Real-Time Data Warehouse
Architecture, Use Cases, and Key Techs
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.
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
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).
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.
ScienceSoft's teams typically rely on the following techs and tools for RTDW implementation projects:
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.
The choice of the optimal tech components for an RTDW depends on your business’ unique operations, the scope of data sources you rely on, and the requirements for analytics complexity. But regardless of what tools you end up using, you will not be able to build a sustainable data warehouse by simply connecting ready-made components. The more advanced your analytics processes are, especially if AI/ML is involved, the more custom coding is needed to ensure that the solution will deliver the expected results and stay reliable in the long run. And even when all the tech components are in place, smooth integration between multiple enterprise and third-party systems is another factor that may require large volumes of custom code.
Stream Processing for Real-Time Alerts in a Pet Tracking App
A GPS tracking app that analyzes the data from pet wearables to provide real-time information on a pet’s location and send immediate push notifications in case of critical events (e.g., an animal leaving the preset safe geographical zone).
- Stream processing of 30,000 events per second from 1 million devices.
- An easily scalable solution able to accommodate any increase in user count or data volume.
Real-Time Reporting for a Custom Fleet Management System
Multiple GPS trackers, accelerometers, temperature and humidity sensors are used to gather vehicle data (e.g., events, geolocation, speed, mileage). The solution performs stream analytics and generates real-time reports. Based on these reports, the fleet management team can take immediate corrective or preventive measures.
- Real-time data analytics from thousands of devices.
- A cost-effective and scalable solution.
Business Intelligence Solution for a Producer of Phytotherapy Products
An Azure-based DWH aggregates data from the integrated corporate sources (ERP, CRM, SCM, manufacturing execution system, accounting software, etc.) and makes it immediately available in Power BI for BA experts to get real-time insights on customer behavior and business processes.
- Improved planning and faster, more informed business decision-making.
- Optimized internal processes and operations.
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.
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*
80% of firms
reported revenue increases after deploying real-time analytics systems and processes
98% of firms
saw a rise in positive customer feedback after real-time analytics implementation
62% of firms
found that access to real-time data made their processes more efficient
$321B of total cost savings
achieved across six industries surveyed, thanks to real-time data
* According to KX & CEBR, 2022
May Your RTDW Solution Bring You Record-Breaking Benefits
And if you need expert help along the way, ScienceSoft will be proud to become a part of your success story.