Extreme Data Volume

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Extreme Data Volume

On February 24, 2011, Posted by , In Mike Pilcher, With No Comments

Tabular data from ERP, CRM, SCM and operational systems was just the beginning. Now we have web logs, application logs, social media, mobile, tablets, NFC, appliance, machine data, sentiment, context and many other extreme varieties of data. These have caused data volumes to increase exponentially. If enterprises attempt to treat all data equally they will never be able to keep up. SAND understands that, like money, data is a commodity. Just like you wouldn’t keep a gold bar in your pocket and you wouldn’t take a treasury note to get a coffee, you need to treat different data types with different solutions.

###Massively Parallel Processing (MPP)

Applying the appropriate processing power to the data loading it as quickly as possible, ensuring it is available 24 x 7 x 52 requires the power SAND’s Massively Parallel Processing technology delivers. Built in conjunction with one of the early pioneers of software as a service (SaaS), SAND’s MPP technology was designed for the elastic cloud. It is not enough to take the approach favoured by hardware dependant vendors and merely to throw a never ending stream of disk, memory and nodes at the problem. An intelligent solution that uses the maximum number of processes to get data loaded when required and to perform queries when required, to scale both up and down is the answer.

###Generation-based Concurrency Control (GBCC)

When looking at volume it is not enough to look at simply the amount of data, we need to look at the amount of people that want to get access to the data. Most approaches in the marketplace take re-purposed technology from online transaction processing (OLTP). The problem is we are not processing online transactions. In an OLTP environment the approach must be to effect the most granular lock on a transaction to ensure performance for all the users wanting to get access to the underlying data, this is a write-mostly environment where the purpose of the data is to be inserted, updated or deleted. In an analytic environment the requirement fundamentally changes, the requirement is a read-mostly, users want access to data that most of the time does not need inserting, updating or deleting. SAND’s solution is Generation Based Concurrency Control (GBCC) delivering a solution optimized for massive volumes of users accessing vast numbers of users.

###Time Travel

Enterprises constantly change organizational structures, product structures and approaches to customers, adapting to today’s dynamic environment and ready for the permanently connected enterprise of tomorrow. This requires the underlying data structures to strive to flex to meet these changes and these legacy structures do not move as quickly as the enterprise demands. SAND’s Time Travel solves this problem by ensuring the enterprise can effect these changes without changes to the supporting data structures, thereby delivering agile analytics to meet business change.


The volume of data that proliferates is not only on the central servers accessed in high network bandwidth environments. As data is distributed to where users want it and when they want it, data is pushed to the user for access in off line modes. SAND Mobile is the right technology for mass deployment of secure local data on disconnected devices. Using SAND’s elastic cloud technology only the data that has changed since the last deployment to the mobile device is transmitted to the mobile device, keeping network traffic to the minimum.

This is how SAND is handling the extreme volumes of data facing today’s business. Next time we’ll look at how this variety and volume leads to extreme velocity.

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