Introduction to Big Data

This 4-day hands-on training will teach you how to process, analyze, and manage large-scale datasets using modern Big Data tools and frameworks (Hadoop, Spark, Hive, Kafka). You will work with distributed systems, streaming data, and real-world datasets to build scalable data pipelines and analytics solutions.

🧠 What You’ll Learn

• Understand Big Data concepts, architecture, and ecosystems

• Install and configure Hadoop and Spark environments

• Perform distributed data storage and querying with HDFS and Hive

• Process large-scale datasets using Apache Spark (RDDs, DataFrames, SQL)

• Stream and process real-time data with Kafka and Spark Streaming

• Apply data cleaning, aggregation, and transformation techniques at scale

• Build scalable ETL pipelines and batch/streaming workflows

• Explore machine learning with Spark MLlib for clustering and prediction

👥 Who Should Take This Training?

• Data engineers and developers working with large or complex datasets

• Analysts and data scientists aiming to scale beyond traditional tools (Excel, Pandas)

• IT professionals and system architects building data platforms

• Anyone interested in cloud-scale data management and analytics

🎯 What You’ll Achieve

• Design and implement distributed data processing workflows

• Use Spark to efficiently query, transform, and analyze massive datasets

• Build real-time data ingestion and processing pipelines with Kafka

• Integrate batch and streaming data into unified analytics solutions

• Apply scalable machine learning techniques for predictive insights

• Gain the ability to design Big Data solutions in enterprise environments

✅ Prerequisites

• Basic Python or SQL knowledge recommended

• Familiarity with databases and data analysis helpful but not required

• A university-level technical background is sufficient to follow the course