The world of data platforms is forging forward with increasing velocity. To stay relevant in today’s Big Data conversation, technologies must implement features and enhancements at a swifter cadence than legacy technology. The only way this is possible is by orchestrating the worldwide execution of an open ecosystem of participants. Consider Apache Hadoop; this level of advancement would not be possible without a broad network of developers and engineers working together to rapidly innovate to solve new problems. In addition to just fixing the issues users have with Hadoop, the community is changing the perception of how users can leverage it. Once a go-to tool for large batch processing jobs, Hadoop is changing to address the needs of multiple workloads simultaneously such as streaming and interactive workloads all done at the same level of scale of the original batch jobs.
Is more better? Not always, but when it comes to more industry leaders contributing to the CloudU Big Data Massive Open Online Course (MOOC); more is definitely better. As CloudU continues to extend its reach beyond its online presence, the program will sponsor the first ever Open BigCloud and Open Compute Project (OCP) Workshop at The University of Texas in San Antonio (UTSA). The event will take place Wednesday, May 7 and Thursday, May 8 on the UTSA campus.
It is no accident that we have recently seen a surge in the amount of interest in big data. Businesses are faced with unprecedented opportunities to understand their customers, achieve efficiencies and predict future trends thanks to the convergence of a number of technologies.
“Big Data” is one of the biggest buzzwords in the technology industry today – you can’t read the tech press or look at social media without seeing it. But what do we mean by Big Data, and what’s wrong with our old friend “little data” that has served us well for 30 odd years.
Data is growing and evolving in ways that legacy architects could not have imagined. We are seeing a shift from systems of record that are highly autonomous and rigorously constructed to make sure every piece of data is processed in-tact to data platforms that throw caution to the wind in exchange for performance and scalability. As data evolves and becomes more complex it begs the question, “What type of relationship are you seeking with your database?”
Analytics have become part of our everyday experience. If you have a Fitbit you can see analytics on your weight loss and step goals over weeks at a time. If you are a stock owner you likely get data analysis on your portfolio and their returns over a certain period. In business, if you happen to use Mailchimp to send out newsletters, you often see analytics on opens, click-throughs and more.