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Smart Streaming - VoltDB
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Smart Streaming

Build intelligent applications that can make decisions from fast data in real-time

To deliver best-in-class applications for users that demand instant gratification, businesses are compelled to move from post event data processing to taking intelligent decisions in-event on data as it streams in. Real-time use cases are increasingly becoming the norm in verticals such as telecommunications, financial services, IoT, gaming, media, eCommerce, and more.

VoltDB Smart Streaming Architecture

In today’s world of fast evolving technology, processing streaming data in real-time is a must-have. A plethora of open source tools let you do just that. However, for businesses to gain the competitive advantage, app developers need to incorporate complex event processing in addition to real-time analytics on streaming data. Real-time intelligent decisions powered by machine learning is the recipe for success in the real-time application economy.

VoltDB is an in-memory relational database that was built for real-time operations with embedded machine learning. VoltDB processes streaming data sequentially and incrementally on a record-by-record basis or over sliding time windows, and utilizes it for a wide variety of analytics tasks such as, correlations, aggregations, filtering, and sampling. Information derived from such analytics gives organizations visibility into many aspects of their business and customer activity. Streaming data processing is beneficial in most scenarios where new, dynamic data is generated on a continual basis. Businesses can begin with simple applications such as collecting system logs and rudimentary processing like rolling min-max computations. Then, as applications evolve move onto more sophisticated real-time complex event processing. VoltDB offers:

  • Blazing fast queries at high throughput with an in-memory, distributed architecture.
  • A future proof investment – in addition to smart streaming users get a full-featured relational database with transactional analytics built-in for a multitude of fast data use cases.
  • The ability to apply machine learning to streaming data by importing complex models built by data scientists through PMML and other standards and implementing them as a stored procedure or a user defined function.
  • A complete plug-and-play solution – you can implement in production in just a few hours. Includes native connectors to ingest streaming data from Apache Kafka, Amazon Kinesis, or easily build you own.
  • Per-machine efficiency; to do more operational work for the same amount of computing resources. This leads to a considerably lower hardware footprint, and easier to manage smaller clusters.
  • Seamless deployment on large-scale distributed cloud environments.
  • Limitless scale simply by adding nodes, even when the database is running. VoltDB enables your transactional data processing to grow as your business grows.
  • Familiar, full-featured ANSI SQL; enabling you to leverage the most widely used and comprehensive database language in the world.
  • Designed to deliver high performance on commodity hardware at a much lower total cost of ownership than legacy databases.
  • Guaranteed ACID compliance.
  • Database replication (active-passive and active-active) – Constantly updated database copies ensure that you data is always safe, VoltDB can read/write cache with immediate consistency across replicas.
  • Active-active high availability ensures that you continue smooth operations even when faced with hardware, software, or network failure. While, most NoSQL solutions such as Redis have a Master/Slave node architecture, and as a result a single point of failure.

Streaming Data Use Cases

A/B Testing & Offer Management in Mobile Gaming

Mobile gaming application developers test out different versions of the same game with users in real-time. With the goal of increasing user engagement, improving stickiness, promoting virality, and ultimately monetizing the game.

User interaction data streams need to be analyzed in real-time, so that developers can serve customized in-game promotions and offers to users based on their individual needs and preferences.

Sensor Data Analytics

The Internet of Things (IoT) includes billions of connected devices generating massive amounts of sensor data. It is crucial to analyze and detect anomalies on sensor data as it streams in, to determine deviations from reference points in use cases such as: predictive maintenance, smart metering, healthcare alerting, smart devices, and more.