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4 Key Big Data Trends to Watch for in The Year 2021

Big Data Trends 2021

As we know, 2020 was not a good year for the majority of the business enterprises across the globe, even though some benefitted out of it. COVID pandemic threw a colossal spanner in the activities of most businesses and left many to close down.

The IT departments of various organizations were forced to burn out of it. However, technology still stood up pretty decently throughout this period as most people were working from home, conferences were held online, and the role of cloud computing gained significantly as a necessity of the age.

Along with this, we have seen Big Data Trends also, which had been elusive for the last several years, started to materialize in 2020 amid the pandemic slowly.

What the outbreak of this pandemic proved at the first point is that human beings can be as productive at home too as they work from offices.

Big Data Trends had risen to the situation as a promise of actionable intelligence in real-time from anywhere, anytime.

Big Data Trends 2021

The actionably insights through data and the technologies like Cloud Automation, Hybrid Cloud, and immersive User Experience, along with allied trends, were radically set to alter the database landscape in 2021 and further.

#1. Actionable data

We are hearing this term for a decade now as being used by data analysts, data integration. Business intelligence specialists are using in terms of customer experience and analytical insights.

Now, in 2021, the fundamental evaluation of Big Data’s potential will be based on the capability of handling such ‘actionable data.’

Data comes with a lot of value it and utilizing it closer to its generation is important to leverage the best benefits out of it.

As data gets older, the value may also diminish, so live data analysis is one of Big Data technologies’ major trends now.

The developments in collecting data, governance, integration, visualization, and data analytics models have brought in more value to data to derive actionable insights from it.

Real-time data processing for cloud and IoT applications and edge computing, in-memory computing, and digital channel management, which instantaneously connects with the customers, have all come together to make data not just actionable but also more insightful in terms of forecasting and decision making.

#2. Hybrid cloud

For nearly a decade now, the cloud has been identified as a fundamental shift in data management technology; however, many enterprises are still not fully comfortable putting their data onto the cloud for various reasons.

They are still bothered about privacy, security, latency, or data processing, etc., among many other reasons. This is the reason why the Hybrid cloud came into the picture.

In theory, a hybrid cloud is an approach that combines both public and private cloud services, in which proprietary software will enable secured communication between these services to ensure optimum privacy and security.

Adopting a hybrid cloud will allow the businesses to perform with greater flexibility by moving the workloads between various cloud solutions based on their needs and budget.

A hybrid cloud will connect at least one private cloud to a public cloud. In this combination, the private cloud will act as the internal network, which is restricted to be accessed by a selected group of users.

In contrast the public cloud facilitated by third-party vendors is made available on the public internet. The major public cloud services like Microsoft, Amazon, Google and Alibaba provide this hybrid offering.

For choosing an appropriate cloud service for your database management, you may consult with consulting services.

In a typical enterprise database management environment, a hybrid cloud will connect one or more of the public clouds to one or more private clouds by creating a single cloud infrastructure that handles the company’s entire computing workload.

A hybrid cloud will ideally help improve developer productivity and contribute to greater efficiency in the infrastructure and strengthen security and regulatory compliance.

Along with facilitating innovation, the hybrid cloud also helps to quicken the time-to-market for products and services and helps create a highly holistic network.

#3. Automation overcloud

Capturing and storing Big Data may be the easiest part. Further, to tag, govern, and utilize data for various purposes involves many behind-the-scenes activities.

Cloud automation is the process of enabling database administrators and developers to build and modify resources on the cloud automatically.

Cloud computing offers various services on demand, but the users need to spin up such resources to fit their varying purposes, test and identify if they are useful, or not take them down. All these may need a few manual efforts too.

Cloud automation ease out the burden of people involved in cloud systems, both private and public clouds.

Automation works based on the set protocols and procedures to facilitate machine learning, deep learning, and AI operations on huge volumes of data and logs. Automation makes it easier to look for data trends and analyze the results.

This will also help resolve any issues in a much controlled and predictive manner and most of the time, even before they occur. Working in conjunction with cloud automation, resource capacity planning will help to eradicate any unneeded expenses.

#4. Edge computing

Cloud-to-the-edge can deliver more storage, computation power, and bandwidth, etc. Edge computing’s ideal use cases are not limited to the collection of data but also largely to data analysis, regulatory compliances, security, and increased privacy.

Data analytics on edge can be more responsive to real-time conditions. When information is processed at cloud-to-the-edge, one can save a lot of time and effort. Analytics modeling can produce quicker results, which are also more authentic and useful.

Edge also facilitates sending huge volumes of data over networks to the private or public cloud, building models, and pushing the results down to edge devices to be utilized in involving processes.

The results of such models, which may have taken a long journey to evolve, maybe useless as the base data used for the same may have gone stable by the time the results come out.

So, edge makes it much better by enabling this modeling as rapidly as possible.

Along with these, blockchain and distributed ledger, etc., have also been hyped along with AI. All these technologies come together and marching towards a wider-reaching acceptance of Big Data.

About Author:

Pete Campbell is a social media manager who has worked as a database administrator in the IT industry and has immense knowledge about email marketing and Instagram promotion. He loves to travel, write and play baseball

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