diver platform 7.0When Dimensional Insight’s Model engine was introduced in 1989, it had many advantages compared to other relational databases at the time. Today, as columnar databases grow in popularity – and in performance – we have again outpaced the competition with the introduction of our new Spectre data engine.

Tweet: 3 reasons you should upgrade to Diver Platform 7.0 and Spectre

If you’re a Dimensional Insight customer, here are 3 reasons why you should upgrade to Diver Platform 7.0, which includes Spectre technology. (And if you’re not a Dimensional Insight customer, here’s why you’ll want to take a look at what we have to offer.)

1. Spectre optimizes performance and ease-of-use

When we introduced the Model engine, it performed well on the hardware of its time. However, these servers had more limited specifications than today’s servers do. The Model engine is still effective in that it pre-calculates many dives during the build process, saves the tables, and at run-time loads the results when requested. It makes end-user performance fast even without fast hardware.

However, Spectre is much better. We designed Spectre from the ground up to optimize performance and perfect ease-of-use features for all users. It takes advantage of the latest hardware advances to boost performance in business environments where there is an increase in quantity and diversity of data in varied formats from different platforms.

2. Large data volumes are no problem

Unlike the Model engine, Spectre has no dimension limit, and it is capable of handling larger data volumes and has much faster build times. In Spectre, a user can build a single table that builds faster, takes up less space on disk, and runs faster. Spectre also has great multi-tab performance and the integrated time series features make it easier to deal with time. With larger data volumes using the Model engine, we used to say that you could take a coffee break while waiting for your marker to open. Sorry to say, but with Spectre, coffee breaks are a thing of the past. Your marker will open in just seconds, even with tens of millions of rows of data.

3. Spectre has advantages over other columnar databases

But why choose Spectre over other columnar databases that are out there? First of all, Spectre makes it easy to build a columnar database. Like with the builder for the Model engine, Dimensional Insight has the concept of the automated build where you just need to describe the shape and source of the data and the Spectre builder takes care of the rest.

We also optimized Spectre’s database for the kinds of problems our customers face such as handling Quickviews and multi-tabs, which tend to be a problem with a standard relational database. Spectre has integrated time series making it easy to work with dates and periods.

One more unique aspect of Spectre is it can optimize anything that comes along because it compiles calculations to machine code without an interpreter. It has a flexible expression language, meaning the database can do certain operations that couldn’t be done in the Model system. Every column and every record is available for calculations – not just what was already built in.

Finally, Spectre has a migration path that allows customers to easily move from Models to Spectre, something that wouldn’t be as easy with other columnar databases. And Dimensional Insight is ready to help with that transition.

If you want to learn even more about Spectre, register for the Dimensional Insight Users Conference. DIUC17 will be held in Boston June 5-8, and you can expect to learn a lot about Spectre there.

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Jamie Clark

Jamie Clark

As senior developer at Dimensional Insight, Jamie Clark is the lead developer of DivePort and the server components of DiveTab, and he manages and writes code for Spectre. Jamie is an expert in the inner workings of almost every Dimensional Insight product. He joined Dimensional Insight after graduating from the Massachusetts Institute of Technology in 2001. Jamie is also a certified beer judge.
Jamie Clark