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Tag Archives: map-reduce

3 Game Changing Capabilities in SQLFire

According to one of our partners, vFabric SQLFire is a product he wishes more customers would use.

“SQLFire is a game-changer.  I think many companies underestimate the value of scaling the data later horizontally.  Every project I propose has a business case, and I see a tremendous amount of value being unlocked with this product—not just for the CIO or CTO’s agenda, but for the CFO and CEO.  Then, you add the fact that the whole application stack is virtualized and has solid integrations. It’s a simple story, the product allows you to add a lot of value in a really cost effective way.”

What makes SQLFire such a game-changer?

In this article, we’ll talk more about three game-changing capabilities: server groups, partitioning, and redundancy.

If you haven’t been following our stories on SQLFire, see the end of this article for a  list of posts and key capabilities that help explain how transformative SQLFire can be to your data management strategies. Continue reading

3 Key Stages to Evolve from Legacy Databases to a Modern Cloud Data Grid

How do you plan a roadmap for moving from a legacy data architecture to a cloud-enabled data grid? In this article, we will offer a pragmatic, three-stage approach. At SpringOne-2012, the “Effective design patterns with NewSQL” session (see presentation embedded below) generated a lot of interest. (Thank you to everyone who joined us!) Jags Ramnarayan and I discussed problems with legacy RDBMS systems, NewSQL driving principles, SQLFire architecture, application design patterns as well as data consistency and reliability.

We went deep into vFabric SQLFire which is a pragmatic solution that addresses these data challenges:

  • How do I architect my data tier for very high concurrent workloads?
  • How do I achieve predictability both for data access response time and availability?
  • How do I distribute data efficiently and real time to multiple data centers (and to external clouds)? 
  • How do I process these large quantities of data in an efficient manner to allow for better real-time decision-making? 

Continue reading