Big Data - What is a Parquet File? Columnar Storage & Predicate Pushdown Masterclass

Handling large datasets with row-based formats like CSV is slowing you down. In this video, we dive deep into Apache Parquet, the industry standard for efficient big data storage. We explain why shifting from row-based to columnar storage can reduce your file size by up to 90% (1TB down to 100GB!) and drastically speed up your analytical queries. In this video, you will learn: (1) The Architecture: How Parquet organizes data into columns instead of rows. (2) Massive Compression: How schemes like Snappy and Gzip work with similar data types. (3) Optimization: What is "Predicate Pushdown" and how it skips unnecessary data. (4) The Verdict: When to use Row-based (Transactional/CRUD) vs. Column-based (Analytical/OLAP). Key Takeaway: If you are doing aggregations (SUM, AVG) or scanning specific columns, Parquet is essential. If you are fetching full rows or doing frequent updates, stick to row-based formats. #BigData #DataEngineering #ApacheParquet #DataScience #Parquet