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Process Streaming Data as it Arrives & Make Instant Decisions | VoltDB
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Why VoltDB?

VoltDB / Why VoltDB?

Why VoltDB?

Process Streaming Data As It Arrives

VoltDB powers applications that require real-time intelligent decisions on streaming data for a connected world, without compromising on ACID requirements.

In the past decade we have seen:

  • Unprecedented growth in the velocity and volume of data.
  • Maturity of cloud infrastructures
  • The arrival of machine learning and artificial intelligence
  • Evolving 5G capabilities
  • The dawn of IoT applications

Real-time now means milliseconds, businesses demand moving from post event reconciliation to in-line data processing. Value of data diminishes rapidly. If action is not taken immediately, the opportunity to monetize an event is lost.

Traditional databases fall short in meeting these requirements; as Dr. Michael Stonebraker, founder of VoltDB, discovered during his research: almost 90% of the database’s allocated hardware resources were being used just to manage the DBMS instead of doing useful work. This research led to the H-store project which in turn led to the commercialization of VoltDB.

Real-time is the need of the hours. The “Store & Query” model is going to be, if not already, outdated in operational transactional data processing. Action oriented decision making while the data is fresh i.e. within milliseconds is the next key differentiator for data driven organizations. VoltDB’s most successful and steadfast customers invariably use VoltDB for processing streaming data as it arrives and make decisions in the moment to instantly monetize these events. For example:

Why VoltDB Works for Real Time Streaming Data

  • Fraud prevention instead of just reconciliatory detection
  • Compliance management on trade data
  • Policy charging and rules function
  • Intelligent campaign spend management

Real-time action oriented decision making requires:

  • Fast – Predictable linear performance
  • Smart – Machine Learning (ML) based decision making
  • Robust – Enterprise Readiness

To deliver on these real-time decisioning essentials, some organizations have tried the DIY approach, by cobbling together a multi-layered solution using disparate tools. Unfortunately, this approach only adds complexity and costs, while not delivering on the promise of a Smart, Fast, and Robust real-time platform. The DIY approach is fraught with problems, such as:

  • Lack of scalability due to the multi-layered architecture
  • Poor performance through latency introduced with each additional layer
  • Higher operational overhead due to poor compatibility between various layers
  • Inadequate resiliency with multiple points of failure at each layer
  • Higher hardware footprint and costs to maintain and operate the collection of frankenstein tools