The Ultimate Guide to Real-Time Data Processing Platforms

Understand how a real-time data processing platform unifies streaming, transactional, analytical, and AI workloads for ultra-low latency decisions at enterprise scale.

  • Learn what real-time data processing platforms are and why they matter
  • Unify streaming, transactional, analytical, and AI workloads across data silos
  • Reduce data movement and latency with in-transaction analytics
  • Evaluate six core capabilities: processing, analytics, hub, AI operations
  • Explore enterprise use cases like fraud prevention and risk calculations
  • Compare platforms vs DBMS, in-memory data grids, and stream processors

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About this eBook

A Real-Time Data Processing Platform simplifies and optimizes data architectures by combining transactional, stream, and analytical processing across data silos into a single platform. It’s designed for ultra-low latency outcomes with horizontal scalability, strong security, and disk-based durability, even across diverse, distributed data sources.

This ebook breaks down what these platforms are, how they work, and the technical building blocks behind them — including transaction acceleration (ACID SQL), stream processing integration, HTAP-style advanced analytics, system-of-record patterns, AI/ML data store capabilities, and data hub / DIH architectures.

You’ll also see where they deliver the most value in practice: credit card fraud detection, real-time risk management, smart decisioning, real-time transactional analytics, low-latency data integration hubs, and high-performance OLTP.

A Real-Time Data Processing Platform simplifies and optimizes data architectures by combining transactional, stream, and analytical processing across data silos into a single platform.

The Ultimate Guide to Real-Time Data Processing Platforms

Ebook on real-time data processing platforms that unify streaming, ACID transactions, analytics, and AI — with use cases, core capabilities, and comparisons to other architectures.

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