What are the three V’s big data which often define its characteristics? It is a definition developed by IBM in consultation with Google and the Department of Defense. To better understand it, we first need to understand what big data is. Large data, as it is defined, involves the integration of numerous discrete data, processed in parallel, to form one big data. Data is traditionally used to refer to collections of individual discrete units.
For example, a hundred million customer records can be considered as large data. This data represents the entire retail sales volume over the last twelve months. We can even use this definition as a parameter for identifying trends and patterns. But the important point to note is that it is not the entire data set which is large data. Rather, it is the large data sets that provide the rich information that the financial analysis require.
The traditional way of handling big data was to build huge computing clusters. Using such computing clusters, data centers were established which housed thousands of servers. Each of these servers is able to process hundreds of terabytes of data. What are the three v’s here?
In a traditional Hadoop deployment, nodes are instantaneously connected via a network. The question we come to is how to deal with large data which does not have to be processed immediately. Data warehousing is one approach which deals with this problem. With data warehousing, a user will send requests to a distributed database which is maintained on several different machines. The data will be accessed, analyzed, and returned in a timely manner.
One popular tool is the Map-reduce framework. Another popular tool is the Spark data processing library. The key innovation here is in the middleman part. The machine can carry out the analytics without needing to actually sit at the system. Instead, the analytics are carried out by the low layer.
There are tools which are capable of handling large volumes of data. Such tools are typically batch-oriented and therefore must scale up and down quickly. batch processing tools are used to achieve immediate results from big data. What are the three v’s here again? Well, these are the time, resources and money required to scale up.
For those who are running a traditional system, they will tell you that their main concern is with capacity. How do they deal with big data which is increasing in volume? They cannot afford to lose data or put off analytics because doing so could lead to data loss. What are the three v’s here?
The first v is resources, which means the amount of time it takes to run one analytical program. If you run hundreds of programs at the same time on the same database, your server will not be able to support it. Secondly, there is the money factor, which means investing in a data center and getting dedicated servers. This will pay for itself within just a few months if you are using a high-traffic site for your analytics. Lastly, there is the space factor, meaning you need to have a very large data center to house your data.
For new businesses, they will tell you the same three V’s: analytics, capacity and space. However, this is only if they have not yet acquired their own server. They cannot afford to have a shared server without any security measures in place. Furthermore, without knowing how much analytics are currently being used, it would be difficult to decide if expansion is necessary. It is also hard to determine what are the three v’s, when asking a new client “What are the three I’s of your data warehouse.”
A better question might be “How can we make sure all the data is consistent and the usage patterns don’t change?” The answer to this is to load balancing. Balancing is when two workloads try to write to the same physical location without conflict. What are the three I’m again? Load balancing helps prevent consistency problems by providing the Big Data tools necessary to manage the data.
If a company has obtained its own server, then it is time to ask “what are the three I’s of big data which often leads to operational problems.” In addition, if you are trying to outsource the big data project to a provider with no background in the field, then it is necessary to understand the V’s and what each one does. It will save you a lot of time, effort and money.