Data is the raw resource.
Along the factory line, it gets mashed, converted, manipulated, updated, aggregated and finally exported into a final product.
A production line similar to a manufacturing plant, which takes raw materials, creates something in a series of repeatable steps, into a finely finished product.
This method gets applied to the reporting life cycle.
Whether it's traditional reporting, Enterprise Data Warehousing or Big Data.
Raw data comes in, it's massaged, perhaps aggregated and finished into an end product.
Typically the end product is a report, an Excel Spreadsheet, a PDF, a CSV File, a Dashboard, Graph, Chart or raw data dump.
So to say that one methodology is better than another is not quite accurate.
Doesn't it really depend on your business case, what questions you are trying to answer. Wouldn't that depend on what Reporting methodology you choose.
If Business Users want to discover data trends, wouldn't Self Service be optimal?
If CEOs wanted Graphs an Charts, with drill down capabilities, wouldn't Traditional Reporting work?
If you have huge volumes of data, wouldn't Big Data options suffice?
So as you can see, all methods work, depending on your scenario. Is one better than another?
Is a fork better than a spoon? Not for eating soup. Is a knife better than a spoon? Not for cutting meat. Each has value depending on your needs.
When the spoon was created, did people say, "This triggers the end of the fork."? Nonsense.
Thus Big Data will solve problems which Relational Databases could not. And NoSQL databases will solve other problems. And Relational Database will solve others.
I don't see the death of any data platforms at the moment. In fact I see the number of options exponentially increasing.
We're all in this together, to form Order out of Chaos, in the world of Data. Data Professional have good job security at the moment, justly so, as we are witnessing the Data Revolution ~!
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