Professional Data Engineering and Big data Program
Types of data in big data
Professional Data Engineering and Big data Program
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Introduction to Big Data and Data Engineering – part1
Big Data needs Data Engineering because raw data is too large, fast, and messy to process or use directly.
Data Engineering solves this by building scalable systems (scale-out) to collect, store, and process data efficiently.
Introduction to Big Data and Data Engineering – part2
Data is growing continuously because of social media, IoT, and digital systems, so Big Data is large, fast, and diverse data that has 5Vs (Volume, Velocity, Variety, Veracity, Value).
We handle it using Batch Processing for large historical data and Real-Time Analytics for instant insights and fast decision-making.
Introduction to Big Data and Data Engineering – part3
Big Data faces challenges like huge volume, variety, and processing complexity, so systems like OLTP, Data Warehouses, Data Lakes, and Lakehouses are used to manage it.
ETL transforms data before loading, while ELT loads data first then transforms it for modern big data processing.