Big Data

NoSQL Databases in Big Data (MongoDB and Cassandra) Traditional relational databases struggle with scalability, flexibility, and performance when handling massive volumes of unstructured and semi-structured data. NoSQL databases were designed.

Working with Streaming Data in Big Data In many real-world applications, data is not generated in batches but arrives continuously in real time. Examples include sensor data, financial transactions, social.

Spark SQL and DataFrames While RDDs give low-level control, most real-world Big Data applications work with structured or semi-structured data. Spark SQL and DataFrames provide a high-level, optimized, and user-friendly.

Apache Spark Basics Apache Spark is a powerful, open-source Big Data processing framework designed for fast, in-memory computation. Unlike MapReduce, Spark processes data in memory, making it significantly faster for.

HDFS and MapReduce in Big Data HDFS and MapReduce are the two core pillars of the Hadoop framework. HDFS handles the storage of massive datasets, while MapReduce processes those datasets.

Introduction to Big Data and Hadoop Ecosystem In today’s digital world, massive amounts of data are generated every second from social media, sensors, mobile devices, transactions, and online platforms. Traditional.