business intelligence dashboards that need both fresh operational data and analytical depth. By serving transactional and analytical workloads (OLTP and OLAP) from the same database, TiDB simplifies BI architectures and enables teams to build BI dashboards and interactive dashboards without maintaining separate systems for reporting and real time analytics.
For instance, in an e-commerce application, transactional data such as purchases and user activity logs can be immediately analyzed to identify trends, optimize inventory, and personalize user experiences. TiDB’s ability to perform complex analytical queries on recent transactional data without any latency is a significant advantage.
Grafana dashboard for operational and real time metrics
Grafana is commonly used for operational dashboards that track system and application metrics in real time. When connected to TiDB, Grafana can query live transactional and event data using SQL, making it well suited for monitoring usage patterns, performance trends, and near real time business signals.
Tableau dashboards for business intelligence reporting
Tableau is widely used for building business intelligence dashboards that support reporting, analysis, and decision making across organizations. TiDB integrates with Tableau through standard MySQL compatible connectors, allowing Tableau to connect directly to TiDB as a MySQL data source without custom drivers, data extracts, or additional data movement.
This direct connection enables Tableau to query live data stored in TiDB tables, making it possible to build BI dashboards and interactive dashboards on up to date information. Analysts can use familiar drag and drop workflows to explore data, apply filters and drilldowns, and perform ad hoc analysis without relying on batch refresh cycles.
By querying TiDB directly, Tableau dashboards can combine historical trends with near real time metrics in a single reporting layer. This makes it easier for teams to maintain a single source of truth for business intelligence while supporting interactive exploration and timely insights.
Apache Superset for self serve BI and exploration
Apache Superset provides an open source option for self serve BI and data exploration. With TiDB as the underlying data store, Superset allows analysts and engineers to create interactive dashboards, run ad hoc queries, and explore multimodal datasets using familiar SQL, without heavy data preparation workflows.
Interactive dashboards best practices: filters, drilldowns, and freshness
Effective interactive and scalable dashboards rely on flexible querying and fresh data. Filters and drilldowns help users explore metrics at different levels of detail, while low latency queries ensure dashboards reflect current system behavior. TiDB supports these patterns by allowing dashboards to query live data directly, reducing reliance on batch refresh cycles and precomputed aggregates.
At the query layer, multimodal visualization in TiDB is enabled by SQL that can seamlessly combine structured and semi structured data in real time. For example, the following query calculates total sales and average product ratings by category by joining relational data with JSON fields.
SELECTcategory,SUM(sales) AS total_sales,AVG(review->>"$.rating") AS average_ratingFROMproductsLEFT JOIN customer_reviewsON products.id = customer_reviews.product_idGROUP BYcategory;
This query combines structured transactional data with semi structured JSON data to produce real time metrics that can be visualized directly in BI dashboards. As a result, teams can build interactive dashboards on TiDB that reflect current business performance without additional transformation layers or duplicated data systems.
Case Studies: Successful Multimodal Data Visualization in the Real World
E-commerce: unify orders + reviews for real time analytics
An online retailer implemented TiDB to manage their sales transactions, customer reviews, and product images. By utilizing TiDB’s HTAP capabilities, they created real-time dashboards that combined structured sales data with unstructured customer feedback and images. Leveraging Grafana, the retailer was able to visualize sales trends, customer sentiment, and product performance in a single dashboard.
IoT: Streaming sensor data into BI dashboards
A manufacturing company used TiDB to integrate sensor data from their IoT devices with their existing transactional data. The sensors generated unstructured time-series data, which was ingested into TiDB using Apache Kafka. TiDB’s real-time analytics enabled the company to monitor equipment health and predict failures. They utilized Apache Superset to create visualizations that provided insights into equipment performance and maintenance schedules.
FinTech: Blend trades + news for big data visualization
A financial services firm migrated to TiDB to handle their trading transactions and market data feeds. By combining trade records (structured data) with market news articles and analyst reports (unstructured data), they were able to develop advanced analytics models. Using Tableau, they created dashboards that provided traders with comprehensive market insights, enabling more informed trading decisions.
Conclusion: Start Building Multimodal Dashboard on TiDB
In summary, multimodal data visualization is a powerful approach to harnessing the full potential of diverse data types. TiDB, with its HTAP capabilities, seamless data integration, and support for structured and unstructured data, provides a robust platform for implementing multimodal data solutions. Through real-time data processing, advanced visualization tools, and successful case studies, TiDB demonstrates its value in delivering comprehensive insights and driving data-driven decision-making. As businesses continue to embrace the complexity and potential of multimodal data, TiDB stands out as a key enabler for innovative and effective data visualization strategies.