en flag +1 214 306 68 37

Real-Time Big Data Analytics

Architecture, Market, Costs

In big data since 2013, ScienceSoft helps companies across 30+ industries build scalable, high-performing big data solutions that deliver prompt insights and automate business operations with the help of real-time analytics.

Real-Time Big Data Analytics - ScienceSoft
Real-Time Big Data Analytics - ScienceSoft


Alex Bekker
Alex Bekker

Head of Data Analytics Department, ScienceSoft

Marina Chernik

Senior Business Analyst and BI Consultant, ScienceSoft

Real-Time Big Data Analytics: Market Stats and Importance for Business Performance

The global big data and analytics market is expected to reach $924 billion by 2032, growing at a CAGR of 13% during the forecasted period. The major market drivers include the need for real-time customer management insights and the increasing adoption of machine learning and AI. The industries that are expected to gain maximum revenue share from implementing the technology include BFSI, healthcare, automotive, telecoms, media, and retail.

According to the Harvard Business Review survey on enterprise data and analytics strategies, 76% of organizations say that real-time data analytics is important for business performance. 80% of leaders (organizations that have mature data analytics processes) state that the importance of real-time data analytics is increasing. The survey features responses from 336 organizations across multiple industries, including financial services, healthcare, technology, and manufacturing.

High-Level Architecture of a Real-Time Big Data Analytics Solution

Real-time big data analytics is a way to ensure instant responses to high-velocity, multi-source data. Below, ScienceSoft's data engineers outline the description of key architecture blocks and data flows for a real-time big data analytics solution.

High-Level Architecture of a Real-Time Big Data Analytics Solution - ScienceSoft

The data sources for a real-time big data analytics solution may include web and mobile user apps, IoT devices (e.g., sensors, wearables, actuators), and external systems (e.g., stock markets, social media platforms, weather information systems).

In most cases, a solution for real-time big data analytics would feature two layers for real-time (stream) and batch big data processing.

Real-time layer

  • The real-time message ingestion engine receives the latest data and sends it for processing.
  • The stream processing and analytics block ensures low-latency response to events and real-time analytics insights (e.g., personalized cross-selling recommendations, alerts on patients’ abnormal vitals).

Batch layer

  • The raw data storage (a.k.a. data lake) captures data in its initial format (structured, unstructured, or semi-structured).
  • The batch processing block filters, cleans, aggregates, and otherwise prepares data for analytics according to the established schedule (e.g., every 2 hours, every 24 hours, every week).

The analytics data storage (a data warehouse (DWH) or a big data database) stores unified views of the data generated by stream and batch processing blocks in a highly structured format relevant to the chosen data model. The insights are served to BI software and back-office systems. Business users can also perform analysis and exploration of the data in the DWH via ad hoc queries.

The machine learning or artificial intelligence (ML/AI) engine is an optional block that enables advanced real-time analytics (e.g., dynamic pricing in ecommerce, predictive maintenance in manufacturing, fraud detection in finance). The ML training module continuously improves the AI engine's accuracy based on historical data.

The data orchestration and governance system automates data cleansing, transformation, and other recurrent data processing actions. It also ensures quality, security, and regulatory compliance of data throughout its lifecycle.

Head of Data Analytics Department, ScienceSoft

Why pair real-time analytics with historical data views?

The main goal of real-time analytics is to act on new input as soon as it arrives. We aim to process vast volumes of data from multiple sources and send back relevant responses within seconds. But how do you keep your real-time responses relevant for years if the data you receive constantly changes?

Real-time analytics doesn't work alone: historical analytics complements it by providing valuable insights for improving output over time. For instance, if you want to prevent fraudulent financial transactions, real-time analytics helps detect and stop fraud as it happens, while historical data helps AI models learn and recognize fraud patterns better over time. That’s why effective big data architectures incorporate both real-time and historical data processing to ensure high analytics accuracy even as the data landscape evolves and unseen scenarios emerge.

Techs & Tools to Build a Real-Time Big Data Analytics Solution

Our Customers' Success Stories Driven by Real-Time Big Data Analytics

ScienceSoft's Expertise to Drive Your Big Data Analytics Initiative

Fueling Financial Growth: The Game-Changing Benefit of Real-Time Big Data Analytics

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65M additional net income.

Richard Joyce, Senior Analyst at Forrester.

Pricing Information

The cost of implementing a real-time big data analytics solution may vary from $200,000 to $1,000,000+, depending on the solution's complexity. Use our online calculator to get a ballpark estimate for your case. It's free and non-binding.

Get a ballpark cost estimate for your real-time big data analytics solution.

Get a quote

Drive Business Success with Real-Time Big Data Analytics

Implementing big data solutions since 2013, ScienceSoft is ready to support your technology initiative. With an in-house project management office, we successfully deliver big data solutions of any complexity and can drive your project to its goals regardless of time and budget constraints.