Big Data Trends of 2019 To Watch Out For

Technology is quick paced and ever evolving. It’s all about the latest technology and how big an impact it can make. If you’re smart and stay ahead of the curve, you’re likely to do better in business. You also might end up making a lot of money. It’s imperative to keep up and stay one step ahead. Here are the top data trends of 2019 that you need to look out for and incorporate into your business.

Thanks to artificial intelligence, cloud computing and the internet of things, even the complexity of big data can be handled by those who are willing to use it to their organization’s advantage. Big data analytics has gone beyond the hot IT trend tag and has now established itself as part of doing business for companies.

Improved Data Management


The main purpose of any kind of data analysis is to find some kind of pattern within the data. Through this pattern, trends can be predicted and forecasted. Companies can evaluate their growth rates and profits thanks to data analytics. Consumer behaviour is no longer a theoretical concept. It is a lot easier to track consumer activities and log them and then analyse and use this data.
The first of data analytics is to generate data. Once that has been taken care of, you now need to store the data for future use. This is where data management gets the spotlight. It’s important for organizations to invest in a good data storage infrastructure. With cloud computing, it’s become a lot more cost efficient. Resource utilization has also reduced. But there’s still a long way to go.

Streaming Analytics

Streaming Analytics operates on the principle that the faster you analyse and act on your data, the better it is for the organization. The problem faced is that this isn’t the easiest thing to do. It’s also not very cost efficient. As technology advances, streaming analytics has become more achievable.
There are efforts to combine SQL capabilities with frameworks such as Kafka, Spark and Flink.

“We will see more and more businesses treat computation in terms of data flows rather than data that is just processed and landed in a database. These data flows capture key business events and mirror business structure. A unified data fabric will be the foundation for building these large-scale flow-based systems.”

Ted Dunning, the Chief Application Architect at MapR

AI Platforms

AI platforms are frameworks that use artificial intelligence to actually improve and learn. This makes them valuable when it comes to performing any computation as opposed to a regular framework. Using AI platforms to process big data is a significant improvement in gathering business intelligence and improving efficiency.
Artificial Intelligence platforms are arranged into five layers of logic:

  1. The Data & Integration Layer gives access to the data. (Critical, as developers do not hand-code the rules. Instead, the rules are being “learned” by the AI.)

  2. The Experimentation Layer lets Data Scientists develop, test, and prove their hypothesis.
  3. The Operations & Deployment Layer supports model governance and deployment. This layer offers tools to manage the deployment of various “containerized” models and components.
  4. The Intelligence Layer organizes and delivers intelligent services and supports AI.
  5. The Experience Layer is designed to interact with users through the use of technologies such as augmented reality, conversational UI, and gesture control.
    For example, when it comes to running social media campaigns, analytics focusses on what kind of an audience you should target. Artificial intelligence can then tell you what kind of a campaign you should run.

Hybrid Clouds

Cloud computing has been steadily becoming more and more popular. With cloud computing, there are a variety of clouds available for use. Of these, a category is that of hybrid clouds. Hybrid clouds incorporate features of public and private clouds. They combine an organization’s private cloud with the rental of a public cloud. The biggest upside to using a hybrid cloud is that you get the advantages of public and private clouds. While an organization may want to keep some data secure in its own data storage, the tools and benefits of a hybrid system make it worth the expense.


The data and resources on the hybrid cloud can be transferred back and forth between on-premises (private) clouds and IaaS (public) clouds. Thus, we have increased flexibility, better and more deployment options, and tools. A public cloud, for example, can be used for the high-volume and low-security projects like email advertisements. On the other hand, the private cloud can be used for more sensitive projects, such as financial reports.

The term “Cloud Bursting” is a feature of hybrid cloud systems and describes an application that is running within the on-premises Cloud, until there is a spike in the demand, and then the application will “burst” through, into the public cloud, and tap into additional resources.

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