The trend of big data should be of importance to entrepreneurs. This as companies once had the problem with not enough data, but things have changed, as they now have to deal with too much data. To solve this problem many are not streaming. Therefore, the focus is more how data is utilized, scrutinized and presented.
In that line Greg Schulz, an analyst with StorageIO Group declared that, “storage solutions are expanding their big data and analytics feature sets into fields such as video, image, click steam analytics, telemetry, transcoding and other forms of data transition.”
Nonetheless, as there is a turn away from the industry standard of Hadoop. Hadoop, which is the framework, as well as group of tools used for the dealing of extremely large data. But the trend is moving towards cloud. Examples are that of IBM’s Bluemix could platform, Redshift, and Google’s BigQuery data analytics service. Besides there are better and faster databases than Hadoop. Examples are that of MemSQL and Exasol. These databases are making Hadoop outdated.
For example, Redshift is lower-priced to operate, and offers large reporting abilities for structured data. In addition, Redshift compared to Hadoop is scalable and easier to control, as virtual machines are less expensive to expand equated to actual machines.
Then again, the industry standard Hadoop might still be around for many years, the problem is that it is simply not at the level what companies are demanding. Luckily, unlike in the past where technologies took years to develop, now they are produced within months.
This is the same with Hadoop. In addition, even though the technology has been altered with the addition of analytics such as SAP HANA and SAS, it was not enough. Still, Hadoop is slowing developing into a general-purpose data operating system. Furthermore, it would allow that a variety of data manipulations and analytics operation. As a result, businesses can use Hadoop as an enterprise data hub. This is as there will still be a need for data storage. And even newer analytics based initiatives should have a good data storage to support it. As such as companies are expanding towards data analytics and management, there should still be a focus and investment on information technology infrastructure.
That said, Hadoop has some other limitations. Firstly, that Hadoop takes around twenty times longer to reply with answers than other technologies, such as Spark, which is a large-scale data processing engine. Secondly, the Hadoop technology is not enterprise ready for building a data lake within the platform. This as the platform does offer an organic model for developing a large-scale database, but the workers need to be extremely skilled.
Nevertheless, there is more emphasis on predicative analytics. This as many companies would want to use their big data to both indicate performance but also produce predictive analytics. This predicative analytics allows companies to make better-informed choices for the future, and do not want to scrutinize data from the past.
The bottom-line. As there companies are dealing with big data, technologies are being upgraded and replaced. More so, there are an integration between analytics and big data. But as analytics are becoming of advanced, it still needs data support for storing, archiving but also to mine big data. This is since many companies are now understanding that all their data are in fact of value, and they can capitalize on this data. With that, more entrepreneurial companies are developing self-service platform. These are platforms where the customer has access to data. Then the company can use the insights gained to improve the company’s functionality. This application could have large benefits for companies to protect against competitors. This means that entrepreneurs should keep an eye on the developments of big data.
