The next big thing of the 21st century when it comes to the technological domain of the fourth Industrial Revolution is undoubtedly big data engineering. Big data is showing its prowess in various types of organizations, big and small. The organizations are also reacting to the advances in big data and investing in big data technologies significantly. This is leading to new techniques and methodologies for collecting, processing, and analyzing voluminous amounts of data. Another outcome of the advances in big data technologies is its juxtaposition with prospective domains of cloud computing, data lake, and other AI technologies.
The rise of edge computing
In the present times, we are surrounded by data diversity that is expanding by leaps and bounds. This data diversity is forcing advanced investments and research in new technologies like edge computing. The benefit of Edge computing is that it allows the processing of data very close to the source where it is generated. For instance, edge computing decreases the latency in the processing of data and even allows re-examination whenever needed.
The data generated from different kinds of IoT devices and voice assistants need to be processed immediately for effective synchronization of different kinds of resources at the edge. It needs to be noted now that the traditional data warehouses cannot cater to the increasing data from smart devices which was an effective option earlier. As big data engineering continues to trigger powerful breakthroughs in data processing, we are looking to invent technologies that can significantly improve our computing abilities.
In addition to this, edge computing enables us to transfer the processing load to the devices and not to the servers at first instance. The acceleration of data analysis and quicker response not only decreases the processing cost but also increases the processing power.
The juxtaposition with data lakes
In the last few years, companies used to handle their storage resources on their own. The management, security, and operation of these resources were possible as the volume of data generated was relatively small. As the companies started to expand, their data needs also started to increase. They gradually moved to cloud computing and availed the services of mega cloud service providers like Amazon Web service, Google, Microsoft, and the like. This allowed companies to deal with a voluminous amount of data and pay for only those resources which they utilize. Not only did this lead to increased savings but this also led to an increase in processing capabilities. The processing capabilities were further boosted when big data resources found a great juxtaposition with the data lake. Data lakes allowed the processing of both structured and unstructured data sets in a holistic way. Another advantage of data lakes was that they allowed the sharing of various services from a pool of data resources which increased data insights in the long run.
DataOps allows the repetitive processing of data by considering the entire data life cycle. This technology has numerous advantages like organizing data storage, seamless data communication, and enhancing data processing capabilities. DataOps also allows the management of unstructured data in its native format. Since it deals with the entire life cycle of data, it allows us to bridge the various lacunae in data governance, data security, and data privacy.
The traditional approach of data processing and analytics has been challenged by the volume of structured and unstructured data. It has forced us to develop technologies that can handle this upcoming data wave. This is where the role of artificial intelligence-powered technologies steps in. One of the greatest technologies powered by artificial intelligence is called distributed computing. With the help of open-source platforms, organizations can process voluminous amounts of data with a lot of ease using distributed computing. This has allowed businesses to not only optimize their operations but also supplement their processing capabilities. As such, business intelligence and analytics are witnessing tremendous progress due to advancements in AI technologies. The processing triangle formed by big data, AI, and machine learning is powering the next generation of data analytics and text analytics. This juxtaposition is also leading to great natural language processing capabilities and a high level of semantics for chatbots. A high degree of automation is powering advances in customer personalization and leading to the development of powerful recommendation systems.
There needs to be a significant increase in investment when it comes to big data, AI, and cloud technologies so that we can witness the next generation of smart products that are able to improvise on their own.
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