Large amounts of data can provide an enterprise with very real and very actionable insight—if that data is properly managed.
In our latest whitepaper, Navigating the Flood: Building Value by Reducing Data Complexity and Properly Managing Your Data, we provide you with a deep dive into how to assess your data, how to simplify your data, and the benefits of data warehousing solutions.
Here’s an excerpt from the section on data simplification:
Not all data is created equal. Some will be incomplete, some won’t be useful from an analytics and business intelligence standpoint.
In order to simplify data, you first need to change the way your analytics projects are run. This means overhauling the way your data is distributed and handles queries.
Scaling out vs. scaling up
As your amount of data increases dramatically, simply buying and installing larger and larger servers is no longer an option.
Eventually, you will need a solution that allows you to store a large quantity of data, while also making it possible for workloads to be spread out, in order to be worked on simultaneously.
So the single server hardware and SMP solution you’ve relied upon in the past becomes a battery of smaller servers with distributed workloads running massively parallel processing (MPP) technology.
By moving to MPP, you gain the ability to distribute queries across one or more nodes, so instead of having a single query hit an eight terabyte table of data, it can spread that two terabyte table across eight nodes. Rather than scaling up you’re scaling out.
The benefits of this solution are twofold:
1. Greater efficiency
Because workloads are spread out across multiple nodes, queries to a pool of data are accelerated.
If we stay with the example above, an eight terabyte table across eight nodes will theoretically — if there are no errors — return a query 8x faster than a query hitting a single pool of eight terabytes.
In other words, it’s the difference between fishing in a pond instead of a lake.
2. Reduced Costs
Beyond that old equation of greater efficiency means lower costs, an MPP solution also reduces your reliance on commodity type hardware—huge boxes with huge amounts of RAM.
By scaling out your workloads, your overall hardware needs expand while also shrinking in price, as the smaller the workload, the smaller the horsepower is needed to handle queries.
To read the full whitepaper and learn how you can utilize data to provide better products and services for your customers, download your free copy of Navigating the Flood: Building Value by Reducing Data Complexity and Properly Managing Your Data.
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