Real-Time Data Warehouse

80% of Companies Witness Revenue Increase with Real-Time 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.

Popular RTDW use cases

• Real-time asset monitoring and optimization (e.g., for inventory, supply chain, fleet management).
• Predictive maintenance (e.g., industrial IoT).
• Spotting the emerging trends and patterns in real-time events and suggesting optimal actions (e.g., for stock market analytics, weather forecasting, dynamic price optimization).
• Security analytics (e.g., real-time fraud detection, SIEM, surveillance systems).
• Real-time personalized suggestions and customer behavior analytics (e.g., for ecommerce).
• Medical IoT.
• Smart city management.

Real-Time Data Warehouse: The Essence

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.

Sample Architecture of a Real-Time Data Warehouse

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.

Key processes that happen in an RTDW

Data ingestion

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:

• Direct connections: for IoT systems.
• APIs: for third-party data sources (e.g., payment gateways, messaging services, authentication services).
• A message bus: for corporate systems (ERP, CRM, accounting software, etc.) and third-party services (e.g., customer data from an ecommerce platform, telematics data from a third-party device provider).

Real-time storage

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).

Real-time processing and analytics

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.

Data access and reporting

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.

Security mechanisms we use

• Data protection: DLP (data leak protection), data discovery and classification, data backup and recovery, data encryption.
• Endpoint protection: antivirus/antimalware, EDR (endpoint detection and response), EPP (an endpoint protection platform).
• Access control: IAM (identity and access management), password management, multi-factor authentication.
• Application security: WAF (web application firewall), SAST, DAST, IAST (security testing techniques).
• Network security: DDoS protection, IDS/IPS, SIEM, XDR, SOAR, email filtering, SWG/web filtering, VPN, network vulnerability scanning.

ScienceSoft: We Have the Expertise You’re Looking For

• Since 1989 in data analytics and data science.
• Since 2005 in data warehousing.
• Since 2003 in big data.
• Senior DWH architects, data engineers, DevOps specialists, database administrators, and QA specialists with 7–20 years of experience.

• Expertise in delivering complex and large-scale solutions for 30+ industries, including healthcare, banking, lending, investment, insurance, retail, ecommerce, professional services, manufacturing, transportation and logistics, energy, telecommunications, and more.
• Established project management practices to guarantee the success of your initiative regardless of time and budget constraints.
• ISO 9001-certified quality management system.
• ISO 27001-certified security management system.

Let’s Work Together to Build a Robust RTDW Solution

You know what you want to achieve with your real-time data warehouse, and we know how to make it happen. Each project ScienceSoft takes on comes with its unique challenges, but our experts take pride in being able to deliver tailored, value-focused solutions that never fail to hit the mark.

Drive Real Revenue with Real-Time Data*

* According to KX & CEBR, 2022