In machine learning, categorical data refers to variables that represent categories rather than numeric values—such as gender, education level, or product rating. While many algorithms can process numerical data effectively, they cannot inherently understand categorical text values. Feeding raw categorical data into models like decision trees, logistic regression, or neural networks often leads to errors ...
The rise of NoSQL databases marked a pivotal shift in how modern applications store, manage, and retrieve data. Unlike traditional relational databases (RDBMS) that rely heavily on structured schemas and SQL queries, NoSQL systems offer greater flexibility, scalability, and speed. They emerged to meet the evolving needs of applications handling big data, real-time analytics, IoT, ...
As data science and big data applications grew in complexity and scale, efficient resource management became a critical need within the Hadoop ecosystem. Traditional MapReduce had limitations in handling diverse workloads and dynamic resource allocation, prompting the development of a more flexible solution—YARN (Yet Another Resource Negotiator). Introduced in Hadoop 2.0, YARN acts as the ...
In today’s rapidly evolving medical landscape, data has become a cornerstone of effective healthcare delivery. From patient records to clinical trials, hospitals and healthcare providers are generating vast volumes of data every day. Harnessing this data through healthcare analytics enables professionals to make informed decisions that improve patient outcomes, optimize operations, and control costs. Whether ...
Apache Hive is an open-source data warehouse infrastructure built on top of the Hadoop ecosystem. Developed initially by Facebook to manage and analyze massive volumes of data, Hive provides a SQL-like interface—known as HiveQL—for querying and managing large datasets stored in the Hadoop Distributed File System (HDFS). Instead of requiring users to write complex MapReduce ...
As organizations increasingly rely on data-driven strategies, the demand for skilled big data engineers in India has surged. From startups to global enterprises, companies are investing in professionals who can design, build, and maintain scalable data infrastructure. With the ever-expanding volume of structured and unstructured data, the role of a big data engineer has become ...
In today’s fast-paced digital world, organizations generate massive streams of data from sources like social media, IoT devices, web applications, and financial transactions. The need to analyze and act on this data in real time has given rise to powerful stream processing frameworks. One of the most prominent among them is Apache Spark Streaming. As ...
Business Intelligence (BI) tools are software applications that collect, process, and visualize data to support smarter, data-driven decision-making. These tools help organizations uncover trends, monitor performance, and forecast outcomes with precision. In 2025, BI tools remain essential for translating complex data into actionable business insights across industries and team sizes. Why Use Business Intelligence Tools? ...
The exponential growth of data in recent years has ushered in the era of big data, where organizations across industries generate and collect massive volumes of information daily. Traditional data processing methods struggle to manage such scale, speed, and complexity. Enter MapReduce—a powerful programming model that revolutionized how large datasets are processed across distributed systems. ...
Hadoop is an open-source framework developed by the Apache Software Foundation to store and process vast amounts of data efficiently across distributed computing clusters. Originally inspired by Google’s MapReduce and GFS papers, Hadoop has become foundational in big data analytics, enabling scalable, fault-tolerant data management for enterprise-grade applications. Hadoop Ecosystem in Big Data In the ...
Team Applied AI
Abhimanyu Saxena
