In data warehousing services since 2005, ScienceSoft helps companies in 30+ industries build fault-tolerant, scalable real-time DWH solutions that enable advanced stream analytics.
According to the 2022 KX & CEBR report, 80% of the businesses that implemented real-time data analytics have experienced a revenue increase of up to 21%. The study covered over 1,200 companies in six countries (US, UK, France, Germany, Singapore, and Australia) and four key industry sectors (manufacturing, automotive, finance and insurance, and telecoms). The total potential revenue gain in the regions and sectors studied is $2.6 trillion, with future potential for an additional $1.6 trillion.
A real-time data warehouse is a solution that supports processing and analytics of event data immediately or shortly after these events happen. All data processing stages (data ingestion, enrichment, analytics, AI/ML-based analysis) are continuous, run with minimal latency, and enable real-time reporting and ad hoc analytics.
The ‘real-time’ in a real-time data warehouse implies that the analytics is performed within a short time frame (from milliseconds to minutes) after the new data arrives, depending on the specific business needs and solution complexity. Below, ScienceSoft’s data engineers provide an example of a high-level real-time data warehouse architecture.
An RTDW ingests real-time data with high throughput performance. Depending on the data source type and the physical distance between the data source and the analytics software, data can be ingested into the processing block by several means:
The real-time storage acts as a buffer that ensures reliable queuing logic, e.g., record ordering, scaling resources, delivering messages with minimal latency. This location also enables pre-analytics processing (ETL/ELT).
Most RTDW solutions rely on AI to enhance real-time streaming data analysis and provide intelligent insights on events as they happen. The software instantly notifies users about the events that require manual settlement and can automatically trigger immediate actions (e.g., block a credit card in case of fraud detection or stop the machine that reported a critical event). AI-powered predictive analytics enables accurate forecasting of the required metrics, while prescriptive analytics offers intelligent recommendations on the proper actions. If you want to know more about real-time data processing, check out our dedicated guide.
An RTDW makes the processed data immediately available as short-term insights and event-based alerts or automated action triggers. But in addition, such solutions enable comprehensive analytics of the accumulated historical data and ad hoc generation of custom reports.
ScienceSoft's teams typically rely on the following techs and tools for RTDW implementation projects:
