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Big Data

451 Research Report “Points & Polygons: VoltDB adds Geospatial Support with v6.0”

Tuesday, February 16, 2016

Jason Stamper, lead analyst at 451 Research, reported today on VoltDB’s newest software release, v6.0. As Stamper points out, v6.0 builds on the foundation of VoltDB’s in-memory, massively parallel architecture. In today’s data-intensive world, your business is only as fast as your database.


We’re thrilled that 451 Research decided to highlight our newest release. We take great pains to understand market needs and deliver the tools to help you advance your enterprise to the next level of data management.

Big DataCamp LA 2014 Wrap-Up

Monday, June 30, 2014

Amazing. That’s about all I could say all day the Saturday before Father’s Day. I was asked to speak at the LA Big Data Camp (#BigDataCampLA) on that day, which I had happily agreed to before I actually realized it was _that_ weekend. But I went down as I agreed, thinking there couldn’t possibly be a big audience on that Saturday morning in LA to talk about data. Amazing!


I heard from the organizers (who did a really great job) that they had over 900 registrants! As of 10 am they had already exceeded the norms for attendants vs. registrants. Needless to say turnout was amazing.


It wasn’t just

Bringing Rack-Aware Topology to VoltDB

Wednesday, September 9, 2015

One of the key tasks during initialization of a VoltDB cluster is determining cluster topology. The topology includes placement of the partition masters and replicas on physical VoltDB nodes. Once determined, the topology does not change unless node failure or elastic scaling happen.


For some years, VoltDB used a simple algorithm for determining topology. It worked by round-robin assigning all partition replicas to different nodes until all sites on each node were assigned a partition. Partition masters were chosen in round-robin order so they spread out in the cluster.

Connecting VoltDB to the Big Data Ecosystem

Monday, June 23, 2014

With its ability to act on data coming in at the rate of hundreds of thousands to millions of events a second, VoltDB is ideally suited as an operational database to process fast data. Fast data creates Big Data, so moving data out of VoltDB becomes very important once that data has been processed and is no longer of immediate value. To move data out of VoltDB, use VoltDB Export. VoltDB Export allows you to transactionally push data from VoltDB into another system, similar to an ETL (extract, transform, load) process.


VoltDB lets you automate the export process by specifying certain tables in

Disrupters Use Fast Data to Create Advantage - So Can You

Tuesday, March 8, 2016

The overwhelming availability of fast data, the datafication of humanity, is igniting a new era of innovation and reshaping industries. Volumes of data are pouring into enterprises, drowning them in the details about their customers, their products, and their networks.


Market disrupters have figured out how to harness this inflow of data to create business value, the minute (or more precisely, the millisecond) it enters the enterprise.

Fast Data Enabler VoltDB in the 2015 Gartner Magic Quadrant

Friday, October 23, 2015

Gartner recently issued the 2015 Magic Quadrant for Operational Database management Systems and VoltDB is featured for the third year running!


VoltDB is the only in-memory SQL OLTP database in this Magic Quadrant report. It’s heartening to see all the hard work and dedication that has been put into building our company and product be recognized, but what I like best are the significant strengths called out:

  • Performance -- highest score for high-speed data ingestion
  • Fast Data -- more than half of VoltDB’s customers use our hybrid transactional/analytical processing (HTAP) capabilities to power

Fast Data – The Opportunity Created by Connecting Everything

Wednesday, September 2, 2015

The world is becoming more connected and more interactive, driven by two mega trends: people increasingly connected via mobile devices and the physical world connected via the Internet of Things. Seventy percent of the global population have a mobile subscription (4.9 billion people, according to Ericsson) – making connection to the Internet more ubiquitous than ever. At the same time, inexpensive sensors are being deployed on everything from cows to light bulbs (50 billion devices by 2020, according to Cisco), bringing the physical and digital worlds onto one network.



(To view larger, click

Five Things You Didn’t Know About VoltDB V4.0

Tuesday, August 12, 2014

#1) Fast Data Integrations

As we like to say at VoltDB, Big Data is created by Fast Data. Because VoltDB is able to ingest and transact on data at phenomenal rates, VoltDB often finds itself at the front end of Fast Data applications.


At the front end of Fast, VoltDB needs to get data in quickly, make decisions, and as the data ages and is no longer needed, get data out just as quickly as it arrived. To smoothly integrate into these Fast applications, VoltDB introduced several new messaging importers and exporters this year. They include:

  • Kafka Export
  • Kafka Import
  • RabbitMQ Export

We’ve also

Integrating VoltDB into the Hadoop ecosystem with Hive and Pig

Tuesday, October 28, 2014

Imagine an online ad broker that has an extremely low latency decisioning system for bidding for online ad space and recording visitor ad views and click-throughs. The company also has a large Hive warehouse it analyzes to fashion ad campaigns for target consumers; this relies on the decisioning system data. In such a use case, using VoltDB is ideal because it excels at ingesting and processing data in real time, and it also features a high-performance data conduit to Hadoop via its export feature.



To enable this feature you need to configure the http export module in the deployment file

Introducing VoltDB v6.0

Wednesday, January 27, 2016

VoltDB is already the go-to product for developing fast data applications because of its unparalleled performance, availability, and durability. v6.0 builds on that foundation by extending existing capabilities and simplifying the development process.


What does a VoltDB application look like? Some of the characteristics are:

  • Applications ingest and process lots of data - data (events) being delivered at hundreds of thousands to millions per second. The workloads being thrown at these applications are write-heavy.
  • Applications need to compute, track and display real-time analytics on live

Part Four: You’d Better Do Fast Data Right – A Five Step approach

Wednesday, July 30, 2014

The last post defined what the Corporate Data Architecture of the future will look like and how “Fast” and “Big” will work together. This one will delve into the details of how to do Fast Data right.


Many solutions are popping onto the scene from some serious tech companies, a testament to the fact that a huge problem is looming. Unfortunately, these solutions miss a huge part of the value you can get from Fast Data. If you go down these paths, you will be re-writing your systems far sooner than you thought.


I am fully convinced that Fast Data is a new frontier.

Part Three: Designing a Data Architecture to Support Both Fast and Big Data

Monday, July 7, 2014

In post one of this series, we introduced the ideas that a Corporate Data Architecture was taking shape and that working with Fast Data is different from working with Big Data. In the second post we looked at examples of Fast Data and what is required of applications that interact with Fast Data. In this post, I will illustrate how I envision the corporate architecture that will enable companies to achieve the data dream that integrates Fast and Big.


The following diagram depicts a basic view of how the “Big” side of the picture is starting to fill out.

Simplifying the (complex) Lambda architecture

Monday, December 1, 2014

The Lambda Architecture defines a robust framework for ingesting streams of fast data while providing efficient real-time and historical analytics. In Lambda, immutable data flows in one direction: into the system. The architecture’s main goal is to execute OLAP-type processing faster - in essence,  reduce columnar analytics from every couple of seconds to 100ms or so, without actually enabling interesting new applications like real time application of user segments/scoring.

The rise of NoSQL is an opportunity for new RDBMS solutions

Friday, July 18, 2014

Written by Francis Pelland


It should come as no surprise that NoSQL has become popular over the past few years. This popularity has been driven in large part by the app revolution. Many new apps are hitting millions of users in less than a week, some in a day. This presents a scaling problem for app developers who seek a large audience.

Scaling a typical RDBMS system like MySQL or MS SQL Server from 0 to 1 million users has never been easy.  You have to setup master and slave servers, shard and balance the data, and ensure you have resources in place for any unexpected events.