This week, we released VoltDB v8.2. Here are some of the highlights: Table TTL If you’ve ever built a high-volume app for Volt, or hooked Volt up to a Kafka or Kinesis stream, you know how important it is to age out the old data so...
Application State Management and Data-Driven Decision Making As the complexity of applications and systems increases, the size of the teams that work on these also increase. In these scenarios, having the system as a monolithic one inhibits the development team from being able to move...
This week published a new ebook focusing on six examples of VoltDB being utilized for real-time decisioning by our customers. It highlights ways that VoltDB is being used to: Levearge machine learning for real-time fraud prevention Transform personalization in media and gaming Revolutionize telco real-time...
Digital transformation has become one of those industry-swamping buzz phrases that emerges to dominate discussion and innovation in telecoms. However, the good news is that we’re moving beyond the marketing hype of digital everything and figuring out how digitally transformed telecoms is actually going to work.
We are pleased to announce VoltDB v8.1. This release includes features that can reduce the total cost of ownership of your VoltDB solution, with improvements to memory management of replicated tables, and performance improvements for many applications. These features include: Reduced Memory for Replicated...
Database Blog Trends, First Half 2018 Looking back over the first half of the year, we notice three trends in reader interest with our blog posts. They are: Defining the real-time database market How to compare database technologies, and How cloud database migration. In this post,...
As we enter the 5G era, strategic telcos are digitizing their infrastructures to not only handle the explosion of data requirements, but to also take advantage of the new business opportunities and revenue streams that will come about with the advent of 5G. Recently we held...
There is a massive difference between generating the input data needed by a Machine Learning model once (to prove a concept), and doing it continuously, indefinitely, at scale, and within short time periods. Years of experience working with disparate and imperfect data sets lead me...
In my previous blog on big data analytics, we discussed how to apply your big data analytics to real-time applications. The idea is that, if you have built analytics on your data, the next step is to use the analytics directly in the applications to...