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Stream Data Analytics

Architecture Types, Toolset, and Results

In data analytics since 1989, ScienceSoft helps companies across 30+ industries build scalable, low-latency stream analytics solutions to enable business agility in decision-making and risk mitigation.

Stream Data Analytics - ScienceSoft
Stream Data Analytics - ScienceSoft


Alex Bekker
Alex Bekker

Head of Data Analytics Department, ScienceSoft

Marina Chernik

Business Analyst and BI Consultant, ScienceSoft

80% of Companies Report Revenue Increase Thanks to Stream Data Analytics

According to the 2022 KX & CEBR survey, 80% of participants achieved a significant revenue increase after implementing stream data processing and analytics. The survey features feedback from 1,200 companies in the manufacturing, automotive, BFSI, and telecommunications domains and spans the US, UK, Germany, Singapore, and Australia. The revenue drivers include timely detection of operational and financial anomalies, significant improvement of business processes, and reduction in non-people operational costs.

At the same time, almost 600 executives and technical specialists surveyed for The State of the Data Race Report by DataStax agree that real-time data processing and analytics have a transformative impact on business. The key metrics positively affected by the technology include revenue growth, customer satisfaction, and market share.

Types of Stream Analytics Architectures

Head of Data Analytics

Stream analytics is used for real-time processing of continuously generated data. Lambda and Kappa architecture designs are optimal for building scalable, fault-tolerant streaming systems. The choice between the two depends on your analytics purposes and use cases, including the approach to combining real-time streaming analytics insights with a historical data context.

Lambda architecture

Lambda Architecture - ScienceSoft

The Lambda architecture features dedicated layers for stream and batch processing that are built with different tech stacks and function independently. The stream processing layer analyzes data as it arrives and is responsible for real-time output (e.g., abnormal heart rate or blood pressure alert during remote patient monitoring). The batch processing layer analyzes data according to the defined schedule (e.g., every 15 minutes, every hour, every 12 hours) and enables historical data analytics (e.g., patterns in heart rate fluctuations, what-if models for trading risk assessment). On top of the two layers, there is a serving layer (a NoSQL database or a distributed database) that combines real-time and batch data views to enable real-time BI insights and self-service data exploration.

Best for: businesses that need to combine real-time insights and analytics-based actions with in-depth historical data analytics.

Lambda pros

  • High fault tolerance: even if there is data loss at the stream processing layer, the batch layer still holds all the historical data. In addition to this, each layer has its own redundant layer for even more reliability.
  • Possibility of in-depth data exploration in search of patterns and tendencies.
  • Can enable efficient training of machine learning models based on vast historical data sets.

Lambda cons

  • May require extra efforts to avoid data discrepancies caused by differences in the processing time of the two layers.
  • Comparatively more difficult and costly to develop due to the tech stack diversity.
  • More challenging to test and maintain.

Kappa architecture

Kappa Architecture - ScienceSoft

In Kappa architecture, real-time and batch analytics are enabled by the stream layer. Both processes rely on the same technologies. The serving layer gets a unified view of analytics results from real-time and batch pipelines.

Best for: systems that must provide low-latency analytical output and feature historical analytics capabilities as a complementary component (e.g., financial fraud detection systems, online gaming platforms).

Kappa pros

  • Potentially cheaper to implement due to a single tech stack.
  • Cheaper to test and maintain.
  • Higher flexibility in scaling and expanding with new functionality.

Kappa cons

  • Lower fault tolerance in comparison to Lambda due to only one processing layer.
  • Limited capabilities for historical data analytics, including ML model training.

Tech and Tools to Build a Real-Time Data Processing Solution

 With expertise in multiple techs like Hadoop, Kafka, Spark, NiFi, Cassandra, Mongo DB, Azure Cosmos DB, Azure Synapse Analytics, Amazon Redshift, Amazon DynamoDB, Google CloudDatastore, and more, ScienceSoft chooses a unique set of tools and services to ensure the optimal cost-to-performance ratio of a stream analytics solution in each particular case.

See How ScienceSoft Implemented Stream Analytics for Our Customers

ScienceSoft’s Competencies and Experience

Consider ScienceSoft to Support Your Stream Analytics Initiative

Consulting on stream analytics

Our team can deliver a feasibility study with cost and ROI estimates. We will design analytics features and the architecture to support them. Our specialists will advise you on the best-fitting techs to ensure high system performance and cost-optimized resource consumption.

If you are dissatisfied with your existing stream analytics solution or want to add real-time capabilities to your analytics software, we will audit your system and provide you with a detailed improvement roadmap.

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Development of a stream analytics solution

We will build a secure, fault-tolerant stream analytics solution with a custom analytics logic and processes, including those powered by AI/ML and techs for big data streaming. We will ensure data management in accordance with the regulations required in your industry.

Our goal is to build a system that easily fits your infrastructure and generates accurate, timely insights, recommendations, and action triggers.

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Get a ballpark cost estimate for your stream analytics solution

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