Big data architecture is the structure of the big data database that a business uses to direct its big data analytics. Designing has critical down the road impact on the usefulness and implementation of big data analytics so getting it right is critical – which is why you need a big data engineer.
Big data engineer
The title is apt, because big data architecture is much like the blueprint of a complex machine with many cogwheels. Big data engineers begin by developing a deep and multifaceted understanding of the big data analytics and business goals the big data architecture as a whole is meant to promote- and then they get to weighing the pros and cons of various design strategies. Only then, do they begin integrating the components of the big data architecture together with appropriate hardware and software, data sources and formats, analytics tools, data storage decisions, and results consumption.
Big data analytics
But do you really need a big data engineer, or big data architecture?
Not necessarily. If your system only performs single computing tasks then you probably aren’t topping 100GB of data, which does not require big data architecture. Even if you are analyzing terabytes and petabytes of data – and doing it consistently — a scalable server would probably suit you better than a massively scale-out architecture like Hadoop. If what you need is big data analytics, then you should consider a scalable array that offers native big data analytics for stored data.
Big data database
However, if in addition to processing 100GB + datasets you need to extract information from extensive networking, are willing to invest in a big data database and big data analytics project, store unstructured data that needs to be transformed into a structured format for better analytics, and want to perform big data analytics for business needs such as applying sentiment analysis to social media posts… well if any of the above applies then yes, you probably do need a big data architecture and a big data engineer to carry it out.
