5 Signs You’ve Outgrown Your In-Memory Computing Platform

Modern data demands are outpacing the capabilities of many legacy in-memory systems, prompting organizations to reconsider their architectural choices for scalability, latency, and operational efficiency.

  • Check scalability limits in current platforms
  • Ensure low-latency access to all data types
  • Watch for rising ops costs or SLA risks
  • Favor distributed processing models
  • Require strong SQL and multi-model support
  • Verify smooth integration with existing systems

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About this white paper

For decades, caching platforms and in-memory databases helped firms speed access to critical data managed by legacy stores or streamed from real-time sources. But today, many of these platforms are straining to cope with increasingly aggressive business demands. 

  • Data volumes continue to grow.
  • Advanced analytical and AI applications generate complex, compute-intensive workloads.
  • Essential data – streaming, transactional, and historical – must be immediately accessible.
  • Speed, availability, and accuracy are vital because more and more business decisions just can’t wait.

Simply put, firms are finding that once-serviceable in-memory computing platforms no longer fit their needs. Concerns about scalability, operational expenses, and service-level agreement (SLA) violations lead many to look for a better alternative. This white paper explores in-depth the five key signs that you may have outgrown your in-memory computing platform, and if you have, what to do next.

Firms in finance, telecom, and technology rely on GridGain to cut data access latencies by 80% and save 50% on infrastructure costs for their critical OLAP, OLTP, and HTAP applications.

5 Signs You’ve Outgrown Your In-Memory Computing Platform

Is your in-memory platform straining under scale, costs, or complexity? Discover the 5 critical signs that signal it's time to upgrade to a unified, high-performance platform like GridGain.

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