So we talk about machine learning and analytics, predictive analytics, the platform itself actually becomes a data creation platform, and that also adds to the different varieties of data. So it actually ends up compounding the drive to work towards the variety of the platform itself. The opportunities that it creates for using the data that comes in will undoubtedly be unlocked.
It will keep growing. The varieties are never going to go in other direction. It's all is going to be increasing, and that's a great opportunity. It means different data types, script data and non SQL data will continue to increase a little. Coming back to how you actually get that data coming the other direction with SQL becoming more and more important, but in the underlying data, the variety is only going to increase.
Abhishek: Larry, any thoughts you may want to add in here?
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Larry: So not only is variety increasing, I am also seeing that more and more customers, even the non technical users, are looking to better understand these no SQL data types. In fact, I know MySQL Server in its latest version, HD, has introduced the Jason type. Back in the day I would say years or two years back, if someone started seeing Jason, you would expect him to be more of a developer persona, but that's not the case anymore.
You know a lot of the internet things data call logs, you know service records, all those things are being captured in unstructured formats, and there will be more and more demand, and there will be lot more need going forward for BI platforms to provide live connectivity to the sources in order to satisfy users of all skill sets.
Abhishek: Yeah, that's great I think that's definitely very consistent with how we see the adoption patterns growing and that variety of data being really the key driver to modernizing, having companies take a look at their longer-term data platform strategy. And our next trend is Spark and machine learning light up Big Data.
So oftentimes when we are reading the industry publications, we see the question being asked around, what is the Big Data platform of the future? Is it Hadoop or Spark? The answer is both. In reality they are serving different use cases and different needs, but Spark is very quickly becoming a data processing platform of choice for a number different use cases such as for ETL, for machine learning, and certainly for level interactive querying.
A recent survey of data architects, IT managers, and BI analysts found that almost 70% of respondents preferred Spark over MapReduce, which is batch oriented and doesn't lend itself interactive applications or real-time stream processing quite as well as Spark does. So it's along the way becoming just a core component of the big data framework. Companies are adopting it as part of their architecture, and that's been a rocketship trajectory. in terms of adoption. Larry, Holly, any anything you guys would want to add on to this trend?
Larry: Yeah, I just want to again reemphasize that we firmly believe that they will continue to coexist. Hadoop will be the platform for scaled out data storage to help processing, and Spark will continue to be used for batch oriented and interactive pressing needs, and the two will continue to play an important role in the next generation of scaled out platforms, and we have seen an explosion in the use cases where both are used.
Holly: I am going to repeat what I have said earlier about machine learning and the tools for analytics, how they become data creators. They become data sources. They create data that is valuable for analysis, and it's very important just to realize that because that speaks to having the pipeline of making that data accessible to the business as quickly as possible and having an agile data platform.
So it's a huge opportunity that the data gets created by tools like Spark and techniques like machine learning is made available to the businesses for analysis, itself, and the fact that these things are creating data, it's really exciting actually.