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Use Case
Real-Time Analytics
The Real-Time OLAP database
enables fast ingestion, subsecond queries, and high concurrency on large-scale data.
Modern Real-Time Analytics Applications
Most applications in the future will be data-driven, with real-time analytics being the key. The shift is from batch data to streaming data,
from internal-facing analytics to external-facing analytics, and from supporting human decisions to enabling AI-driven automation.
Traditional data warehouses process historical data in batches. Real-time data warehouses continuously ingest and process data as it arrives, enabling faster decision-making with up-to-date insights.
Real-Time BI & Dashboard
User Behavior Analytics
Gaming Analytics
With the growing popularity of cloud computing and SaaS software, embedding analytics into applications has become crucial. This is also referred to as customer-facing or user-facing analytics.
Order Analytics
Advertising Analytics
Inventory Analytics
As AI technologies, especially AI Agents, become more prevalent, an increasing number of analytical decisions will be automatically made by AI programs. This shift will enhance efficiency and accuracy in decision-making processes.
Fraud Detection
Ad Serving
Personalized Recommendation
Why Choose VeloDB
minimum data latency
Streaming ingestion from Database CDC and Kafka
Row-level updates with strict primary key consistency
Data Warehousing and BI
Atomic writes and MVCC-based reads
Lightweight schema evolution
average query latency
MPP distributed architecture
Vectorized execution engine
Complex join queries with a CBO optimizer
Runtime Filtering
maximum query concurrency
Data pruning based on partitioning and bucketing
Various data indexes
Pre-aggregated table
Materialized views
In October 2024, Doris achieved top 3 in ClickBench. In TPC-H, Doris is 3-8x faster than Greenplum in JOIN queries. In TPC-DS, Doris Lakehouse Analytics outperformed Trino by 3x.
Modern Real-Time Analytics Architecture with VeloDB
Supports direct data writing via StreamLoad, continuous data pulling from Kafka-like systems via RoutineLoad, and real-time ETL via Flink, Fivetran, and Airbyte.
VeloDB is compatible with MySQL protocol and can be connected via JDBC and ODBC in the MySQL ecosystem, making it extremely easy to use.
A more simplified data pipeline makes real-time analytics more efficient, robust, and easier to deploy, maintain, and integrate into applications.
Guides, reference manuals, and deep dive - all the technical documentation about real-time analytics.
Learn about Apache Doris & VeloDB’s capabilities for real-time analytics from webinar.
Join real-time analytics dedicated group on Slack and special category on Forum to ask question and get support.
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