Why Real‑Time Data Warehouses Are the New Competitive Edge for Enterprises

Enterprises face ever‑more complex and fast‑changing markets, requiring real‑time data insight and rapid response. Building a real‑time infrastructure has become essential, with modern tech stacks shifting toward supporting real‑time processing.

Real‑Time Analysis Three Scenarios

According to Gartner, real‑time data processing needs can be divided into three categories based on event order:

Real‑time stream processing : continuous 24‑hour ingestion and processing of streaming data.

On‑demand real‑time analysis : responding to ad‑hoc user requests by combining real‑time streams with historical data (T+0 to T+X, ranging from seconds to days).

Offline analysis : traditional batch processing of stored data.

Typical use cases include real‑time marketing, per‑minute business analysis, personalized recommendation pages, financial risk control, and production‑line monitoring. The rise of 5G and other technologies will further increase the demand for massive real‑time data processing.

Limitations of Traditional Stream Engines

Pure stream engines such as Flink and Spark Streaming can only handle continuous stream processing and lack on‑demand real‑time capabilities. To achieve on‑demand analysis, a real‑time data warehouse must store the results of these engines and provide high‑performance, on‑demand query capabilities.

Skylab: An Integrated Cloud‑Native Data Platform

OddNumber Technology’s real‑time data warehouse is not a standalone product but part of the Skylab cloud‑native data platform, which consists of four core components:

Cloud‑native database OushuDB Machine‑learning platform LittleBoy Data‑management platform Lava Data‑analysis & application platform Kepler Skylab’s ANCHOR features (All Data Types, Native on Cloud, Consistency, High Concurrency, One Copy of Data, Real‑Time T+0) enable it to handle both streaming and batch workloads.

Omega Architecture: Merging Lambda and Kappa

The Omega architecture combines the strengths of Lambda and Kappa for stream processing, adding real‑time on‑demand and offline‑on‑demand intelligence. It can snapshot mutable data from business applications in real time, allowing the solution to blend on‑demand real‑time computation with on‑demand batch processing.

Principles for Future Data‑Technology Fusion

According to Zhang Liqun, the construction of a data‑technology stack should follow three basic principles:

Flexibility and openness : support multiple compatible technologies and leverage existing IT assets.

Resource efficiency : avoid waste by pooling cloud‑native resources for isolation and dynamic management.

Enhanced user experience : deliver high‑performance, high‑concurrency, low‑latency services that meet diverse user data‑service needs.

As real‑time analysis scenarios proliferate, solutions with real‑time processing capabilities—such as real‑time data warehouses—will see broader adoption.