Five trends in the processing and development of big data in the future

In recent years, big data has changed from the popular words and concepts unique to large companies to the driving force behind the development of our digital life. The following are five trends in the processing and development of big data in the future.

1. Data science is becoming more and more popular

With the popularity of online education platforms related to data analysis such as Coursera, Udacity and Edx, more and more people can learn all the knowledge, from basic statistical knowledge to natural language processing, without spending a penny. And machine learning. In addition to this, Oxdata simplifies and integrates the analysis products launched after the R language. Quid's tools for machine learning and artificial intelligence concepts are also designed with a fool-like interface and image-specific user presentation methods. More companies like Kaggle have launched a crowdsourcing platform for predictive models. So one of the trends in big data processing is that data analysis, like Datahero, Infogram and Statwing, makes data analysis easy to use.

2.Hadoop's dependence on MapReduce is getting smaller and smaller

The era of Hadoop platform only for MapReduce service officially ended with version 2.0 of Hadoop. Products and services supported by the new version will use the same SQL query engine as Cloudpal's Impala, or other methods to replace MapReduce. The HBase NoSQL database is a good example of Hadoop leaving MapReduce constraints. Large network companies, like Facebook and eBay, have used HBase to handle transactional applications.

3. More and more big data is being used in our applications.

The first is that big data applications are becoming less demanding for our developers. Sometimes developing big data applications is like adding a few lines to your application's code, or writing a script. Secondly, the application scope of big data has also been expanded. User habit analysis, network security, artificial intelligence, after-sales service, etc. can be realized by making big data processing into products or applications. Today's big data technology has been brought into many web and mobile applications, from shopping recommendations to finding people who are connected with them.

4. Machine learning is everywhere

It's easy to see that machine learning is becoming more and more popular, from the small apps around us Prismatic, Summly, Trifacta, CloudFlare, Twitter, Google, Facebook, Bidgely, Healthrageous, Predilytics, BloomReach, DataPop, Gravity... A technology company without machine learning technology can survive. Heck, and even Microsoft, have made a big bet on machine learning and it will be an important source of income.

5. The phone will become the data source of artificial intelligence

Apps on our phones and phones are currently the largest source of private information. Through machine learning, speech recognition and other technologies, these applications can know where we are going, who our friends are, what reminders are on our calendars, and what we browse online. Through a new generation of personal assistant applications (Siri, Saga and Google Now, etc.), our mobile phones are more able to understand our speech, know where we often go, what we usually eat, the time we spend at home, work and outing, and so on.