The integration of real-time streaming data into business intelligence (BI) systems (i.e. analytics on top of real time data) is transforming how organisations make decisions, interact with customers, and approach market challenges. This shift toward a more dynamic and responsive data environment is crucial in today's rapidly changing business landscape, where the ability to react instantly to emerging trends and insights can provide a significant competitive advantage.
Define Real Time
Let’s define “Real Time”, Real Time has different perspective based on use case. In case of banking transaction/fraud detection its less than few seconds, IoT sensor data for transportation or other could be less than a minute, clickstream or log collection few minutes or feed from stock exchange every 30 min etc. Quantitative-ness of real-time in creates huge difference in terms of cost, performance and user experience.
Understanding Real-time Data Streaming & Processing
Real-time data streaming involves the continuous ingestion and processing of data as it is generated, allowing businesses to analyse and act upon information immediately. This contrasts with traditional data processing, where data is collected, stored, and analysed in batches at regular intervals.
Technological Choice for Streaming
a) Traditional method e.g. IBM MQ, Rabbit MQ, Pub-Sub etc
b) Kafka, fairly simple and can support variey of use case
c) Flink, High performant, low latency but expensive to setup
Architecture Pattern
a) Lambda Architecture: Lambda architecture process BATCH and Real Time data in a more combination approach to bring freshness with cost perspective
b) Kappa Architecture: In kappa approach there is nothing as such called BATCH, everything is streaming and quite complex to handle. More preferred option but debatable
Processing Style: Based on velocity and volume of data, spark streaming mostly does the job with back pressure setting in either ETL or ELT fashion where data move across medallion pathway of Raw -> Processed form. Once data is curated, it is further analytically optimized for consumption using DWH solution (definitely with Dimension table complement).
Workload Management: Once data is processed, it need to be served and compute layer need to balanced across user type requiring different power
o BI User
o Stakeholders (PM)
o Batch Jobs
o Maintenance
Serving Data with Analytical store & BI Tools
● Loading to Analytical DB: Load to any analytical optimized data warehouse which could be
●Trinio
● Snowflake
● Redshift
● Google BigQuery
● Other’s
● Serve via Query or Dashboard: Serve or Surface insight using traditional tooling be it a) Tableau b) PowerBI etc
Real-time data streaming offers several benefits to businesses, including:
● Immediate Insight and Action: Real-time data processing enables businesses to respond to changing market conditions, customer behaviors, and operational challenges as they occur.
● Enhanced Customer Experiences: By analyzing customer data in real time, businesses can offer personalized experiences, respond to customer needs promptly, and improve overall satisfaction.
● Predictive Analytics and Decision Making: Streaming data can be used to fuel predictive models, providing forecasts and insights that guide future strategies and decisions.
● Operational Efficiency: Real-time data allows for the monitoring and optimization of operations, identifying inefficiencies and opportunities for improvement as they arise.
Key Applications and Case Studies
● E-commerce Personalization: Alibaba, the world's largest e-commerce retailer, uses Apache Flink for real-time personalization on its platform. By analyzing customer interactions and product data in real time, Alibaba optimizes search relevance and product recommendations, significantly impacting sales and customer experience.
● Anomaly and Fraud Detection: In the financial sector, real-time data streaming is vital for detecting and preventing fraud. Microsoft's anomaly detection engine identifies malicious activities in real time, such as compromised accounts or ransomware attacks.
● Healthcare Monitoring: Wearable health devices utilize real-time data to provide critical health alerts. These devices can analyze data like electrocardiogram tests and alert users or caregivers to potential medical emergencies.
● Industrial IoT and Manufacturing: Real-time sensor data in manufacturing processes can predict equipment failures, reducing downtime and maintenance costs. This application is crucial for operational efficiency and cost savings.
● Dynamic Marketing Strategies: Real-time data streaming enables businesses to optimize their advertising campaigns by analyzing web traffic, click logs, and customer behavior as it happens. This approach improves targeting, conversion rates, and campaign ROI.
Conclusion
The role of real-time data streaming in enhancing business intelligence is increasingly significant in today's data-driven world. As technology continues to evolve, the ability to process and analyze data in real time is becoming a key differentiator for businesses across various industries. From e-commerce and finance to healthcare and manufacturing, the application of real-time data streaming is enabling more responsive, efficient, and intelligent business operations. As this trend continues, we can expect to see more innovative uses of real-time data, further transforming how businesses understand and interact with their customers, markets, and internal processes.