Big Data Will Be Dead in 5 Years. Everything will be big data, so it… | by Lewis Gavin | Oct, 2020



Everything will be big knowledge, so it will not want a particular title

Dead Hadoop (picture by creator)

Big knowledge is a good advertising and marketing time period however in actuality, that’s all that it’s. It’s a time period used to excite enterprise executives to make them really feel like Google or Amazon. The actuality is that big knowledge doesn’t imply something and its that means is simply going to scale back.

As corporations develop into extra acquainted with knowledge processing and repair suppliers summary away extra complexity, big knowledge will simply develop into knowledge. Big knowledge engineers will simply develop into knowledge engineers and any knowledge engineer price their salt will be dealing with what we now name “big data processing”.

Fear not although, this doesn’t imply your big knowledge data is out of date, simply that its title may not imply as a lot because it as soon as did.


It isn’t useless in any respect. In truth, it’s solely going to develop into extra distinguished. By 2025 it’s predicted that the worldwide “data sphere” will be 175ZB (zettabytes), up from 50ZB in the present day.

All of this knowledge goes to want crunching a technique or one other so how can big knowledge be dying?

The reply is that “big data processing” itself isn’t going wherever, it will simply develop into the norm. Because of this, we’ll now not be calling it big knowledge and wish specialised “big data engineers”. The complexity and scaling behind big knowledge purposes will be abstracted away by cloud suppliers like Amazon so that every one knowledge engineering may in impact be “big data engineering”.

This is not a brand new phenomenon both, it began again in the early days of Hadoop. When Facebook began out utilizing Hadoop to handle its big knowledge units, they discovered writing MapReduce jobs lengthy, laborious and costly. This is as a result of on the time MapReduce jobs required programming, so they constructed Hive.

Hive is SQL on Hadoop. Facebook abstracted the complexity away from writing MapReduce jobs they usually grew to become easy SQL queries. This meant that anybody who knew SQL had the power to construct big knowledge purposes, not simply big knowledge engineers.

Fast ahead to in the present day and you’ve got on demand scaling knowledge options like Amazon Redshift or Google Big Table. Most fashionable companies cater to small knowledge however can be simply scaled to work for big knowledge.

You may use Amazon S3 as the information retailer for a “small data” software but when your knowledge footprint grows then you may nonetheless use S3 as a knowledge lake because it’s successfully an infinite knowledge retailer. There are even knowledge processing instruments like Athena that sit on prime of S3 now making it much more compelling.

None of us actually know what the subsequent big advertising and marketing buzz phrase will be. Data Science is already following an identical path. At the minute it’s the poster little one of the information world however once more, as its complexity will get abstracted away then the necessity for specialised knowledge scientists will cut back and its buzz will dwindle.

The necessary factor to remove is just not that big knowledge processing is dying, however the time period “Big Data” itself is dying. Behind all the abstractions, big knowledge processing methods will nonetheless be there. We’ll nonetheless be utilizing horizontally scaling clusters, we’ll nonetheless be decreasing knowledge ingestion latency and processing petabytes of information. None of that is going away, these methods are simply being hidden so they’re extra accessible to everybody.

We will nonetheless want knowledge engineers who’re expert in knowledge extraction and manipulation. We’ll additionally nonetheless want knowledge scientists and analysts who can construct predictions and supply reporting.

What will be lacking is the power of the engineers to construct a strong and scalable knowledge lake from the bottom up, as this may be deployed with the push of a button. The knowledge scientists received’t want to grasp as a lot of the “nitty-gritty” maths both. Instead, they’ll solely must know which fashions are required and what knowledge to offer. The complexity of coaching and deploying the mannequin will be abstracted away and offered as a cloud service.


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