Data Science and relevant deep learning techniques have the potential to transform business intelligence operations in terms of the savings they generate out of operational efficiencies, human resource automation, and next-gen predictive intelligence. With the explosion in big data science technologies in recent years, the demand for data science and analytics jobs has grown exponentially. Almost every data driven company is now focusing on building analytical approaches to improve their overall organizational technology stacks.
In this article, we will explore the role of various IT-specific Data Science technologies and applications that certified professionals from the best data science institute in Bangalore work on.
Data Lake Management
Data Lake Management is one of the top ranked specializations in the Big data industry. Every year, data companies lose billions of dollars due to the lack of filters and upgrades to data pools. Data lake management brings the essence of working within a solid ecosystem of data administration, governance, and security of structure and unstructured data. It is a complex architecture of Cloud and IT that makes data lake management so hard to understand in the initial phases, especially if you are working outside of any collaborative self-service platform or virtualized systems.
Top data lake management concepts promoted by companies like IBM, Azure, and Informatica can help any training professional to understand the fundamental characteristics of data lake management and its role in Big Data Analytics.
AIops Virtualization
The scientific use of AI capabilities within the context of IT frameworks is called as AIops. In the last 18 months, a major transformation in Cloud and IT services have been reported where CIOs and CISOs are leveraging AI and Machine learning capabilities to manage complex IT architecture. In the modern context of Cloud deployment and migration, AIops spells the digital evolution of IT Cloud systems for Platform as a Services (PaaS) and Infrastructure as a Service provider (IaaS).
AIOps has fundamentally three layers of applications, built within the IT structures -
1 - AI ML Algorithms
2 - Big Data Analytics
3 - IoT / APIs
When these merge with virtual machines (VMs), we derive the result of working in a Virtualized AIops scenario.
Three reasons why AIops virtualization would become the mainstay of any IT Cloud business are listed as follows;
1 - Automated Platform Management
Infrastructures are getting complex and harder to manage in real time. AIops make this process automated and ready to launch in matters of minutes if not seconds. In most cases, CIOs report working in Virtual AIops systems more convenient and manageable from the user experience point of view.
2 - Super Compute
We have endlessly discussed the future of Cloud computing lies in the two new branches - Edge, and Quantum. Both sciences are explicable in the way 5G, IoT, and Virtual machines are becoming the new norms of doing business in an extremely competitive IT landscape. As budgets shift from core IT management to user experience and security, leveraging the skills of big data science professionals only add to value in the industry.
3 - CI CD Workflows
A very popular posture in IT management is Continuous Integration Continuous Deployment (CI CD) that super accelerates the whole process of ETL workflows in real time. Thanks to newly developed Predictive Intelligence tools and solutions, IT operations and IT Service management teams can work collaboratively from remote workplaces without bothering too much about the perennial issues of data silos and access management policies. Simply put, AIOps fast tracks CI CD workflow with the new modalities of correlation, collaboration, and contextualization in DevOps and IT Cloud Management.
Big Data For Security
93% of the data science companies are under direct attack from external threat agencies. The best data science institute in Bangalore extensively train professionals in cybersecurity applications and approaches. These approaches work collectively with tools, solutions, tactics, and predictive intelligence to guard departmental and organizational assets against internal as well as external cyber threats and ransomware.
Big Data Security professionals are either concerned with building new technology stacks for authentication and authorization, or they are working with DevOps teams to build fully secured data warehouse and enterprise data security strategies. Data security management is a costly, but a very future proof concept, and hence data-driven enterprises have no qualms about aggressively investing in top Data Science and cybersecurity talent and resources.
Due to the massive influx of AI capabilities, enterprises hire Big data Project managers who can bring a 100% sense of assurance.
