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Innovating the Three Most Influential Factors of Analytic Speed

By on May 23, 2016

“That is the way to learn the most, that when you are doing something with such enjoyment that you don’t notice the time passes.” – Albert Einstein

Even before the era of computers and computing interfaces, Albert Einstein understood the value of user experience. Apple has certainly embraced the importance of user experience. And online gaming companies, with the continuous study of game flow and other user experience metrics, have an almost religious relationship with how customers use their products.

What if Albert Einstein’s quote was applied to you, the business or data analyst. What if you had a user experience that allowed you to learn from their data in such a way you could go about their tasks with enjoyment, not drudgery? This would create a quantum leap in speed, measured in terms of how fast you found the answers the business needs.

Speed should be measured not just on performance benchmarks, but also in terms of how fast one completes a job – end to end. There are three key factors that impact this:

  1. How in-tune is the analytic workflow to how an analyst works?
  2. How fully self-service is the UI and UX?
  3. How is the analysis actually executed against the full data?

It’s these three factors that fueled our decision making when we scoped out what we’d introduce to you today in Datameer. Read on to find out not only what’s new, but more importantly, why it matters to you:

The Analytic Workflow: Reinvented

The analytic workflow is an under looked yet vitally important aspect of speed. The “enjoyment” Albert Einstein referred to is majorly influenced by the workflow. If the analyst can “get in the zone,” similar to how gaming companies try to get their users into the flow zone (see graph below), then finding answers from big data will be faster, simpler and more enjoyable.

Datameer gaming flow zone

In Datameer, we’ve completely reinvented the analytic workflow because we know analysis is much faster in modern BI when it is iterative and fluid. Such a user experience feeds the experimentation process, which is how you perform data discovery. In Datameer, the traditionally sequential steps of data integration, preparation, analytics and visualization are blended into an open, fluid interaction, rather than a linear one.

Using fluid data discovery, you can experiment with each phase of the cycle. The different phases of the cycle open at the same time, allowing you to make an iterative set of tweaks to any of the phases, and directly see the upstream and downstream impacts. You can quickly run through experiments to find answers without ever having to switch context. Quickly and efficiently, you discover the answers you seek.

True Self-Service

Often times analytic software products will put barriers in the path of analysis by forcing analysts to drop down and code at certain points in time. This causes a shift in context, tools or team members, that disrupts the workflow process.

Many platforms have gaps such as these which you should be aware of, including:

  • Some platforms require you to build a data lake, write code to ingest all your data into Hadoop before it can be analyzed. This creates delays when trying to add new data to the analysis process.
  • Other platforms will force you to model your data writing Spark SQL to create views on the data for analysis. This breaks the workflow and creates delays in the integration and preparation steps.
  • Specific bridges, such as OLAP on Hadoop or SQL connections to Spark, will force you to write programs that pre-structure your data to fit a specific static model. This also breaks the workflow and creates delays in the integration and preparation steps.

Datameer is a truly fluid data discovery platform that is completely self-service, providing a means to perform all the steps of analysis within a common iterative process by a single analyst without having to drop down and code. The completely redesigned UI is streamlined and purposely makes your data the center of your experience.

Tableau and Datameer Your Big Data Challenges Solved regulatory compliance Big Data Visualization Datameer Smart Execution job graph

–> Click here to learn more about how the data discovery user experience was completely re-imagined in Datameer to streamline your path to answering questions.

Ensuring the Fastest Execution of the Analysis – Every Time

And of course, how fast the engine can crunch through the data is also critical. This involves using the most appropriate execution engines, including Spark, to handle the right analytic workload at the right time. But it also means that the technical complexity of how the job is executing it should be hidden from you. You should only need to know that it executes quickly and efficiently so your experimental workflow is not inhibited.

A number of products can connect to or embed Spark as execution engine. But the technical complexity of using Spark is not hidden. This creates the need to understand how to use Spark, and often times how to program in Spark. It also creates future upgradability problems as Spark changes, or new execution engines emerge.

By adding Spark to Smart Execution in Datameer you’re guaranteed a modern BI platform that optimizes for the latest and greatest execution engines while abstracting the technical complexities. That gives you the best of both worlds – fast analytic job execution and future-proofing for easy upgrades to new technologies as they mature on the market.

–> Click here to learn how Datameer leverages powerful execution engines such as Spark while hiding all the technical complexities.

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John Morrell

John Morrell

John Morrell is Sr. Director of Product Marketing at Datameer.

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