Big Data Analytics with Hive, Pig, and MapReduce

Processing massive datasets requires specialized tools that can scale. Hive, Pig, and MapReduce form the backbone of classic big data architecture, enabling structured querying and data processing at scale. This text-based course guides you from big data novice to confidently structuring databases, writing data transformation scripts, and understanding the execution model of distributed systems. You will learn to optimize query performance and handle modern file formats like Parquet and ORC. What you'll learn: - Understand the core architecture of the Hadoop ecosystem, HDFS, and MapReduce execution flows. - Design and manage Hive databases, external tables, and partitioned datasets for optimized performance. - Write Pig Latin scripts to transform, filter, and analyze unstructured and semi-structured data. - Query complex data types and integrate modern columnar storage formats like Parquet and ORC. - Apply best practices for data serialization, schema design, and performance tuning in big data pipelines. You will start with essential big data concepts and architecture definitions before moving into hands-on query writing, script development, and database design. This course is designed for beginners interested in data engineering and big data analytics, with no prior Hadoop experience required. Start reading today to build your foundation in big data processing.