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Must Know Techniques Linked to Big Data Analysis

By 2025, the global business for SaaS and Cloud-based platforms will almost triple to $120 billion. A major drive pushing this trend is attributed to the rampaging growth of Big Data and Analytics solutions. Almost 90% of the current Cloud-based companies are already heavily invested in Big Data analysis.

Popularity-wise, here are the basic Big Data analysis techniques for those who see the opportunities in the industry.

Data Mining and Fusion

The massive volume and variety of data can incredibly handicap the existing IT infrastructure. To prevent data deluge from putting IT systems off their course, Big Data analysis teams work 24/7 in sync with data collection points. In conflict scenarios, Big data analysis helps to put a bigger, accurate picture of how various data types provide different types of information. Data mining and fusion techniques in Big Data course Malaysia could cover a variety of Probabilistic Modeling methods, including Bayesian and human-machine intelligence built using data exploration.


AIOps is often considered as hotwiring between DevOps and Big data analysis. In the last 4-5 years, AIOps has smartly grown as a successor to every IT risk and governance management platform. When implementing Cloud and SaaS, AIOps related to big data analysis can help to create the most robust experience to users.

No matter where and how you begin in the industry, a thorough knowledge of AIOps techniques would equip you to handle complex data infrastructure.

Anomaly Detection

In Big Data analysis, Anomalies need to be corrected in real-time to reduce redundancy and falsified intelligence from disrupting the workflow. In generic techniques, anomaly detection (AD) involves the detection of suspicious objects, events, or a bunch of abstract files that show deviation from standardized data benchmarks. Also called Outlier Detection, AD techniques use machine learning algorithms.

In Big data courses, these AD techniques are broadly classified as Point Anomaly, Collective Anomaly, and Contextual Anomaly. Sharper the machine learning capabilities, clearer would be AD distinctions.

These can be further classified as supervised and unsupervised AD techniques, based on the level of penetration of advanced analysis such as Vectors, Neural Networks, Cognitive Learning, and Classifiers.

Hyper-Converged Architecture

Preparing for Cloud? Well, it’s nothing short of a miracle that most data scientists would hire an IT professional to handle all kinds of Big Data analysis that relate to Converged Architecture. In the current industry parlance, Big Data built on converged architecture allow companies to profit from “digital” turnarounds and not merely turnovers.

Big data companies have more data than they can possibly fathom, and that needs incredible reliance on building and accessing proper architecture that seamlessly allows all the data to cobble together under one roof. But wait, today, we have already migrated to a realm where IT teams are more interested in Hyper-converged architecture and not merely the simpler one, with fine combination of Big Data analysis for virtual computing and AIOps management.

In the world that is increasingly getting smarter with quintillion sized data, it’s an opportune moment to get certified from Big Data Course Malaysia.

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