Data Streaming

44% of IT Leaders Cite up to 5X ROI on Data Streaming Investments

These findings are featured in the 2025 Data Streaming Report by Confluent, which presents feedback from 4,175 IT leaders across 12 countries and major industries, including healthcare, insurance, investment, financial services, manufacturing, retail, telecom, media, logistics, and technology. For 86% of organizations, data streaming is considered a critical or highly important strategic priority, and 64% plan to increase investments in data streaming platforms in 2025. Additionally, 89% of IT leaders view data streaming as a key enabler for advancing AI adoption.

Sample Architecture of a Data Streaming Solution

Data streaming is the low-latency processing of continuously arriving data, which enables real-time tracking and analytics, business process automation, personalized user experiences, and more.

ScienceSoft’s software engineering experts suggest Lambda architecture as the most universal option suited for complex data streaming tasks, including big data analytics and AI-powered automation. The provided sample architecture can be simplified or expanded, depending on the unique data streaming needs of each organization.

Data streaming solutions can process data from a variety of sources, including enterprise software (e.g., ERP, CRM, EHR), IoT systems, customer apps (e.g., ridesharing apps, ecommerce platforms), and external sources (e.g., financial data marketplaces, weather information systems).

In most cases, stream data has value for both real-time and historical use. For instance, financial transaction data is processed in real time to enable ML/AI-powered fraud detection; then, the results are accumulated in historical data storage that can be used to continuously improve the accuracy of ML/AI models. To support both low-latency output and historical data analytics, stream processing solutions can have two processing layers:

Analytical data storage is a data warehouse (DWH) or a big data database that aggregates data from both layers according to the chosen data model. This data becomes the source of reports and analytical insights for BI software and enterprise systems. Data scientists and data analysts can also query the storage for ad hoc data exploration.

The machine learning or artificial intelligence (ML/AI) engine is an optional block that enables advanced streaming data analytics (e.g., financial fraud detection, social media algorithms, dynamic price optimization). The ML training module is responsible for the continuous improvement of ML/AI models based on historical data.

Data quality, integrity, and security are enforced by the data governance framework that is usually developed in compliance with case-specific regulations (e.g., HIPAA for healthcare). Some of the most common data governance measures are encrypting data at rest and in transit, data masking and anonymization, data backup and recovery, and role-based access.

Strong Data Governance as a Key Reliability Differentiator of a Streaming Data Solution

Estimate the Development Cost of Your Data Streaming Solution