Big Data Architecture and Patterns

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What is big data?

First of all, classifying big data isn’t an easy thing to do. You can’t just look at something and say, “Yup, that’s some big data.”

Big data is, like the name suggests, big data sets that can be analyzed in order to reveal trends and associations that are generally relevant to human beings. Big data is stored and processed in several ways.

big data

Big data is used, for example, to collect information on a customer so that you can optimize the money you get from them by, let’s say, determining which promotions you should send them and at what time of day.

Because the internet and the web is being so used, the increasing amount of data that is being produced must be processed. The big idea regarding big data is that having more data should provide more accurate results in order to provide better operational efficiency, like you saw in the aforementioned example of a customer you’re gathering information on. The data is separated between structured, unstructured, and semi-structured and it is generated in relation to e-commerce and social media, especially.

Hadoop and Cassandra are being used more and more in order to handle the extremely large amount of data being processed because, quite simply, there is too much of it for organizations to do on their own. Hadoop and Cassandra are considered solutions because they are platforms that process this big data. These platforms, along with others of their kind, are dependent upon distributed architectures. These software perform parallel queries across many, many servers. One must make a game-plan that manages infrastructure for this kind of analytic.

Big Data Architecture Patterns provides an approach that simplifies one’s defining of big data architecture. Assessing whether or not a scenario within a business is a problem with big data is a priority; therefore, pointers are included in order to determine if a situation is appropriate for big data solutions.

A virtual infrastructure gives agility that is necessary to compute moments dynamically. At the same time, it allows non-analytic work to run next to each other. Because of this, it isn’t necessary to buy manage hardware that is specific to applications. There is also more control of resource placement because these policy-based configurations provide workloads in just minutes.

Data Lake Vision

There are businesses which are looking to Data Lake for a solution, which is a data platform, because it allows streaming data in one location to acquire knowledge from third parties and from the rest of the enterprise. The previously mentioned Hadoop, for example, is being used as a data lake because it’s an open source that uses common languages to process layers.

Stratoscale

What does Stratoscale have to offer? It allows one to continue with initiatives concerning with big data. It also permits organizations and businesses to concentrate on improving their productivity and decisiveness using big data.

Conclusion

Big data is becoming more relevant in the lives of those working in organizations because it provides them with more accurate readings about their customers; however, there is so much data that it’s hard to comb through. That’s why there are platforms coming out which help process all of this information.


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