Five Key Trends in Big Data Analytics

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New Age technologies almost exclusively dominate the world today. Among these technologies, big data has gained popularity. This concept is not new. What is new is the extent to which big data analytics is being implemented by businesses around the world and the growing number of professionals now specializing in the field. Thanks to the explosion of computing power, accelerated digitalization and the widespread migration of data to the cloud, we are now discovering the true potential of big data analysis. Although they offer a wealth of opportunities for businesses, big data trends, like all other technologies, are constantly evolving. Here are five trends to watch in 2023.

1. The evolution of streaming analytics

Flow analysis is a new trend in data analysis that has become increasingly popular in recent years. It’s based on the idea that real-time data can be analyzed as it comes in, rather than waiting for all the data to be collected.

Two main factors have contributed to the rise of streaming analytics. First, more and more companies are moving their operations online, where a significant portion of their business is done. This means businesses now have more real-time access to data than ever before. Second, the amount of data produced has grown exponentially over the past decade. The growing list of technical tools for working with real-time analytics, including Spark, Kafka, Kinesis, and HubSpot Operation Hub, also plays a role.

2. The Rise of AI-Powered Big Data Analytics

The future is driven by artificial intelligence (AI), and the next generation of big data analytics is also driven by AI, which brings several factors into play. For example, technology has developed and storage costs have come down significantly. It enables the storage and processing of various use cases that were unthinkable ten years ago. Next is the speed at which the AI ​​tools process the data. Parallel processing, graphics processing units (GPUs), on-demand computing power tools, and faster, more accessible movement of data between stages have all opened the door to use cases for AI.

Additionally, AI has increased the accuracy with which it can interpret data. AI has expanded algorithm and library options, improved efficiency, and allowed algorithm pipelines to be combined to produce better predictions and better results.

The degree of automation has also increased. Once the data models and required hyperparameters are defined, automating the ingest of data into the data pipeline, as well as triggering the machine learning (ML) algorithm from start to table. final analytical edge or to notifications, is generalized throughout the process without error and quickly.

3. The expansion of edge computing

Simply put, edge computing is the processing of data at the edge of the network or device rather than in a central location. The growing popularity of Internet of Things (IoT) devices is driving the growth of edge computing. With so many connected devices, it’s difficult to manage all the data from a central location. As a result, many small businesses are building third-party networks capable of handling edge computing. With the growing need for faster, real-time analytics, edge computing and analytics are becoming the need of the hour. Most cloud providers have IoT edge analysis tools, and some IoT platforms like PTC Thingworx are popular for such use cases.

4. Heavy reliance on cloud storage

Despite some drawbacks, cloud storage is a convenient option for storing large amounts of data. When dealing with large or particularly sensitive data, this is not always the ideal option. Also, it can be difficult to keep track of large amounts of cloud storage accounts. However, one of the most important developments in Big Data remains cloud storage. People today care more about who has access to their information than where it is stored. “Noisy neighbor” is a common concern among customers who fear that their data will be hacked by one of their competitors or a bad actor.

Yet the industry still has work to do before these concerns are allayed. Regulations present challenges that must be considered when organizations plan how and where their data will be stored. Shared software-as-a-service (SaaS) companies are considering becoming cloud-agnostic, which means that coding and data can be stored in customers’ clouds without privacy concerns.

5. DataOps for data

DataOps has brought structure to the growing data management craze, allowing an organization to see the ROI that has flowed into the data paradigm. The SaaS wave has put DataOps in the spotlight.

In recent years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a remarkable part of daily operations. There are now multiple aspects of data management in an organization and the list keeps growing. This includes data volumes, use cases, update frequency, data model changes, security and privacy testing, adding data sources and consumers, and customizable analytics. Data management has become the responsibility of the organization, which affects the collected data in every possible way.


Valued at US$271.83 billion in 2022, the global big data analytics market is expected to reach US$655.53 billion by 2029. Taken together, these trends will fundamentally change the field of big data analytics. big data and their impact will be significant.


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