To fulfill this, some ETL vendors are extending their product lines horizontally by data tools and features to capture real time data to provide a complete data management solution. And the big problem that was identified was: with traditional tools, such as classic ETL it was impossible to organize and structure the big data in a way that they can be quickly and easily accessed for analyses.ĭata integration solutions were designed to meet the biggest part of an organization’s BI requirements. Only well analyzed and interpreted data could give powerful business and market insights. However, businesses also realized that the gigantic amount of raw and unprocessed information itself wasn’t of big value until they could be structured, analyzed, and interpreted well. Just think about all the data that every business already owns! And so, the buzz around big data started. In the past few decades, these raw and unprocessed information gained more and more traction because companies realized that these data could change the way we live, work and think. But for our purposes, we will stick with the definition of data as raw or unprocessed information. When you look for a definition of data on the internet, you will get tons of different ones. What is data? Big Data And Problems Dealing With Them But before we dive into this topic, let us first start with the basics. Especially, processing them the right way has become a crucial solution for many businesses around the world. Over the years, and through technological evolution, data has become an essential topic and a key factor to business success. As you can imagine, there is a lot of data generated every minute. According to Domo, our current data output is roughly 2.5 quintillion bytes per day. 90% of today’s data has been created in the last two years only.
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