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Volume Variety and Velocity – These are the Big Data Buzzwords



Big data is data that will surely – be exceeding – the processing capacity of conventional database systems. The data can either be too big, or moving too speedily, or might now be befitting with the existing structures of your database architectures.

Thus, for gaining the desired value from this data, you must be choosing an alternative way and then, process it – with no doubts.

Volume Variety and Velocity - These are the Big Data Buzzwords rashi

The hot IT buzzword of 2012, big data has become viable – as cost-effective approaches – these approaches will also be benefitting the QuickBooks Hosting Providers have emerged well and then, taming the velocity, volume, and variability of massive data.

Within this data – lie those valuable patterns – and information, that was previously hidden – since the amount of work required to extract them –  was unmanageable. To the award-winning and leading corporations, such as Google or Walmart, this power has been accessed for a while, but at a ludicrous cost.

In addition to all of that, today’s commodity hardware, open-source software, and cloud architectures have successfully brought forward big data processing – within the reach – of the less well-resourced.

Henceforth, Big data processing will eminently be feasible – even for – the small garage startups, who will cost-effectively be renting server time – in the cloud-types.

Even the value delivered by big data – to an organization – will promisingly be falling – into two categories: the first is -> analytical use, and the second – enabling new products.

Concisely, Big data analytics can undoubtedly reveal insights – somewhere hidden previously by data – which will be too costly to process, such as peer influence among customers, that can anytime be – revealed by analyzing shoppers’ transactions, social and geographical data.

Thus, being able to process every item of data in a reasonable time – will surely be removing the troublesome need for sampling and then, promoting an investigative approach to data, in contrast to the somewhat static nature – of running the mandatory – predetermined reports.

How will Big Data be Looking Like?

If we think that big data will be a catch-all term, we must not think the same – it may be pretty nebulous, in the same manner – that the term cloud -has smartly covered the diverse technologies.

At the time the input data to big data systems could be chattering from the existing social networks, web server logs, traffic flow sensors, satellite imagery, banking transactions -reviewed on QuickBooks Cloud – or the broadcast audio streams, and MP3s of rock music, the content of web pages which will be scanning the government documents or the GPS trails – will surely be helping the systems – maintain the list – with lesser hustles.

Were these all – really – the same thing?

To clarify matters, the three Vs of volume, velocity, and variety are commonly used to characterize different aspects of big data.

They’re a helpful lens through which to view and understand the nature of the data and the software platforms available to exploit them. Most probably you will contend with each of the Vs to one degree or another.

# V Number One – Volume

The benefit gained from the ability – of processing massive amounts of information – will be the main attraction of big data analytics.

Either the owners are entertaining the more data beats out or preferring to have better models: the simple bits of math – will unreasonably be effective – given the fact that datasets are available – in larger amounts.

If you are the ones – interested in running the forecast – taking into account those few hundred factors – rather than five or six, this may be a question – that whether or not – could you predict demand – better?

Undoubtedly, this volume will be presenting the most immediate challenges to the conventional IT structures.

Also, it will be calling out – for the scalable storage – that may be used in the Cloud QuickBooks hosting process, and then – a distributed approach – for handling the queries.

This is the only reason that many companies have already decided to opt for the large amounts of archived data, perhaps – in the form of logs, but won’t be capable – for entertaining the capacity – with which this can be processed.

Assuming the fact that the volumes of data are larger than the ones – inclined towards conventional relational database infrastructures – can be coping with that, processing those options will surely be breaking down – broadly – into that choice – lying amongst the massively parallel processing architectures.

One may exemplify them — as – data warehouses or databases – like Greenplum — and Apache’s Hadoop-based solutions. So, this choice is often[ly] informed by the degree – with which the one of the other Vs — which means [variety] — has come into play.

Typically, the award-winning data warehousing approaches have involved those predetermined schemas, which will be suiting – a repetitive and slowly evolving dataset. Apache Hadoop, on the other hand, has placed no conditions – on the existing structures of the data – that can be processed by it.

# V Number Two – Velocity

The importance of data’s velocity — which will be adhering to the increasing rate at which data may flow into the organizational grids — has sincerely followed a similar pattern – if the same is compared with volume one.

Moreover, those problems previously restricted to segments of various industries – are now able to present themselves – in a much broader dynamics. Also, specialized companies such as financial traders have long turned systems that will now – be coping well – with those fast-moving data – to their advantage. Now – our turn – has come up.

Volume Variety and Velocity - These are the Big Data Buzzwords raaa

You must be thinking – Why is that so troubling? The Internet and mobile era has cleared the fact that the way we have been delivering and consuming products and services – like the QuickBooks Remote Desktop Services– will increasingly be instrumented, thereby – generating a data flow back to the appropriate providers.

Furthermore, those Online retailers are now able to compile those large histories of customers’ every click and interaction: not just the finally-fetched sales. Those who may quickly be utilizing that information, by recommending additional purchases – for instance, – will promisingly be gaining a competitive advantage.

Thus, the smartphone era will be increasing again – the rate at which data is collected, as consumers have been carrying with them – a streaming source of geolocated imagery and undoubtedly – the audio data files.

It’s not just the velocity of the incoming datasets – the issue must be this: it’s quite possible to streamline fast-moving datasets – into bulk storage – for later batches and their necessary processing. For example – the importance will be lying in the speed of the feedback loop, then – taking data from input through – to the desired decision.

We may proclaim the fact that a commercial – from IBM – must be making the point that – you may not be crossing the road(s) – if all you are acquiring – is a five-minute old snapshot – of the required traffic location. There will be times – when you simply – fail to wait for that report – which may be running well on Hadoop – for completing the allocated task.

# V Number Three – Variety

Rarely will data be presenting itself – in that form – perfectly ordered and can readily be processed. A common theme – which can’t be ignored by the big data systems – is that – the source data may emerge in diverse forms, and may not be falling – into the clearer and neat relational structures.

It could be text – accessed – from the trending social networks, image data, a raw feed – obtained from your Qb Cloud account – directly from the identified sensor source. This may be possible that none of these things – will be coming in-hand – and are ready – for integrating the demanded applications.

Even on the web, where computer-to-computer communication is entertained with some guarantees, the reality of data has become quite messy. This is because different browsers will be sending different data, and the users will be withholding – that information.

Later, they may be using differing software versions or vendors – for the purpose – of communicating well – with the existing peers. And, you can undoubtedly – bet – that if part of the process will be involving a human, there will surely – be errors and also – the non-required inconsistencies.

A common use of big data processing will be – to take unstructured data and then, extracting ordered meaning, for consumption – this may either be from the humans’ end or as a structured input – chosen for an application.

One such example will be entity resolution – it is the process of determining exactly what a name will precisely – be referring to. Is this city  England, London, or Texas? By the time your business logic will be getting to this, you don’t need – to guess more.

Confidently, the process of moving from source data to processed application data will be involving – information loss. When you are prepared well, you will be ending up – throwing the unused stuff – away. This has underlined an award-winning principle of big data: when you can handle it, you must be keeping everything.

Time to Know Where the Organization Want to go Ahead

Finally, it is necessary to remember the fact that big data is no – a nostrum. There are chances of finding patterns and clues – which may be hidden – in your acquired data, but then what – could be the scenario?

For this, it is necessary to first – decide what the problem was- then we may later prepare the strategies – for solving those – the problems may be related to Qb Hosting.

Henceforth, while picking a real business problem up, such as how we must be changing the advertising strategies – for the sake of increasing spend done on a customer, it will be guiding the implementation well. So, the organizations must be preparing the strategies – keeping in mind – velocity, volume, and variety of big data.

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